Datasets:

allenai

/

SciRIFF

like 33

Follow

Ai2 1,409

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Current state-of-the-art statistical parsers (Collins 1999; Charniak 2000) are all trained on large annotated corpora such as the Penn Treebank (Marcus, Santorini, and Marcinkiewicz 1993). For example, while it is difficult to induce a grammar with raw text alone, the task is tractable when the syntactic analysis for each sentence is provided as a part of the training data (Pereira and Schabes 1992). Many learning tasks for natural language processing require supervised training; that is, the system successfully learns a concept only if it has been given annotated training data. Citation Sentence: Current state-of-the-art statistical parsers ( Collins 1999 ; Charniak 2000 ) are all trained on large annotated corpora such as the Penn Treebank ( Marcus , Santorini , and Marcinkiewicz 1993 ) . Context after the citation: However, supervised training data are difficult to obtain; existing corpora might not contain the relevant type of annotation, and the data might not be in the domain of interest. For example, one might need lexical-semantic analyses in addition to the syntactic analyses in the treebank, or one might be interested in processing languages, domains, or genres for which there are no annotated corpora. Because supervised training demands significant human involvement (e.g., annotating the syntactic structure of each sentence by hand), creating a new corpus is a labor-intensive and time-consuming endeavor. The goal of this work is to minimize a system’s reliance on annotated training data.

Background

acl_arc_intent_classification:train:1268

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: Although the parser only derives projective graphs, the fact that graphs are labeled allows non-projective dependencies to be captured using the pseudoprojective approach of Nivre and Nilsson (2005) . • LEFT-ARC(r): Add an arc labeled r from next to top; pop the stack. • RIGHT-ARC(r): Add an arc labeled r from top to next; push next onto the stack. Citation Sentence: Although the parser only derives projective graphs , the fact that graphs are labeled allows non-projective dependencies to be captured using the pseudoprojective approach of Nivre and Nilsson ( 2005 ) . Context after the citation: Another limitation of the parsing algorithm is that it does not assign dependency labels to roots, i.e., to tokens having HEAD=0. To overcome this problem, we have implemented a variant of the algorithm that starts by pushing an artificial root token with ID=0 onto the stack. Tokens having HEAD=0 can now be attached to the artificial root in a RIGHT-ARC(r) action, which means that they can be assigned any label. Since this variant of the algorithm increases the overall nondeterminism, it has only been used for the data sets that include informative root labels (Arabic, Czech, Portuguese, Slovene).

Background

acl_arc_intent_classification:train:1269

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The Penn Treebank results reported here for the Markov model approach are at least equivalent to those reported for the Maximum Entropy approach in (Ratnaparkhi, 1996). Additionally, we present results of the tagger on the NEGRA corpus (Brants et al., 1999) and the Penn Treebank (Marcus et al., 1993). The aim of this paper is to give a detailed account of the techniques used in TnT. Citation Sentence: The Penn Treebank results reported here for the Markov model approach are at least equivalent to those reported for the Maximum Entropy approach in ( Ratnaparkhi , 1996 ) . Context after the citation: For a comparison to other taggers, the reader is referred to (Zavrel and Daelemans, 1999).

CompareOrContrast

acl_arc_intent_classification:train:127

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Riehemann 1993; Oliva 1994; Frank 1994; Opalka 1995; Sanfilippo 1995). In a number of proposals, lexical generalizations are captured using lexical underspecification (Kathol 1994; Krieger and Nerbonne 1992; Lexical rules have not gone unchallenged as a mechanism for expressing generalizations over lexical information. Citation Sentence: Riehemann 1993 ; Oliva 1994 ; Frank 1994 ; Opalka 1995 ; Sanfilippo 1995 ) . Context after the citation: The lexical entries are only partially specified, and various specializations are encoded via the type hierarchy, definite clause attachments, or a macro hierarchy. These approaches seem to propose a completely different way to capture lexical generalizations. It is therefore interesting that the covariation lexical rule compiler produces a lexicon encoding that, basically, uses an underspecification representation: The resulting definite clause representation after constraint propagation represents the common information in the base lexical entry, and uses a definite clause attachment to encode the different specializations. 8.

CompareOrContrast

acl_arc_intent_classification:train:1270

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The idea resurfaced forcefully at several points in the more recent history of linguistic research (Tesni`ere, 1959; Gruber, 1965; Fillmore, 1968). He was a grammarian who analysed Sanskrit (Misra, 1966). The first chronicled endeavour to connect text elements and organise connections between them goes back to the 51h century B.C. and the work of Paninil. Citation Sentence: The idea resurfaced forcefully at several points in the more recent history of linguistic research ( Tesni`ere , 1959 ; Gruber , 1965 ; Fillmore , 1968 ) . Context after the citation: Now it has the attention of many researchers in natural language processing, as shown by recent research in semantic parsing and semantic 'The sources date his work variously between the 5th and 7th century. Graph-like structures are a natural way of organising one’s impressions of a text seen from the perspective of connections between its simpler constituents of varying granularity, from sections through paragraphs, sentences, clauses, phrases, words to morphemes. In this work we pursue a well-known and often tacitly assumed line of thinking: connections at the syntactic level reflect connections at the semantic level (in other words, syntax carries meaning).

Background

acl_arc_intent_classification:train:1271

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: A detailed introduction to the SBD problem can be found in Palmer and Hearst (1997). Furthermore, an abbreviation itself can be the last token in a sentence in which case its period acts at the same time as part of this abbreviation and as the end-of-sentence indicator (fullstop). In certain cases, however, a period denotes a decimal point or is a part of an abbreviation, and thus it does not necessarily signal a sentence boundary. Citation Sentence: A detailed introduction to the SBD problem can be found in Palmer and Hearst ( 1997 ) . Context after the citation: The disambiguation of capitalized words and sentence boundaries presents a chicken-and-egg problem. If we know that a capitalized word that follows a period is a common word, we can safely assign such period as sentence terminal. On the other hand, if we know that a period is not sentence terminal, then we can conclude that the following capitalized word is a proper name. Another frequent source of ambiguity in end-of-sentence marking is introduced by abbreviations: if we know that the word that precedes a period is not an abbreviation, then almost certainly this period denotes a sentence boundary.

Background

acl_arc_intent_classification:train:1272

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Task properties Determining whether or not a speaker supports a proposal falls within the realm of sentiment analysis, an extremely active research area devoted to the computational treatment of subjective or opinion-oriented language (early work includes Wiebe and Rapaport (1988), Hearst (1992), Sack (1994), and Wiebe (1994); see Esuli (2006) for an active bibliography). Note that from an experimental point of view, this is a very convenient problem to work with because we can automatically determine ground truth (and thus avoid the need for manual annotation) simply by consulting publicly available voting records. In this paper, we investigate the following specific instantiation of this problem: we seek to determine from the transcripts of U.S. Congressional floor debates whether each “speech” (continuous single-speaker segment of text) represents support for or opposition to a proposed piece of legislation. Citation Sentence: Task properties Determining whether or not a speaker supports a proposal falls within the realm of sentiment analysis , an extremely active research area devoted to the computational treatment of subjective or opinion-oriented language ( early work includes Wiebe and Rapaport ( 1988 ) , Hearst ( 1992 ) , Sack ( 1994 ) , and Wiebe ( 1994 ) ; see Esuli ( 2006 ) for an active bibliography ) . Context after the citation: In particular, since we treat each individual speech within a debate as a single “document”, we are considering a version of document-level sentiment-polarity classification, namely, automatically distinguishing between positive and negative documents (Das and Chen, 2001; Pang et al., 2002; Turney, 2002; Dave et al., 2003). Most sentiment-polarity classifiers proposed in the recent literature categorize each document independently. A few others incorporate various measures of inter-document similarity between the texts to be labeled (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005; Goldberg and Zhu, 2006). Many interesting opinion-oriented documents, however, can be linked through certain relationships that occur in the context of evaluative discussions.

Background

acl_arc_intent_classification:train:1273

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The BEETLE II system architecture is designed to overcome these limitations (Callaway et al., 2007). It also becomes more difficult to experiment with different tutorial policies within the system due to the inherent completixites in applying tutoring strategies consistently across a large number of individual hand-authored remediations. author a different remediation dialogue for every possible dialogue state. Citation Sentence: The BEETLE II system architecture is designed to overcome these limitations ( Callaway et al. , 2007 ) . Context after the citation: It uses a deep parser and generator, together with a domain reasoner and a diagnoser, to produce detailed analyses of student utterances and generate feedback automatically. This allows the system to consistently apply the same tutorial policy across a range of questions. To some extent, this comes at the expense of being able to address individual student misconceptions. However, the system’s modular setup and extensibility make it a suitable testbed for both computational linguistics algorithms and more general questions about theories of learning.

Background

acl_arc_intent_classification:train:1274

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: For the evaluation of the results we use the BLEU score (Papineni et al., 2001). tion 7. The generation process consisting mainly of word reordering and lexical insertions is explained in Section 6, an example illustrating the generation steps is presented in Sec- Citation Sentence: For the evaluation of the results we use the BLEU score ( Papineni et al. , 2001 ) . Context after the citation: Section 8 compares translations generated from automatically built and manually annotated tectogrammatical representations. We also compare the results with the output generated by the statistical translation system GIZA++/ISI ReWrite Decoder (AlOnaizan et al., 1999; Och and Ney, 2000; Germann et al., 2001), trained on the same parallel corpus.

Uses

acl_arc_intent_classification:train:1275

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: There are very few reported attempts at corpus-based automation of help-desk responses (Carmel, Shtalhaim, and Soffer 2000; Lapalme and Kosseim 2003; Bickel and Scheffer 2004; Malik, Subramaniam, and Kaushik 2007). The process of composing a planned response for a new request is informed by probabilistic and lexical properties of the requests and responses in the corpus. In contrast, the techniques examined in this article are corpus-based and data-driven. Citation Sentence: There are very few reported attempts at corpus-based automation of help-desk responses ( Carmel , Shtalhaim , and Soffer 2000 ; Lapalme and Kosseim 2003 ; Bickel and Scheffer 2004 ; Malik , Subramaniam , and Kaushik 2007 ) . Context after the citation: eResponder, the system developed by Carmel, Shtalhaim, and Soffer (2000), retrieves a list of request–response pairs and presents a ranked list of responses to the user. If the user is unsatisfied with this list, an operator is asked to generate a new response. The operator is assisted in this task by the retrieval results: The system highlights the request-relevant sentences in the ranked responses. However, there is no attempt to automatically generate a single response.

CompareOrContrast

acl_arc_intent_classification:train:1276

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For an introduction to maximum entropy modeling and training procedures, the reader is referred to the corresponding literature, for instance (Berger et al., 1996) or (Ratnaparkhi, 1997). This enables us to translate the verb alone even if it occurs in the training corpus only as a spliced entry. • the verb contained in a transformed lexicon entry (e.g. 'go' for 'you_go' or 'you_will_go): hs, ,v(s ,t) = S(s. s') • V erb(t, v) , where Citation Sentence: For an introduction to maximum entropy modeling and training procedures , the reader is referred to the corresponding literature , for instance ( Berger et al. , 1996 ) or ( Ratnaparkhi , 1997 ) . Context after the citation:

Background

acl_arc_intent_classification:train:1277

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: This design idea was adopted from TANKA (Barker et al., 1997b). Our system begins operation with a minimum of manually encoded knowledge, and accumulates information as it processes the text. Citation Sentence: This design idea was adopted from TANKA ( Barker et al. , 1997b ) . Context after the citation: The only manually encoded knowledge is a dictionary of markers (subordinators, coordinators, prepositions). This resource does not affect the syntacticsemantic graph-matching heuristic. Because the system gradually accumulates knowledge as it goes through the input text, it uses a form of memory-based learning to make predictions about the semantic relation that fits the current pair. The type of knowledge that it accumulates consists of previously analysed pairs, together with the semantic relation assigned, and a syntactic-semantic graph centered on each word in a sentence which appears as the main element in a processed pair.

Uses

acl_arc_intent_classification:train:1278

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The problem of handling ill-formed input has been studied by Carbonell and Hayes (1983), Granger (1983), Jensen et al. (1983), Kwasny and Sondheimer (1981), Riesbeck and Schank (1976), Thompson (1980), Weischedel and Black (1980), and Weischedel and Sondheimer (1983). A detailed description of the kinds of expectation mechanisms appearing in these systems appears in Fink (1983). While some of these systems did exhibit expectation capabilities at the sentence level, none acquired dialogues of the kind described here for the sake of dialogue level expectation and error correction. Citation Sentence: The problem of handling ill-formed input has been studied by Carbonell and Hayes ( 1983 ) , Granger ( 1983 ) , Jensen et al. ( 1983 ) , Kwasny and Sondheimer ( 1981 ) , Riesbeck and Schank ( 1976 ) , Thompson ( 1980 ) , Weischedel and Black ( 1980 ) , and Weischedel and Sondheimer ( 1983 ) . Context after the citation: A wide variety of techniques have been developed for addressing problems at the word, phrase, sentence, and in some cases, dialogue level. However, these methodologies have not used historical information at the dialogue level as described here. In most cases, the goal of these systems is to characterize the ill-formed input into classes of errors and to correct on that basis. The work described here makes no attempt to classify the errors, but treats them as random events that occur at any point in a sentence.

CompareOrContrast

acl_arc_intent_classification:train:1279

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Briscoe and Carroll (1997), by comparison, employ 163 distinct predefined frames. from Penn-II and 221 from Penn-III. 5 To recap, if two verbs have the same subcategorization requirements (e.g., give([subj, obj, obj2]), send([subj, obj, obj2])), then that frame [subj, obj, obj2] is counted only once. Citation Sentence: Briscoe and Carroll ( 1997 ) , by comparison , employ 163 distinct predefined frames . Context after the citation:

Background

acl_arc_intent_classification:train:128

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: In addition to its explanatory capacity, this symbolic acquisition technique has obtained good results for other acquisition tasks when compared to existing statistical techniques (Bouillon et al., 2002). highlighting of the corpus-specific structures conveying the target element. understanding of the results, that is, why a specific element has been retrieved or not; 2. Citation Sentence: In addition to its explanatory capacity , this symbolic acquisition technique has obtained good results for other acquisition tasks when compared to existing statistical techniques ( Bouillon et al. , 2002 ) . Context after the citation:

Motivation

acl_arc_intent_classification:train:1280

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The context of a current token ti is clearly one of the most important features in predicting whether ti is a mention or not (Florian et al., 2004). We use features such as the shallow parsing information associated with the tokens in a window of 3 tokens, POS, etc. This set can be partitioned into 4 categories: lexical, syntactic, gazetteer-based, and those obtained by running other named-entity classifiers (with different tag sets). Citation Sentence: The context of a current token ti is clearly one of the most important features in predicting whether ti is a mention or not ( Florian et al. , 2004 ) . Context after the citation: We denote these features as backward token tri-grams and forward token tri-grams for the previous and next context of ti respectively. For a token ti, the backward token n-gram feature will contains the previous n − 1 tokens in the history (ti_n+1.... ti_1) and the forward token n-gram feature will contains the next n − 1 tokens (ti+1.... ti+n_1).

Background

acl_arc_intent_classification:train:1281

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Features using the word context (left and right tokens) have been shown to be very helpful in coreference resolution (Luo et al., 2004). Citation Sentence: Features using the word context ( left and right tokens ) have been shown to be very helpful in coreference resolution ( Luo et al. , 2004 ) . Context after the citation: For Arabic, since words are morphologically derived from a list of roots (stems), we expected that a feature based on the right and left stems would lead to improvement in system accuracy. Let m1 and m2 be two candidate mentions where a mention is a string of tokens (prefixes, stems, and suffixes) extracted from the segmented text. In order to make a decision in either linking the two mentions or not we use additional features such as: do the stems in m1 and m2 match, do stems in m1 match all stems in m2, do stems in m1 partially match stems in m2. We proceed similarly for prefixes and suffixes.

Uses

acl_arc_intent_classification:train:1282

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Here, the PET and GR kernel perform similar: this is different from the results of (Nguyen et al., 2009) where GR performed much worse than PET for ACE data. As in the undersampled system, when the data is balanced, SqGRW (sequence kernel on dependency tree in which grammatical relations are inserted as intermediate nodes) achieves the best recall. As in the baseline system, a combination of structures performs best. Citation Sentence: Here , the PET and GR kernel perform similar : this is different from the results of ( Nguyen et al. , 2009 ) where GR performed much worse than PET for ACE data . Context after the citation: This exemplifies the difference in the nature of our event annotations from that of ACE relations. Since the average distance between target entities in the surface word order is higher for our events, the phrase structure trees are bigger. This means that implicit feature space is much sparser and thus not the best representation. for relation detection.

CompareOrContrast

acl_arc_intent_classification:train:1283

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In modern syntactic theories (e.g., lexical-functional grammar [LFG] [Kaplan and Bresnan 1982; Bresnan 2001; Dalrymple 2001], head-driven phrase structure grammar [HPSG] [Pollard and Sag 1994], tree-adjoining grammar [TAG] [Joshi 1988], and combinatory categorial grammar [CCG] [Ades and Steedman 1982]), the lexicon is the central repository for much morphological, syntactic, and semantic information. Citation Sentence: In modern syntactic theories ( e.g. , lexical-functional grammar [ LFG ] [ Kaplan and Bresnan 1982 ; Bresnan 2001 ; Dalrymple 2001 ] , head-driven phrase structure grammar [ HPSG ] [ Pollard and Sag 1994 ] , tree-adjoining grammar [ TAG ] [ Joshi 1988 ] , and combinatory categorial grammar [ CCG ] [ Ades and Steedman 1982 ] ) , the lexicon is the central repository for much morphological , syntactic , and semantic information . Context after the citation: * National Centre for Language Technology, School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland. E-mail: {rodonovan,mburke,acahill,josef,awayl@computing.dcu.ie. † Centre for Advanced Studies, IBM, Dublin, Ireland. Submission received: 19 March 2004; revised submission received: 18 December 2004; accepted for publication: 2 March 2005.

Background

acl_arc_intent_classification:train:1284

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Some works abstract perception via the usage of symbolic logic representations (Chen et al., 2010; Chen and Mooney, 2011; Matuszek et al., 2012; Artzi and Zettlemoyer, 2013), while others choose to employ concepts elicited from psycholinguistic and cognition studies. Many approaches to multimodal research have succeeded by abstracting away raw perceptual information and using high-level representations instead. The language grounding problem has received significant attention in recent years, owed in part to the wide availability of data sets (e.g. Flickr, Von Ahn (2006)), computing power, improved computer vision models (Oliva and Torralba, 2001; Lowe, 2004; Farhadi et al., 2009; Parikh and Grauman, 2011) and neurological evidence of ties between the language, perceptual and motor systems in the brain (Pulverm¨uller et al., 2005; Tettamanti et al., 2005; Aziz-Zadeh et al., 2006). Citation Sentence: Some works abstract perception via the usage of symbolic logic representations ( Chen et al. , 2010 ; Chen and Mooney , 2011 ; Matuszek et al. , 2012 ; Artzi and Zettlemoyer , 2013 ) , while others choose to employ concepts elicited from psycholinguistic and cognition studies . Context after the citation: Within the latter category, the two most common representations have been association norms, where subjects are given a 1http://stephenroller.com/research/ emnlp13 cue word and name the first (or several) associated words that come to mind (e.g., Nelson et al. (2004)), and feature norms, where subjects are given a cue word and asked to describe typical properties of the cue concept (e.g., McRae et al. (2005)). Griffiths et al. (2007) helped pave the path for cognitive-linguistic multimodal research, showing that Latent Dirichlet Allocation outperformed Latent Semantic Analysis (Deerwester et al., 1990) in the prediction of association norms.

Background

acl_arc_intent_classification:train:1285

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: This is where robust syntactic systems like SATZ (Palmer and Hearst 1997) or the POS tagger reported in Mikheev (2000), which do not heavily rely on word capitalization and are not sensitive to document length, have an advantage. We noted in Section 8 that very short documents of one to three sentences also present a difficulty for our approach. optical character reader–generated texts. Citation Sentence: This is where robust syntactic systems like SATZ ( Palmer and Hearst 1997 ) or the POS tagger reported in Mikheev ( 2000 ) , which do not heavily rely on word capitalization and are not sensitive to document length , have an advantage . Context after the citation: Our DCA uses information derived from the entire document and thus can be used as a complement to approaches based on the local context. When we incorporated the DCA system into a POS tagger (Section 8), we measured a 30–35% cut in the error rate on proper-name identification in comparison to DCA or the POS-tagging approaches alone. This in turn enabled better tagging of sentence boundaries: a 0.20% error rate on the Brown corpus and a 0.31% error rate on the WSJ corpus, which corresponds to about a 20% cut in the error rate in comparison to DCA or the POS-tagging approaches alone. We also investigated the portability of our approach to other languages and obtained encouraging results on a corpus of news in Russian.

CompareOrContrast

acl_arc_intent_classification:train:1286

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Our work is inspired by the latent left-linking model in Chang et al. (2013) and the ILP formulation from Chang et al. (2011). This section describes our joint coreference resolution and mention head detection framework. Citation Sentence: Our work is inspired by the latent left-linking model in Chang et al. ( 2013 ) and the ILP formulation from Chang et al. ( 2011 ) . Context after the citation: The joint learning and inference model takes as input mention head candidates 4Available at http://cogcomp.cs.illinois. edu/page/software_view/Coref (Sec.

Background

acl_arc_intent_classification:train:1287

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For example, Radzinsky (1991) proves that Chinese numerals such as wu zhao zhao zhao zhao zhao wu zhao zhao zhao zhao wu zhao zhao zhao wu zhao zhao wu zhao, for the number 5000000000000000005000000000000005000000000005000000005000, are not context-free, which implies that Chinese is not a context-free language and thus might parse in exponential worst-case time. Average, since it can happen that the grammar does admit hard-toparse sentences that are not used (or at least not frequently used) in the real corpus. Specific, since a specific grammar belonging to a high complexity class may well prove to parse much faster than the worst grammar of its class, even with the general algorithm, if the possible time-consuming behavior of the algorithm never happens for this grammar. Citation Sentence: For example , Radzinsky ( 1991 ) proves that Chinese numerals such as wu zhao zhao zhao zhao zhao wu zhao zhao zhao zhao wu zhao zhao zhao wu zhao zhao wu zhao , for the number 5000000000000000005000000000000005000000000005000000005000 , are not context-free , which implies that Chinese is not a context-free language and thus might parse in exponential worst-case time . Context after the citation: Do such arguments—no doubt important for mathematical linguistics—have any direct consequences for an engineering linguistics? Even if a Chinese grammar includes a non-context-free rule for parsing such numerals, how frequently will it be activated? Does it imply impossibility of processing real Chinese texts in reasonable time? Clearly, the average time for a specific grammar cannot be calculated in such a mathematically elegant way as the worst-case complexity of a grammar class; for the time being, the only practical way to compare the complexity of natural language processing formalisms is the hard one—building real-world systems and comparing their efficiency and coverage.

Background

acl_arc_intent_classification:train:1288

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Barzilay and McKeown (2001) also note that the applicability of paraphrases is strongly influenced by context. For example, year end is an unsuitable paraphrase for the end of this year in the sentence The chart compares the gold price at the end of last year with the end of this year. Moreover, there are some (phrase, paraphrase) pairs which are only suitable in particular contexts. Citation Sentence: Barzilay and McKeown ( 2001 ) also note that the applicability of paraphrases is strongly influenced by context . Context after the citation: Section 4 describes our method for determining if a paraphrase is suitable in a given context.

Background

acl_arc_intent_classification:train:1289

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: Brockmann and Lapata (2003) have showed that WordNet-based approaches do not always outperform simple frequency-based models, and a number of techniques have been recently proposed which may offer ideas for refining our current unsupervised approach (Erk, 2007; Bergsma et al., 2008). Considerable research has been done on SP acquisition most of which has involved collecting argument headwords from data and generalizing to WordNet classes. In addition to the ideas mentioned earlier, our future plans include looking into optimal ways of acquiring SPs for verb classification. Citation Sentence: Brockmann and Lapata ( 2003 ) have showed that WordNet-based approaches do not always outperform simple frequency-based models , and a number of techniques have been recently proposed which may offer ideas for refining our current unsupervised approach ( Erk , 2007 ; Bergsma et al. , 2008 ) . Context after the citation: The number and type (and combination) of GRs for which SPs can be reliably acquired, especially when the data is sparse, requires also further investigation. In addition, we plan to investigate other potentially useful features for verb classification (e.g. named entities and preposition classes) and explore semi-automatic ML technology and active learning for guiding the classification. Finally, we plan to conduct a bigger experiment with a larger number of verbs, and conduct evaluation in the context of practical application tasks.

FutureWork

acl_arc_intent_classification:train:129

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: Promising features for classification include part of speech, frequency of co-occurrence, relative word position, and translational entropy (Melamed, 1997). Even better accuracy can be achieved with a more fine-grained link class structure. Unlike other translation models, the word-to-word model can automatically produce dictionary-sized translation lexicons, and it can do so with over 99% accuracy. Citation Sentence: Promising features for classification include part of speech , frequency of co-occurrence , relative word position , and translational entropy ( Melamed , 1997 ) . Context after the citation: Another interesting extension is to broaden the definition of a "word" to include multi-word lexical units (Smadja, 1992). If such units can be identified a priori, their translations can be estimated without modifying the word-to-word model. In this manner, the model can account for a wider range of translation phenomena.

FutureWork

acl_arc_intent_classification:train:1290

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For descriptions of SMT systems see for example (Germann et al., 2001; Och et al., 1999; Tillmann and Ney, 2002; Vogel et al., 2000; Wang and Waibel, 1997). In the work presented here, we restrict ourselves to transforming only one language of the two: the source, which has the less inflected morphology. If necessary, the inverse of these transformations will be applied to the generated output string. Citation Sentence: For descriptions of SMT systems see for example ( Germann et al. , 2001 ; Och et al. , 1999 ; Tillmann and Ney , 2002 ; Vogel et al. , 2000 ; Wang and Waibel , 1997 ) . Context after the citation:

Background

acl_arc_intent_classification:train:1291

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: like information extraction (Yates and Etzioni, 2009) and textual entailment (Berant et al., 2010). Furthermore, if we use a labeled-ALS as the metric for augmented-loss training, we also see a considerable increase in LAS. When training with ALS (labeled and unlabeled), we see an improvement in UAS, LAS, and ALS. Citation Sentence: like information extraction ( Yates and Etzioni , 2009 ) and textual entailment ( Berant et al. , 2010 ) . Context after the citation: In Table 3 we show results for parsing with the ALS augmented-loss objective. For each parser, we consider two different ALS objective functions; one based on unlabeled-ALS and the other on labeledALS. The arc-length score penalizes incorrect longdistance dependencies more than local dependencies; long-distance dependencies are often more destructive in preserving sentence meaning and can be more difficult to predict correctly due to the larger context on which they depend. Combining this with the standard attachment scores biases training to focus on the difficult head dependencies.

Background

acl_arc_intent_classification:train:1292

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In a final processing stage, we generalize over the marker lexicon following a process found in Block (2000). We ignore here the trivially true lexical chunk “<QUANT> 14 : 14.” <DET> the board : le conseil <DET> the : le <PREP> to <QUANT> 14 members : a` 14 membres <QUANT> 14 members : 14 membres <LEX> expanding : augmente <LEX> board : conseil <PREP> to : a` <LEX> members : membres Citation Sentence: In a final processing stage , we generalize over the marker lexicon following a process found in Block ( 2000 ) . Context after the citation: In Block’s approach, word alignments are assigned probabilities by means of a statistical word alignment tool. In a subsequent stage, chunk pairs are extracted, which are then generalized to produce a set of translation templates for each (source, target) segment. Block distinguishes chunks from “patterns,” as we do: His chunks are similar to our marker chunks, and his patterns are similar to our generalized marker chunks. Once chunks are derived from (source, target) alignments, patterns are computed from the derived chunks by means of the following algorithm: “for each pair of chunk pairs ((CS1,CT1),(CS2, CT2)), if CS1 is a substring in CS2 and CT1 is a substring in CT2, then (PS, PT) is a pattern pair where PS equals CS2 with CS1 replaced by a variable V and PT equals CT2 with CT1 replaced by V” (Block 2000, pages 414–415).

Uses

acl_arc_intent_classification:train:1293

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: One approach to this problem consists in defining, within the Cut-free atomic-id space, normal form derivations in which the succession of rule application is regulated (Konig 1989, Hepple 1990, Hendriks 1993). For example, composition as above has the following alternative derivation: But even in the Cut-free atomic-id calculus there is spurious ambiguity: equivalent derivations differing only in irrelevant rule ordering. Citation Sentence: One approach to this problem consists in defining , within the Cut-free atomic-id space , normal form derivations in which the succession of rule application is regulated ( Konig 1989 , Hepple 1990 , Hendriks 1993 ) . Context after the citation: Each sequent has a distinguished category formula (underlined) on which rule applications are keyed: In the regulated calculus there is no spurious ambiguity, and provided there is no explicit or implicit antecedent product, i.e., provided .1_, is not needed, r = A is a theorem of the Lambek calculus iff F = is a theorem of the regulated calculus. However, apart from the issue regarding . L, there is a general cause for dissatisfaction with this approach: it assumes the initial presence of the entire sequent to be proved, i.e., it is in principle nonincrementah on the other hand, allowing incrementality on the basis of Cut would reinstate with a vengeance the problem of spurious ambiguity, for then what are to be the Cut formulas?

Background

acl_arc_intent_classification:train:1294

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The SPR uses rules automatically learned from training data, using techniques similar to (Collins, 2000; Freund et al., 1998). Our primary contribution is a method for training the SPR. In the second phase, the sentence-plan-ranker (SPR) ranks the sample sentence plans, and then selects the top-ranked output to input to the surface realizer. Citation Sentence: The SPR uses rules automatically learned from training data , using techniques similar to ( Collins , 2000 ; Freund et al. , 1998 ) . Context after the citation: Our method for training a sentence planner is unique in neither depending on hand-crafted rules, nor on the existence of a text or speech corpus in the domain of the sentence planner obtained from the interaction of a human with a system or another human. We show that the trained SPR learns to select a sentence plan whose rating on average is only 5% worse than the top human-ranked sentence plan. In the remainder of the paper, section 2 describes the sentence planning task in more detail. We then describe the sentence plan generator (SPG) in section 3, the sentence plan ranker (SPR) in section 4, and the results in section 5.

CompareOrContrast

acl_arc_intent_classification:train:1295

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: McKnight and Srinivasan (2003) have previously examined the task of categorizing sentences in medical abstracts using supervised discriminative machine learning techniques. Furthermore, the availability of rich ontological resources, in the form of the Unified Medical Language System (UMLS) (Lindberg et al., 1993), and the availability of software that leverages this knowledge— MetaMap (Aronson, 2001) for concept identification and SemRep (Rindflesch and Fiszman, 2003) for relation extraction—provide a foundation for studying the role of semantics in various tasks. (NLM), which also serves as a readily available corpus of abstracts for our experiments. Citation Sentence: McKnight and Srinivasan ( 2003 ) have previously examined the task of categorizing sentences in medical abstracts using supervised discriminative machine learning techniques . Context after the citation: Building on the work of Ruch et al. (2003) in the same domain, we present a generative approach that attempts to directly model the discourse structure of MEDLINE abstracts using Hidden Markov Models (HMMs); cfXXX (Barzilay and Lee, 2004). Although our results were not obtained from the same exact collection as those used by authors of these two previous studies, comparable experiments suggest that our techniques are competitive in terms of performance, and may offer additional advantages as well. Discriminative approaches (especially SVMs) have been shown to be very effective for many supervised classification tasks; see, for example, (Joachims, 1998; Ng and Jordan, 2001). However, their high computational complexity (quadratic in the number of training samples) renders them prohibitive for massive data processing.

CompareOrContrast

acl_arc_intent_classification:train:1296

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For complementing this database and for converse constructions, the LADL tables (Gross, 1975) can furthermore be resorted to, which list detailed syntactico-semantic descriptions for 5 000 verbs and 25 000 verbal expressions. For shuffling paraphrases, french alternations are partially described in (Saint-Dizier, 1999) and a resource is available which describes alternation and the mapping verbs/alternations for roughly 1 700 verbs. However as for other types of synonymy, distributional analysis and clustering techniques can be used to develop such resources. Citation Sentence: For complementing this database and for converse constructions , the LADL tables ( Gross , 1975 ) can furthermore be resorted to , which list detailed syntactico-semantic descriptions for 5 000 verbs and 25 000 verbal expressions . Context after the citation: In particular, (Gross, 1989) lists the converses of some 3 500 predicative nouns.

FutureWork

acl_arc_intent_classification:train:1297

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: These operations are not domain-specific and are similar to those of previous aggregation components (Rambow and Korelsky,1992; Shaw, 1998; Danlos, 2000), although the various MERGE operations are, to our knowledge, novel in this form. Joins two complete clauses with a period. PERIOD. Citation Sentence: These operations are not domain-specific and are similar to those of previous aggregation components ( Rambow and Korelsky ,1992 ; Shaw , 1998 ; Danlos , 2000 ) , although the various MERGE operations are , to our knowledge , novel in this form . Context after the citation: The result of applying the operations is a sentence plan tree (or sp-tree for short), which is a binary tree with leaves labeled by all the elementary speech acts from the input text plan, and with its interior nodes labeled with clause-combining operations3. Each node is also associated with a DSyntS: the leaves (which correspond to elementary speech acts from the input text plan) are linked to a canonical DSyntS for that speech act (by lookup in a hand-crafted dictionary). The interior nodes are associated with DSyntSs by executing their clausecombing operation on their two daughter nodes.

Background

acl_arc_intent_classification:train:1298

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Following construction of the marker lexicon, the (source, target) chunks are generalized further using a methodology based on Block (2000) to permit a limited form of insertion in the translation process. Work using the marker hypothesis for MT adapts this monolingual mapping for pairs of languages: It is reasonably straightforward to map an English determiner-noun sequence onto a Japanese noun–case marker segment, once one has identified the sets of marker tags in the languages to be translated. Nevertheless, Juola (1998, page 23) observes that “a slightly more general mapping, where two adjacent terminal symbols can be merged into a single lexical item (for example, a word and its case-marking), can capture this sort of result quite handily.” Citation Sentence: Following construction of the marker lexicon , the ( source , target ) chunks are generalized further using a methodology based on Block ( 2000 ) to permit a limited form of insertion in the translation process . Context after the citation: As a byproduct of the chosen methodology, we also derive a standard ”word-level” translation lexicon. These various resources render the set of original translation pairs far more useful in deriving translations of previously unseen input. In Section 3, we describe in detail the segmentation process, together with the procedure whereby target chunks are combined to produce candidate translations. In Section 4, we report initially on two experiments in which we test different versions of our EBMT system against test sets of NPs and sentences.

Uses

acl_arc_intent_classification:train:1299

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: transition-based dependency parsing framework (Nivre, 2008) using an arc-eager transition strategy and are trained using the perceptron algorithm as in Zhang and Clark (2008) with a beam size of 8. • Transition-based: An implementation of the For our experiments we focus on two dependency parsers. Citation Sentence: transition-based dependency parsing framework ( Nivre , 2008 ) using an arc-eager transition strategy and are trained using the perceptron algorithm as in Zhang and Clark ( 2008 ) with a beam size of 8 . Context after the citation: Beams with varying sizes can be used to produce k-best lists. The features used by all models are: the part-ofspeech tags of the first four words on the buffer and of the top two words on the stack; the word identities of the first two words on the buffer and of the top word on the stack; the word identity of the syntactic head of the top word on the stack (if available); dependency arc label identities for the top word on the stack, the left and rightmost modifier of the top word on the stack, and the left most modifier of the first word in the buffer (if available). All feature conjunctions are included. • Graph-based: An implementation of graph-

Uses

acl_arc_intent_classification:train:13

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: fθ on demand (Mohri et al., 1998) can pay off here, since only part of fθ may be needed subsequently.) 10Traditionally log(strength) values are called weights, but this paper uses “weight” to mean something else. In the extreme, if each input string is fully observed (not the case if the input is bound by composition to the output of a one-to-many FST), one can succeed by restricting g to each input string in turn; this amounts to manually dividing f(x, y) by g(x). Citation Sentence: fθ on demand ( Mohri et al. , 1998 ) can pay off here , since only part of fθ may be needed subsequently . ) Context after the citation: As training data we are given a set of observed (input, output) pairs, (xi, yi). These are assumed to be independent random samples from a joint distribution of the form fe(x, y); the goal is to recover the true ˆ0. Samples need not be fully observed (partly supervised training): thus xi C E*, yi C A* may be given as regular sets in which input and output were observed to fall. For example, in ordinary HMM training, xi = E* and represents a completely hidden state sequence (cfXXX Ristad (1998), who allows any regular set), while yi is a single string representing a completely observed emission sequence.11 What to optimize?

Background

acl_arc_intent_classification:train:130

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: ment (Sarkar and Wintner, 1999; Doran et al., 2000; Makino et al., 1998). 1In this paper, we use the term LTAG to refer to FBLTAG, if not confusing. There have been many studies on parsing techniques (Poller and Becker, 1998; Flickinger et al., 2000), ones on disambiguation models (Chiang, 2000; Kanayama et al., 2000), and ones on programming/grammar-development environ- Citation Sentence: ment ( Sarkar and Wintner , 1999 ; Doran et al. , 2000 ; Makino et al. , 1998 ) . Context after the citation: These works are restricted to each closed community, and the relation between them is not well discussed. Investigating the relation will be apparently valuable for both communities. In this paper, we show that the strongly equivalent grammars enable the sharing of “parsing techniques”, which are dependent on each computational framework and have never been shared among HPSG and LTAG communities. We apply our system to the latest version of the XTAG English grammar (The XTAG Research Group, 2001), which is a large-scale FB-LTAG grammar.

Background

acl_arc_intent_classification:train:1300

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The ability to explicitly identify these sections in unstructured text could play an important role in applications such as document summarization (Teufel and Moens, 2000), information retrieval (Tbahriti et al., 2005), information extraction (Mizuta et al., 2005), and question answering. fields generally follow the pattern of “introduction”, “methods”, “results”, and “conclusions” (SalangerMeyer, 1990; Swales, 1990; Or˘asan, 2001). As an example, scientific abstracts across many different Citation Sentence: The ability to explicitly identify these sections in unstructured text could play an important role in applications such as document summarization ( Teufel and Moens , 2000 ) , information retrieval ( Tbahriti et al. , 2005 ) , information extraction ( Mizuta et al. , 2005 ) , and question answering . Context after the citation: Although there is a trend towards analysis of full article texts, we believe that abstracts still provide a tremendous amount of information, and much value can still be extracted from them. For example, Gay et al. (2005) experimented with abstracts and full article texts in the task of automatically generating index term recommendations and discovered that using full article texts yields at most a 7.4% improvement in F-score. Demner-Fushman et al. (2005) found a correlation between the quality and strength of clinical conclusions in the full article texts and abstracts. This paper presents experiments with generative content models for analyzing the discourse structure of medical abstracts, which has been confirmed to follow the four-section pattern discussed above (Salanger-Meyer, 1990).

Background

acl_arc_intent_classification:train:1301

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Recent developments in linguistics, and especially on grammatical theory — for example, Generalised Phrase Structure Grammar (GPSG) (Gazdar et al., 1985), Lexical Functional Grammar (LFG) (Kaplan and Bresnan, 1982) — and on natural language parsing frameworks — for example, Functional Unification Grammar (FUG) (Kay, 1984a), PATR-II (Shieber, 1984) — make it feasible to consider the implementation of efficient systems for the syntactic analysis of substantial fragments of natural language. This paper describes the extraction of this, and other, information from LDOCE and discusses the utility of the coding system for automated natural language processing. The grammar coding system employed by the Longman Dictionary of Contemporary English (henceforth LDOCE) is the most comprehensive description of grammatical properties of words to be found in any published dictionary available in machine readable form. Citation Sentence: Recent developments in linguistics , and especially on grammatical theory -- for example , Generalised Phrase Structure Grammar ( GPSG ) ( Gazdar et al. , 1985 ) , Lexical Functional Grammar ( LFG ) ( Kaplan and Bresnan , 1982 ) -- and on natural language parsing frameworks -- for example , Functional Unification Grammar ( FUG ) ( Kay , 1984a ) , PATR-II ( Shieber , 1984 ) -- make it feasible to consider the implementation of efficient systems for the syntactic analysis of substantial fragments of natural language . Context after the citation: These developments also emphasise that if natural language processing systems are to be able to handle the grammatical and semantic idiosyncracies of individual lexical items elegantly and efficiently, then the lexicon must be a central component of the parsing system. Real-time parsing imposes stringent requirements on a dictionary support environment; at the very least it must allow frequent and rapid access to the information in the dictionary via the dictionary head words. The research described below is taking place in the context of three collaborative projects (Boguraev, 1987; Russell et al., 1986; Phillips and Thompson, 1986) to develop a general-purpose, wide coverage morphological and syntactic analyser for English. One motivation for our interest in machine readable dictionaries is to attempt to provide a substantial lexicon with lexical entries containing grammatical information compatible with the grammatical framework employed by the analyser.

Background

acl_arc_intent_classification:train:1302

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The latter question is tackled by applicationspecific evaluation, where a measure is tested within the framework of a certain application, e.g. word sense disambiguation (Patwardhan et al., 2003) or malapropism detection (Budanitsky and Hirst, 2006). Mathematical analysis can assess a measure with respect to some formal properties, e.g. whether a measure is a metric (Lin, 1998).4 However, mathematical analysis cannot tell us whether a measure closely resembles human judgments or whether it performs best when used in a certain application. According to Budanitsky and Hirst (2006), there are three prevalent approaches for evaluating SR measures: mathematical analysis, applicationspecific evaluation and comparison with human judgments. Citation Sentence: The latter question is tackled by applicationspecific evaluation , where a measure is tested within the framework of a certain application , e.g. word sense disambiguation ( Patwardhan et al. , 2003 ) or malapropism detection ( Budanitsky and Hirst , 2006 ) . Context after the citation: Lebart and Rajman (2000) argue for application-specific evaluation of similarity measures, because measures are always used for some task. But they also note that evaluating a measure as part of a usually complex application only indirectly assesses its quality. A certain measure may work well in one application, but not in another. Application-based evaluation can only state the fact, but give little explanation about the reasons.

Background

acl_arc_intent_classification:train:1303

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Lebart and Rajman (2000) argue for application-specific evaluation of similarity measures, because measures are always used for some task. The latter question is tackled by applicationspecific evaluation, where a measure is tested within the framework of a certain application, e.g. word sense disambiguation (Patwardhan et al., 2003) or malapropism detection (Budanitsky and Hirst, 2006). Mathematical analysis can assess a measure with respect to some formal properties, e.g. whether a measure is a metric (Lin, 1998).4 However, mathematical analysis cannot tell us whether a measure closely resembles human judgments or whether it performs best when used in a certain application. Citation Sentence: Lebart and Rajman ( 2000 ) argue for application-specific evaluation of similarity measures , because measures are always used for some task . Context after the citation: But they also note that evaluating a measure as part of a usually complex application only indirectly assesses its quality. A certain measure may work well in one application, but not in another. Application-based evaluation can only state the fact, but give little explanation about the reasons. The remaining approach comparison with human judgments is best suited for application independent evaluation of relatedness measures.

Background

acl_arc_intent_classification:train:1304

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: The first direct application of parse forest in translation is our previous work (Mi et al., 2008) which translates a packed forest from a parser; it is also the base system in our experiments (see below). The forest concept is also used in machine translation decoding, for example to characterize the search space of decoding with integrated language models (Huang and Chiang, 2007). Nevertheless we suspect that their extraction algorithm is in principle similar to ours, although they do not provide details of forest-based fragmentation (Algorithm 1) which we think is non-trivial. Citation Sentence: The first direct application of parse forest in translation is our previous work ( Mi et al. , 2008 ) which translates a packed forest from a parser ; it is also the base system in our experiments ( see below ) . Context after the citation: This work, on the other hand, is in the orthogonal direction, where we utilize forests in rule extraction instead of decoding. BLEU score Our experiments will use both default 1-best decoding and forest-based decoding. As we will see in the next section, the best result comes when we combine the merits of both, i.e., using forests in both rule extraction and decoding. There is also a parallel work on extracting rules from k-best parses and k-best alignments (Venugopal et al., 2008), but both their experiments and our own below confirm that extraction on k-best parses is neither efficient nor effective.

Extends

acl_arc_intent_classification:train:1305

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: 4 This interpretation of the signature is sometimes referred to as closed world (Gerdemann and King 1994; Gerdemann 1995). To avoid confusion, we will only use the terminology introduced in the text. Types are also referred to as sorts, appropriateness conditions as feature declarations, and features as attributes. Citation Sentence: 4 This interpretation of the signature is sometimes referred to as closed world ( Gerdemann and King 1994 ; Gerdemann 1995 ) . Context after the citation: 5 An in-depth discussion including a comparison of both approaches is provided in Calcagno, Meurers, and Pollard (in preparation). 6 The Partial-VP Topicalization Lexical Rule proposed by Hinrichs and Nakazawa (1994, 10) is a linguistic example. The in-specification of this lexical rule makes use of an append relation to constrain the valence attribute of the auxiliaries serving as its input. In the lexicon, however, the complements of an auxiliary are uninstantiated because it raises the arguments of its verbal complement.

Background

acl_arc_intent_classification:train:1306

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: In their Gaijin system, Veale and Way (1997) give a result of 63% accurate translations obtained for English −> German on a test set of 791 sentences from CorelDRAW manuals. On novel test sentences, he gives results of 72% correct translation. For English −> Urdu, Juola (1997, page 213) notes that “the system learned the original training corpus ... perfectly and could reproduce it without errors”; that is, it scored 100% accuracy when tested against the training corpus. Citation Sentence: In their Gaijin system , Veale and Way ( 1997 ) give a result of 63 % accurate translations obtained for English − > German on a test set of 791 sentences from CorelDRAW manuals . Context after the citation: As in METLA and Gaijin, we exploit lists of known marker words for each language to indicate the start and end of segments. For English, our source language, we use the sets of marker words in (13): <DET> {the, a, an, those, these, ... } <PREP> {in, on, out, with, from, to, under, ... } <QUANT> {all, some, few, many, ... } A similar set (14) was produced for French, the target language in our wEBMT system: <DET> {le, la, l’, les, ce, ces, ceux, cet, ... } <PREP> {dans, sur, avec, de, `a, sous, ... } <QUANT> {tous, tout, toutes, certain, quelques, beaucoup, ... } <CONJ> {et, ou, ... } <POSS> {mon, ma, mes, ton, ta, tes, notre, nos, ... } <PRON> {je, j’, tu, il, elle, ... } In a preprocessing stage, the aligned (source, target) pairs in the phrasal lexicon are traversed word by word, and whenever any such marker word is encountered, a new chunk is begun, with the first word labeled with its marker category (<DET>, <PREP>, etc.). The example in (15) illustrates the results of running the marker hypothesis over the source phrase all uses of asbestos:

CompareOrContrast

acl_arc_intent_classification:train:1307

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: This method can be generalized, inspired by Stolcke and Segal (1994), who derive N-gram probabilities from stochastic context-free grammars. That is, each X E EA is a terminal, or a nonterminal not in the same set Ni as A, but immediately reachable from set N„ through B E N. Our implementation is made slightly more sophisticated by taking EA to be {X I 3B, a, /3[B E N, A B aX0 AX N]}, for each A such that A E N, and recursive(N) = self, for some i. Citation Sentence: This method can be generalized , inspired by Stolcke and Segal ( 1994 ) , who derive N-gram probabilities from stochastic context-free grammars . Context after the citation: By ignoring the probabilities, each N = 1, 2, 3, ... gives rise to a superset approximation that can be described as follows: The set of strings derivable from a nonterminal A is approximated by the set of strings al ... an such that • for each substring v = ai±i ai+N (0 < i < n — N) we have A —>* wvy, for some w and y, • for each prefix v =al ... ai (0 < < n) such that i < N we have A vy, for some y, and • for each suffix v = afro. . an (0 < i < n) such that n — i < N we have

Background

acl_arc_intent_classification:train:1308

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The system was trained on the Penn Treebank (Marcus et al., 1993) WSJ Sections 221 and tested on Section 23 (Table 1), same as used by Magerman (1995), Collins (1997), and Ratnaparkhi (1997), and became a common testbed. Citation Sentence: The system was trained on the Penn Treebank ( Marcus et al. , 1993 ) WSJ Sections 221 and tested on Section 23 ( Table 1 ) , same as used by Magerman ( 1995 ) , Collins ( 1997 ) , and Ratnaparkhi ( 1997 ) , and became a common testbed . Context after the citation: The tasks were selected so as to demonstrate the benefit of using internal structure data for learning composite structures. We have studied the effect of noun-phrase information on learning verb phrases by setting limits on the number of embedded instances, nemb in a tile. A limit of zero emulates the flat version since learning takes place from POS tags only.

CompareOrContrast

acl_arc_intent_classification:train:1309

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: An approach (also based on regulation of the succession of rule application) to the associated problem of spurious ambiguity is given in Hepple and Morrill (1989) but again, to our knowledge, there is no predictive relation between incremental combinatory processing and the kind of processing phenomena cited in the introduction. Combinatory categorial grammar does not concern itself with the capture of all (or only) the concatenatively valid combinatory schemata, but rather with incrementality, for example, on a shiftreduce design. By a result of Zielonka (1981), the Lambek calculus is not axiomatizable by any finite set of combinatory schemata, so no such combinatory presentation can constitute the logic of concatenation in the sense of Lambek calculus. Citation Sentence: An approach ( also based on regulation of the succession of rule application ) to the associated problem of spurious ambiguity is given in Hepple and Morrill ( 1989 ) but again , to our knowledge , there is no predictive relation between incremental combinatory processing and the kind of processing phenomena cited in the introduction . Context after the citation: 3. Proof Nets Lambek categorial derivations are often presented in the style of natural deduction or sequent calculus. Here we are concerned with categorial proof nets (Roorda 1991) as the fundamental structures of proof in categorial logic, in the same sense that linear proof nets were originally introduced by Girard (1987) as the fundamental structures of proof in linear logic.

Background

acl_arc_intent_classification:train:131

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The Nash arbitration plan, for example, would allow a doubly graded description whenever the product of the Values for the referent r exceeds that of all distractors (Nash 1950; cfXXX Gorniak and Roy 2003; Thorisson 1994, for other plans). Many alternative strategies are possible. For example, if the example is modified by letting width(a) = 3.1 m, making a slightly fatter than b, then b might still be the only reasonable referent of the tall fat giraffe. Citation Sentence: The Nash arbitration plan , for example , would allow a doubly graded description whenever the product of the Values for the referent r exceeds that of all distractors ( Nash 1950 ; cfXXX Gorniak and Roy 2003 ; Thorisson 1994 , for other plans ) . Context after the citation: 9.3.2 Multidimensional Adjectives (and Color). Multidimensionality can also slip in through the backdoor. Consider big, for example, when applied to 3D shapes. If there exists a formula for mapping three dimensions into one (e.g., length x width x height) then the result is one dimension (overall-size), and the algorithm of Section 4 can be applied verbatim.

Background

acl_arc_intent_classification:train:1310

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The paraphrase dictionary that we use was generated for us by Chris Callison-Burch, using the technique described in Callison-Burch (2008), which exploits a parallel corpus and methods developed for statistical machine translation. Hence we require possible paraphrases for phrases that occur in Section 00. The cover text used for our experiments consists of newspaper sentences from Section 00 of the Penn Treebank (Marcus et al., 1993). Citation Sentence: The paraphrase dictionary that we use was generated for us by Chris Callison-Burch , using the technique described in Callison-Burch ( 2008 ) , which exploits a parallel corpus and methods developed for statistical machine translation . Context after the citation: Table 1 gives summary statistics of the paraphrase dictionary and its coverage on Section 00 of the Penn Treebank. The length of the extracted n-gram phrases ranges from unigrams to five-grams. The coverage figure gives the percentage of sentences which have at least one phrase in the dictionary. The coverage is important for us because it determines the payload capacity of the embedding method described in Section 5.

Uses

acl_arc_intent_classification:train:1311

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: To name a few examples, Rohrbach et al. (2010) and Socher et al. (2013) show how semantic information from text can be used to improve zero-shot classification (i.e., classifying never-before-seen objects), and Motwani and Mooney (2012) show that verb clusters can be used to improve activity recognition in videos. The Computer Vision community has also benefited greatly from efforts to unify the two modalities. Other work on modeling the meanings of verbs using video recognition has also begun showing great promise (Mathe et al., 2008; Regneri et al., 2013). Citation Sentence: To name a few examples , Rohrbach et al. ( 2010 ) and Socher et al. ( 2013 ) show how semantic information from text can be used to improve zero-shot classification ( i.e. , classifying never-before-seen objects ) , and Motwani and Mooney ( 2012 ) show that verb clusters can be used to improve activity recognition in videos . Context after the citation:

Background

acl_arc_intent_classification:train:1312

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: This approach has now gained wide usage, as exemplified by the work of Collins (1996, 1999), Charniak (1996, 1997), Johnson (1998), Chiang (2000), and many others. The DOP model, on the other hand, was the first model (to the best of our knowledge) that proposed not to train a predefined grammar on a corpus, but to directly use corpus fragments as a grammar. Waegner 1992; Pereira and Schabes 1992). Citation Sentence: This approach has now gained wide usage , as exemplified by the work of Collins ( 1996 , 1999 ) , Charniak ( 1996 , 1997 ) , Johnson ( 1998 ) , Chiang ( 2000 ) , and many others . Context after the citation: The other innovation of DOP was to take (in principle) all corpus fragments, of any size, rather than a small subset. This innovation has not become generally adopted yet: many approaches still work either with local trees, i.e. single level rules with limited means of information percolation, or with restricted fragments, as in Stochastic Tree-Adjoining Grammar (Schabes 1992; Chiang 2000) that do not include nonlexicalized fragments. However, during the last few years we can observe a shift towards using more and larger corpus fragments with fewer restrictions. While the models of Collins (1996) and Eisner (1996) restricted the fragments to the locality of head-words, later models showed the importance of including context from higher nodes in the tree (Charniak 1997; Johnson 1998a).

Motivation

acl_arc_intent_classification:train:1313

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For example, such schema can serve as a mean to represent translation examples, or find structural correspondences for the purpose of transfer grammar learning (Menezes & Richardson, 2001), (Aramaki et al., 2001), (Watanabe et al., 2000), (Meyers et al., 2000), (Matsumoto et al., 1993), (kaji et al., 1992), and example-base machine translation EBMT3 (Sato & Nagao, 1990), (Sato, 1991), (Richardson et al., 2001), (Al-Adhaileh & Tang, 1999). Due to these limitations, instead of investigating into the synchronization of two grammars, we propose a flexible annotation schema (i.e. Synchronous Structured String-Tree Correspondence (S-SSTC)) to realize additional power and flexibility in expressing structural correspondences at the level of language sentence pairs. Similar limitations also appear in synchronous CFGs (Harbusch & Poller,1994). Citation Sentence: For example , such schema can serve as a mean to represent translation examples , or find structural correspondences for the purpose of transfer grammar learning ( Menezes & Richardson , 2001 ) , ( Aramaki et al. , 2001 ) , ( Watanabe et al. , 2000 ) , ( Meyers et al. , 2000 ) , ( Matsumoto et al. , 1993 ) , ( kaji et al. , 1992 ) , and example-base machine translation EBMT3 ( Sato & Nagao , 1990 ) , ( Sato , 1991 ) , ( Richardson et al. , 2001 ) , ( Al-Adhaileh & Tang , 1999 ) . Context after the citation:

Background

acl_arc_intent_classification:train:1314

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Increasingly, corpus researchers are tapping the Web to overcome the sparse data problem (Keller et al., 2002). In corpus linguistics building such megacorpora is beyond the scope of individual researchers, and they are not easily accessible (Kennedy, 1998: 56) unless the web is used as a corpus (Kilgarriff and Grefenstette, 2003). Due to the Zipfian nature of word frequencies, around half the word types in a corpus occur only once, so tremendous increases in corpus size are required both to ensure inclusion of essential word and phrase types and to increase the chances of multiple occurrences of a given type. Citation Sentence: Increasingly , corpus researchers are tapping the Web to overcome the sparse data problem ( Keller et al. , 2002 ) . Context after the citation: This topic generated intense interest at workshops held at the University of Heidelberg (October 2004), University of Bologna (January 2005), University of Birmingham (July 2005) and now in Trento in April 2006. In addition, the advantages of using linguistically annotated data over raw data are well documented (Mair, 2005; Granger and Rayson, 1998). As the size of a corpus increases, a near linear increase in computing power is required to annotate the text. Although processing power is steadily growing, it has already become impractical for a single computer to annotate a mega-corpus.

Background

acl_arc_intent_classification:train:1315

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: As such it resembles the parser of the grammar development system Attribute Language Engine (ALE) of (Carpenter and Penn, 1994). In contrast to Johnson and D6rre's deduction system, though, the selective magic parsing approach combines top-down and bottom-up control strategies. The proposed parser is related to the so-called Lemma Table deduction system (Johnson and D6rre, 1995) which allows the user to specify whether top-down sub-computations are to be tabled. Citation Sentence: As such it resembles the parser of the grammar development system Attribute Language Engine ( ALE ) of ( Carpenter and Penn , 1994 ) . Context after the citation: Unlike the ALE parser, though, the selective magic parser does not presuppose a phrase structure backbone and is more flexible as to which sub-computations are tabled/filtered.

CompareOrContrast

acl_arc_intent_classification:train:1316

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: de URL: http://www.sfs.nphil.uni-tuebingen.de/sfb /b4home.html 1 This is, for example, the case for all proposals working with verbal lexical entries that raise the arguments of a verbal complement (Hinrichs and Nakazawa 1989) that also use lexical rules such as the Complement Extraction Lexical Rule (Pollard and Sag 1994) or the Complement Cliticization Lexical Rule (Miller and Sag 1993) to operate on those raised elements. nphil.uni-tuebingen. email: {dm,minnen}@sfs. Citation Sentence: de URL : http://www.sfs.nphil.uni-tuebingen.de/sfb / b4home.html 1 This is , for example , the case for all proposals working with verbal lexical entries that raise the arguments of a verbal complement ( Hinrichs and Nakazawa 1989 ) that also use lexical rules such as the Complement Extraction Lexical Rule ( Pollard and Sag 1994 ) or the Complement Cliticization Lexical Rule ( Miller and Sag 1993 ) to operate on those raised elements . Context after the citation: Also an analysis treating adjunct extraction via lexical rules (van Noord and Bouma 1994) results in an infinite lexicon. Treatments of lexical rules as unary phrase structure rules also require their fully explicit specification, which entails the last problem mentioned above. In addition, computationally treating lexical rules on a par with phrase structure rules fails to take computational advantage of their specific properties. For example, the interaction of lexical rules is explored at run-time, even though the possible interaction can be determined at compile-time given the information available in the lexical rules and the base lexical entries.2 Based on the research results reported in Meurers and Minnen (1995, 1996), we propose a new computational treatment of lexical rules that overcomes these shortcomings and results in a more efficient processing of lexical rules as used in HPSG.

Background

acl_arc_intent_classification:train:1317

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Research that is more similar in goal to that outlined in this paper is Vosse (Vosse, 1992). However, as is evident from the discussion above, previous spelling research does provide an important role in suggesting productive features to include in the decision tree. Naturally, these are not appropriate comparisons for the work reported here. Citation Sentence: Research that is more similar in goal to that outlined in this paper is Vosse ( Vosse , 1992 ) . Context after the citation: Vosse uses a simple algorithm to identify three classes of unknown words: misspellings, neologisms, and names. Capitalization is his sole means of identifying names. However, capitalization information is not available in closed captions. Hence, his system would be ineffective on the closed caption domain with which we are working.

CompareOrContrast

acl_arc_intent_classification:train:1318

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Mani and MacMillan (1995) pointed out that little attention had been paid in the named-entity recognition field to the discourse properties of proper names. For handling very long documents with our method, however, the information decay strategy seems to be the right way to proceed. Instead of decaying nonlocal information, we opted for not propagating it from one document for processing of another. Citation Sentence: Mani and MacMillan ( 1995 ) pointed out that little attention had been paid in the named-entity recognition field to the discourse properties of proper names . Context after the citation: They proposed that proper names be viewed as linguistic expressions whose interpretation often depends on the discourse context, advocating text-driven processing rather than reliance on pre-existing lists. The DCA outlined in this article also uses nonlocal discourse context and does not heavily rely on pre-existing word lists. It has been applied not only to the identification of proper names, as described in this article, but also to their classification (Mikheev, Grover, and Moens 1998). Gale, Church, and Yarowsky (1992) showed that words strongly tend to exhibit only one sense in a document or discourse (“one sense per discourse”).

Uses

acl_arc_intent_classification:train:1319

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: This equivalence is doing essentially the same job as Pereira's pronoun abstraction schema in Pereira (1990). The relevant equivalence is: Before going on to more complex cases, we will also show how to deal with intrasentential anaphora, including reflexives, and binding of a pronoun by a quantifier. Citation Sentence: This equivalence is doing essentially the same job as Pereira 's pronoun abstraction schema in Pereira ( 1990 ) . Context after the citation: It will identify a pronoun with any term of type e elsewhere in the QLF, relying on the binding conditions to prevent impossible associations. 3 For this case, and for many other types of restrictions currently handled by conditions, more elegant solutions are available using the &quot;sorted&quot; and &quot;colored&quot; versions of higher-order unification developed by Michael Kohlhase and colleagues (Gardent and Kohlhase 1996b, 1997; Gardent Kohlhase, and van Leusen 1996; Gardent, Kohlhase, and Konrad 1999). We illustrate this equivalence with the relevant instantiations for the following cases (in fact, the reflexive case is done with a separate equivalence differing only in that it mentions he-self instead of he, with associated differences in binding conditions): (3) Smith admires himself.

CompareOrContrast

acl_arc_intent_classification:train:132

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Table 5 shows our mapping from publication type and MeSH headings to evidence grades based on principles defined in the Strength of Recommendations Taxonomy (Ebell et al. 2004). The potential highest level of the strength of evidence for a given citation can be identified using the Publication Type (a metadata field) and MeSH terms pertaining to the type of the clinical study. Metadata associated with most MEDLINE citations (MeSH terms) are extensively used to determine the strength of evidence and in our EBM citation scoring algorithm (Section 6). Citation Sentence: Table 5 shows our mapping from publication type and MeSH headings to evidence grades based on principles defined in the Strength of Recommendations Taxonomy ( Ebell et al. 2004 ) . Context after the citation: 5.10 Sample Output A complete example of our knowledge extractors working in unison is shown in Figure 2, which contains an abstract retrieved in response to the following question: “In children with an acute febrile illness, what is the efficacy of single-medication therapy with acetaminophen or ibuprofen in reducing fever?” (Kauffman, Sawyer, and Scheinbaum 1992). Febrile illness is the only concept mapped to DISORDER, and hence is identified as the primary problem.

Uses

acl_arc_intent_classification:train:1320

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: conclusion Context before the citation: While we have observed reasonable results with both G2 and Fisher's exact test, we have not yet discussed how these results compare to the results that can be obtained with a technique commonly used in corpus linguistics based on the mutual information (MI) measure (Church and Hanks 1990): For the higherfrequency words, Fisher's exact test leads to a slightly better recall with the same precision scores (0.31 for both tests). Note that this technique is optimal for the extraction of the lowest-frequency words, leading to identical performance for G2 and Fisher's exact test for these words. Citation Sentence: While we have observed reasonable results with both G2 and Fisher 's exact test , we have not yet discussed how these results compare to the results that can be obtained with a technique commonly used in corpus linguistics based on the mutual information ( MI ) measure ( Church and Hanks 1990 ) : Context after the citation: In (4), y is the seed term and x a potential target word. A high MI score for a given target word suggests an association between this target and the seed term. Or perhaps more precisely, a low MI score suggests a dissociation between target and seed word (Manning and Schiltze 1999). To compute recall, precision, and F, we require a cut-off value.

Uses

acl_arc_intent_classification:train:1321

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Following Ponzetto and Strube (2006), we consider an anaphoric reference, NPi, correctly resolved if NPi and its closest antecedent are in the same coreference chain in the resulting partition. We report performance in terms of two metrics: (1) the Fmeasure score as computed by the commonly-used MUC scorer (Vilain et al., 1995), and (2) the accuracy on the anaphoric references, computed as the fraction of anaphoric references correctly resolved. As in SC induction, we use the ACE Phase 2 coreference corpus for evaluation purposes, acquiring the coreference classifiers on the 422 training texts and evaluating their output on the 97 test texts. Citation Sentence: Following Ponzetto and Strube ( 2006 ) , we consider an anaphoric reference , NPi , correctly resolved if NPi and its closest antecedent are in the same coreference chain in the resulting partition . Context after the citation: In all of our experiments, we use NPs automatically extracted by an in-house NP chunker and IdentiFinder.

Uses

acl_arc_intent_classification:train:1322

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: From an IR view, a lot of specialized research has already been carried out for medical applications, with emphasis on the lexico-semantic aspects of dederivation and decomposition (Pacak et al., 1980; Norton and Pacak, 1983; Wolff, 1984; Wingert, 1985; Dujols et al., 1991; Baud et al., 1998). This is particularly true for the medical domain. When it comes to a broader scope of morphological analysis, including derivation and composition, even for the English language only restricted, domain-specific algorithms exist. Citation Sentence: From an IR view , a lot of specialized research has already been carried out for medical applications , with emphasis on the lexico-semantic aspects of dederivation and decomposition ( Pacak et al. , 1980 ; Norton and Pacak , 1983 ; Wolff , 1984 ; Wingert , 1985 ; Dujols et al. , 1991 ; Baud et al. , 1998 ) . Context after the citation: While one may argue that single-word compounds are quite rare in English (which is not the case in the medical domain either), this is certainly not true for German and other basically agglutinative languages known for excessive single-word nominal compounding. This problem becomes even more pressing for technical sublanguages, such as medical German (e.g., ‘Blut druck mess gerdt’ translates to ‘device for measuring blood pressure’). The problem one faces from an IR point of view is that besides fairly standardized nominal compounds, which already form a regular part of the sublanguage proper, a myriad of ad hoc compounds are formed on the fly which cannot be anticipated when formulating a retrieval query though they appear in relevant documents. Hence, enumerating morphological variants in a semi-automatically generated lexicon, such as proposed for French (Zweigenbaum et al., 2001), turns out to be infeasible, at least for German and related languages.

Background

acl_arc_intent_classification:train:1323

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: The Gsearch system (Corley et al., 2001) also selects sentences by syntactic criteria from large on-line text collections. A new collection does not become available for analysis until the LSE completes the annotation process, which may entail significant delay with multiple users of the LSE server. The second method pushes the new collection onto a queue for the LSE annotator to analyse. Citation Sentence: The Gsearch system ( Corley et al. , 2001 ) also selects sentences by syntactic criteria from large on-line text collections . Context after the citation: Gsearch annotates corpora with a fast chart parser to obviate the need for corpora with pre-existing syntactic mark-up. In contrast, the Sketch Engine system to assist lexicographers to construct dictionary entries requires large pre-annotated corpora. A word sketch is an automatic one-page corpus-derived summary of a word's grammatical and collocational behaviour. Word Sketches were first used to prepare the Macmillan English Dictionary for Advanced Learners (2002, edited by Michael Rundell).

Background

acl_arc_intent_classification:train:1324

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Thus, over the past few years, along with advances in the use of learning and statistical methods for acquisition of full parsers (Collins, 1997; Charniak, 1997a; Charniak, 1997b; Ratnaparkhi, 1997), significant progress has been made on the use of statistical learning methods to recognize shallow parsing patterns syntactic phrases or words that participate in a syntactic relationship (Church, 1988; Ramshaw and Marcus, 1995; Argamon et al., 1998; Cardie and Pierce, 1998; Munoz et al., 1999; Punyakanok and Roth, 2001; Buchholz et al., 1999; Tjong Kim Sang and Buchholz, 2000). While earlier work in this direction concentrated on manual construction of rules, most of the recent work has been motivated by the observation that shallow syntactic information can be extracted using local information by examining the pattern itself, its nearby context and the local part-of-speech information. to ] [NP only $ 1.8 billion ] [PP in ] [NP September] . Citation Sentence: Thus , over the past few years , along with advances in the use of learning and statistical methods for acquisition of full parsers ( Collins , 1997 ; Charniak , 1997a ; Charniak , 1997b ; Ratnaparkhi , 1997 ) , significant progress has been made on the use of statistical learning methods to recognize shallow parsing patterns syntactic phrases or words that participate in a syntactic relationship ( Church , 1988 ; Ramshaw and Marcus , 1995 ; Argamon et al. , 1998 ; Cardie and Pierce , 1998 ; Munoz et al. , 1999 ; Punyakanok and Roth , 2001 ; Buchholz et al. , 1999 ; Tjong Kim Sang and Buchholz , 2000 ) . Context after the citation: Research on shallow parsing was inspired by psycholinguistics arguments (Gee and Grosjean, 1983) that suggest that in many scenarios (e.g., conversational) full parsing is not a realistic strategy for sentence processing and analysis, and was further motivated by several arguments from a natural language engineering viewpoint. First, it has been noted that in many natural language applications it is sufficient to use shallow parsing information; information such as noun phrases (NPs) and other syntactic sequences have been found useful in many large-scale language processing applications including information extraction and text summarization (Grishman, 1995; Appelt et al., 1993). Second, while training a full parser requires a collection of fully parsed sentences as training corpus, it is possible to train a shallow parser incrementally. If all that is available is a collection of sentences annotated for NPs, it can be used to produce this level of analysis.

Background

acl_arc_intent_classification:train:1325

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: See also the work of Byrd and Chodorow (1985), which considers computer-based pronunciation by analogy but does not mention the possible application to text-to-speech synthesis. It was first proposed for ITS applications over a decade ago by Dedina and Nusbaum (1986, 1991). Pronunciation by analogy (PbA) is a data-driven technique for the automatic phonemization of text, originally proposed as a model of reading, e.g., by Glushko (1979) and Kay and Marcel (1981). Citation Sentence: See also the work of Byrd and Chodorow ( 1985 ) , which considers computer-based pronunciation by analogy but does not mention the possible application to text-to-speech synthesis . Context after the citation: As detailed by Damper (1995) and Damper and Eastmond (1997), PbA shares many similarities with the artificial intelligence paradigms variously called case-based, memory-based, or instance-based reasoning as applied to letter-to-phoneme conversion (Stanfill and Waltz 1986; Lehnert 1987; Stanfill 1987, 1988; Golding 1991; Golding and Rosenbloom 1991; van den Bosch and Daelemans 1993). PbA exploits the phonological knowledge implicitly contained in a dictionary of words and their corresponding pronunciations. The underlying idea is that a pronunciation for an unknown word is derived by matching substrings of the input to substrings of known, lexical words, hypothesizing a partial pronunciation for each matched substring from the phonological knowledge, and assembling the partial pronunciations.

Background

acl_arc_intent_classification:train:1326

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The resulting list of POS-tagged lemmas is weighted using the SMART ‘ltc’8 tf.idf-weighting scheme (Salton, 1989). The three preprocessing steps (tokenization, POS-tagging, lemmatization) are performed using TreeTagger (Schmid, 1995). Due to the special structure of presentations, this corpus will be particularly demanding with respect to the required preprocessing components of an information retrieval system. Citation Sentence: The resulting list of POS-tagged lemmas is weighted using the SMART ` ltc ' 8 tf.idf-weighting scheme ( Salton , 1989 ) . Context after the citation: We implemented a set of filters for word pairs. One group of filters removed unwanted word pairs. Word pairs are filtered if they contain at least one word that a) has less than three letters b) contains only uppercase letters (mostly acronyms) or c) can be found in a stoplist. Another filter enforced a specified fraction of combinations of nouns (N), verbs (V) and adjectives (A) to be present in the result set.

Uses

acl_arc_intent_classification:train:1327

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The psycholinguistic studies of Martin (1970), Allen (1975), Hillinger et al. (1976), Grosjean et al. (1979), Dommergues and Grosjean (1983), and Gee and Grosjean (1983), responding to the idea of readjusted syntax as the source of prosodic phrasing, show that grammatical structure, even if readjusted, is not in itself a reliable predictor of prosodic phrasing: mismatches between syntax and prosody occur often and systematically, and can be related to specific nonsyntactic factors such as length and word frequency. Citation Sentence: The psycholinguistic studies of Martin ( 1970 ) , Allen ( 1975 ) , Hillinger et al. ( 1976 ) , Grosjean et al. ( 1979 ) , Dommergues and Grosjean ( 1983 ) , and Gee and Grosjean ( 1983 ) , responding to the idea of readjusted syntax as the source of prosodic phrasing , show that grammatical structure , even if readjusted , is not in itself a reliable predictor of prosodic phrasing : mismatches between syntax and prosody occur often and systematically , and can be related to specific nonsyntactic factors such as length and word frequency . Context after the citation: For example, although prosodic boundaries between subject and verb do occur, there also exist prosodic patterns in which the boundary comes between the verb and object, i.e., the data reveal both X(VY) and (XV)Y groupings. Grosjean et al. (1979) claims that such mismatches are due for the most part to constituent length, which interacts with grammatical structure and, in some cases, overrides it. Thus syntactic and prosodic structure match when the major constituents of a sentence are roughly equal in length; for example, the main prosodic phrase break corresponds to the subject-predicate boundary in Waiters who remember well II serve orders correctly. Discrepancies in length throw constituents off balance, and so prosodic phrasing will cross constituent boundaries in order to give the phrases similar lengths; this is the case in Chickens were eating II the remaining green vegetables, where the subject-predicate boundary finds no prosodic correspondent.4 The most explicit version of this approach is the analysis presented in Gee and Grosjean (1983) (henceforth G&G).

Background

acl_arc_intent_classification:train:1328

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Some methods are based on likelihood (Och and Ney, 2002; Blunsom et al., 2008), error rate (Och, 2003; Zhao and Chen, 2009; Pauls et al., 2009; Galley and Quirk, 2011), margin (Watanabe et al., 2007; Chiang et al., 2008) and ranking (Hopkins and May, 2011), and among which minimum error rate training (MERT) (Och, 2003) is the most popular one. h is a feature vector which is scaled by a weight W. Parameter estimation is one of the most important components in SMT, and various training methods have been proposed to tune W. where f and e (e') are source and target sentences, respectively. Citation Sentence: Some methods are based on likelihood ( Och and Ney , 2002 ; Blunsom et al. , 2008 ) , error rate ( Och , 2003 ; Zhao and Chen , 2009 ; Pauls et al. , 2009 ; Galley and Quirk , 2011 ) , margin ( Watanabe et al. , 2007 ; Chiang et al. , 2008 ) and ranking ( Hopkins and May , 2011 ) , and among which minimum error rate training ( MERT ) ( Och , 2003 ) is the most popular one . Context after the citation: All these training methods follow the same pipeline: they train only a single weight on a given development set, and then use it to translate all the sentences in a test set. We call them a global training method. One of its advantages is that it allows us to train a single weight offline and thereby it is efficient. However, due to the diversity and uneven distribution of source sentences(Li et al., 2010), there are some shortcomings in this pipeline.

Background

acl_arc_intent_classification:train:1329

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: Finally, we experiment with a method for combining phrase tables proposed in (Nakov and Ng, 2009; Nakov and Ng, 2012). Sophisticated phrase table combination. In order to counter-balance this, we repeat the smaller Indonesian–English bi-text enough times so that we can make the number of sentences it contains roughly the same as for the “Indonesian”–English bi-text; then we concatenate the two bi-texts and we train an SMT system on the resulting bi-text. Citation Sentence: Finally , we experiment with a method for combining phrase tables proposed in ( Nakov and Ng , 2009 ; Nakov and Ng , 2012 ) . Context after the citation: The first phrase table is extracted from word alignments for the balanced concatenation with repetitions, which are then truncated so that they are kept for only one copy of the Indonesian–English bi-text. The second table is built from the simple concatenation. The two tables are then merged as follows: all phrase pairs from the first one are retained, and to them are added those phrase pairs from the second one that are not present in the first one. Each phrase pair retains its original scores, which are further augmented with 1–3 additional feature scores indicating its origin: the first/second/third feature is 1 if the pair came from the first/second/both table(s), and 0 otherwise.

Uses

acl_arc_intent_classification:train:133

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Numerous previous pseudodisambiguation evaluations only include arguments that occur between 30 and 3000 times (Erk, 2007; Keller and Lapata, 2003; Rooth et al., 1999). We set the MI-threshold, T, to be 0, and the negative-to-positive ratio, K, to be 2. Passive subjects (the car was bought) were converted to objects (bought car). Citation Sentence: Numerous previous pseudodisambiguation evaluations only include arguments that occur between 30 and 3000 times ( Erk , 2007 ; Keller and Lapata , 2003 ; Rooth et al. , 1999 ) . Context after the citation: Presumably the lower bound is to help ensure the negative argument is unobserved because it is unsuitable, not because of data sparseness. We wish to use our model on arguments of any frequency, including those that never occurred in the training corpus (and therefore have empty cooccurrence features (Section 3.3.1)). We proceed as follows: first, we exclude pairs whenever the noun occurs less than 3 times in our corpus, removing many misspellings and other noun noise. Next, we omit verb co-occurrence features for nouns that occur less than 10 times, and instead fire a low-count feature.

CompareOrContrast

acl_arc_intent_classification:train:1330

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: • cross-language information retrieval (e.g., McCarley 1999), • multilingual document filtering (e.g., Oard 1997), • computer-assisted language learning (e.g., Nerbonne et al. 1997), • certain machine-assisted translation tools (e.g., Macklovitch 1994; Melamed 1996a), • concordancing for bilingual lexicography (e.g., Catizone, Russell, and Warwick 1989; Gale and Church 1991), Applications where word order is not essential include Empirically estimated models of translational equivalence among word types can play a central role in both kinds of applications. Citation Sentence: • cross-language information retrieval ( e.g. , McCarley 1999 ) , • multilingual document filtering ( e.g. , Oard 1997 ) , • computer-assisted language learning ( e.g. , Nerbonne et al. 1997 ) , • certain machine-assisted translation tools ( e.g. , Macklovitch 1994 ; Melamed 1996a ) , • concordancing for bilingual lexicography ( e.g. , Catizone , Russell , and Warwick 1989 ; Gale and Church 1991 ) , Context after the citation: • corpus linguistics (e.g., Svartvik 1992), • &quot;crummy&quot; machine translation (e.g., Church and Hovy 1992; Resnik 1997). For these applications, empirically estimated models have a number of advantages over handcrafted models such as on-line versions of bilingual dictionaries. Two of the advantages are the possibility of better coverage and the possibility of frequent updates by nonexpert users to keep up with rapidly evolving vocabularies. A third advantage is that statistical models can provide more accurate information about the relative importance of different translations.

Background

acl_arc_intent_classification:train:1331

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: One would think that the type information ti, which is more specific than that 16 A linguistic example based on the signature given by Pollard and Sag (1994) would be a lexical rule deriving predicative signs from nonpredicative ones, i.e., changing the PRD value of substantive signs from — to -F, much like the lexical rule for NPs given by Pollard and Sag (1994, p. 360, fn. With respect to frame specification this means that there can be lexical entries, such as the one in Figure 7, for which we need to make sure that tj as the value of c gets transferred.' For example, the lexical rule 1 of Figure 6 applies to word objects with tj as their c value and to those having t2 as their c value. Citation Sentence: One would think that the type information ti , which is more specific than that 16 A linguistic example based on the signature given by Pollard and Sag ( 1994 ) would be a lexical rule deriving predicative signs from nonpredicative ones , i.e. , changing the PRD value of substantive signs from -- to - F , much like the lexical rule for NPs given by Pollard and Sag ( 1994 , p. 360 , fn . Context after the citation: 20). In such a Predicative Lexical Rule (which we only note as an example and not as a linguistic proposal) the subtype of the head object undergoing the rule as well as the value of the features only appropriate for the subtypes of substantive either is lost or must be specified by a separate rule for each of the subtypes. Definition of the frame predicate for lexical rule 1. given in the output of the lexical rule, can be specified on the out-specification of the lexical rule if the specification of c is transferred as a whole (via structure sharing of the value of c).

Background

acl_arc_intent_classification:train:1332

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: • language learning (Green 1979; Mori and Moeser 1983; Morgan, Meier, and Newport 1989) • monolingual grammar induction (Juola 1998) • grammar optimization (Juola 1994) • insights into universal grammar (Juola 1998) • machine translation (Juola 1994, 1997; Veale and Way 1997; Gough, Way, and Hearne 2002) The marker hypothesis has been used for a number of different language-related tasks, including The marker hypothesis is arguably universal in presuming that concepts and structures like these have similar morphological or structural marking in all languages. Citation Sentence: • language learning ( Green 1979 ; Mori and Moeser 1983 ; Morgan , Meier , and Newport 1989 ) • monolingual grammar induction ( Juola 1998 ) • grammar optimization ( Juola 1994 ) • insights into universal grammar ( Juola 1998 ) • machine translation ( Juola 1994 , 1997 ; Veale and Way 1997 ; Gough , Way , and Hearne 2002 ) Context after the citation: With respect to translation, a potential problem in using the marker hypothesis is that some languages do not have marker words such as articles, for instance. Green’s (1979) work showed that artificial languages, both with and without specific marker words, may be learned more accurately and quickly if such psycholinguistic cues exist. The research of Mori and Moeser (1983) showed a similar effect due to case marking on pseudowords in such artificial languages, and Morgan, Meier, and Newport (1989) demonstrated that languages that do not permit pronouns as substitutes for phrases also provide evidence in favor of the marker hypothesis.

Background

acl_arc_intent_classification:train:1333

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Notable early papers on graph-based semisupervised learning include Blum and Chawla (2001), Bansal et al. (2002), Kondor and Lafferty (2002), and Joachims (2003). inter-document references in the form of hyperlinks (Agrawal et al., 2003). Previous sentiment-analysis work in different domains has considered inter-document similarity (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005; Goldberg and Zhu, 2006) or explicit Citation Sentence: Notable early papers on graph-based semisupervised learning include Blum and Chawla ( 2001 ) , Bansal et al. ( 2002 ) , Kondor and Lafferty ( 2002 ) , and Joachims ( 2003 ) . Context after the citation: Zhu (2005) maintains a survey of this area. Recently, several alternative, often quite sophisticated approaches to collective classification have been proposed (Neville and Jensen, 2000; Lafferty et al., 2001; Getoor et al., 2002; Taskar et al., 2002; Taskar et al., 2003; Taskar et al., 2004; McCallum and Wellner, 2004). It would be interesting to investigate the application of such methods to our problem. However, we also believe that our approach has important advantages, including conceptual simplicity and the fact that it is based on an underlying optimization problem that is provably and in practice easy to solve.

Background

acl_arc_intent_classification:train:1334

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: As a result, researchers have re-adopted the once-popular knowledge-rich approach, investigating a variety of semantic knowledge sources for common noun resolution, such as the semantic relations between two NPs (e.g., Ji et al. (2005)), their semantic similarity as computed using WordNet (e.g., Poesio et al. (2004)) or Wikipedia (Ponzetto and Strube, 2006), and the contextual role played by an NP (see Bean and Riloff (2004)). In fact, semantics plays a crucially important role in the resolution of common NPs, allowing us to identify the coreference relation between two lexically dissimilar common nouns (e.g., talks and negotiations) and to eliminate George W. Bush from the list of candidate antecedents of the city, for instance. While these approaches have been reasonably successful (see Mitkov (2002)), Kehler et al. (2004) speculate that deeper linguistic knowledge needs to be made available to resolvers in order to reach the next level of performance. Citation Sentence: As a result , researchers have re-adopted the once-popular knowledge-rich approach , investigating a variety of semantic knowledge sources for common noun resolution , such as the semantic relations between two NPs ( e.g. , Ji et al. ( 2005 ) ) , their semantic similarity as computed using WordNet ( e.g. , Poesio et al. ( 2004 ) ) or Wikipedia ( Ponzetto and Strube , 2006 ) , and the contextual role played by an NP ( see Bean and Riloff ( 2004 ) ) . Context after the citation: Another type of semantic knowledge that has been employed by coreference resolvers is the semantic class (SC) of an NP, which can be used to disallow coreference between semantically incompatible NPs. However, learning-based resolvers have not been able to benefit from having an SC agreement feature, presumably because the method used to compute the SC of an NP is too simplistic: while the SC of a proper name is computed fairly accurately using a named entity (NE) recognizer, many resolvers simply assign to a common noun the first (i.e., most frequent) WordNet sense as its SC (e.g., Soon et al. (2001), Markert and Nissim (2005)). It is not easy to measure the accuracy of this heuristic, but the fact that the SC agreement feature is not used by Soon et al.’s decision tree coreference classifier seems to suggest that the SC values of the NPs are not computed accurately by this first-sense heuristic. Motivated in part by this observation, we examine whether automatically induced semantic class knowledge can improve the performance of a learning-based coreference resolver, reporting evaluation results on the commonly-used ACE corefer-

Background

acl_arc_intent_classification:train:1335

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: An example of psycholinguistically oriented research work can be found in Bond and Hayes (1983). Although these dictates are fairly clear, the underlying notion of topic is not. In these sources, a paragraph is notionally defined as something like a series of sentences that develop one single topic, and rules are laid down for the construction of an ideal (or at least an acceptable) paragraph. Citation Sentence: An example of psycholinguistically oriented research work can be found in Bond and Hayes ( 1983 ) . Context after the citation: These authors take the position that a paragraph is a psychologically real unit of discourse, and, in fact, a formal grammatical unit. Bond and Hayes found three major formal devices that are used, by readers, to identify a paragraph: (1) the repetition of content words (nouns, verbs, adjectives, adverbs); (2) pronoun reference; and (3) paragraph length, as determined by spatial and/or sentence-count information. Other psycholing-uistic studies that confirm the validity of paragraph units can be found in Black and Bower (1979) and Haberlandt et al. (1980). The textualist approach to paragraph analysis is exemplified by E. J. Crothers.

Background

acl_arc_intent_classification:train:1336

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: Alternatively, we may think of user-centered comparative studies (Hersh et al., 1995). This will become even more interesting when mappings of our synonym identifiers to a large medical thesaurus (MeSH, (NLM, 2001)) are incorporated into our system. It would be interesting to evaluate the retrieval effectiveness (in terms of precision and recall) of different versions of the synonym class indexing approach in those cases where retrieval using word or subword indexes fails due to a complete mismatch between query and documents. Citation Sentence: Alternatively , we may think of user-centered comparative studies ( Hersh et al. , 1995 ) . Context after the citation:

FutureWork

acl_arc_intent_classification:train:1337

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Crothers (1979, p. 112), for example, bemoans the fact that his &quot;theory lacks a world knowledge component, a mental 'encyclopedia,' which could be invoked to generate inferences... &quot;. It is the lack of exactly this notion of referential level that has stood in the way of other linguists who have been interested in the paragraph as a unit. This type of consultation uses existing natural language texts as a referential level for processing purposes. Citation Sentence: Crothers ( 1979 , p. 112 ) , for example , bemoans the fact that his `` theory lacks a world knowledge component , a mental ` encyclopedia , ' which could be invoked to generate inferences ... '' . Context after the citation: With respect to that independent source of knowledge, our main contributions are two. First, we identify its possible structure (a collection of partially ordered theories) and make formal the choice of a most plausible interpretation. In other words, we recognize it as a separate logical level—the referential level. Second, we suggest that natural language reference works, like dictionaries and thesauri, can quite often fill the role of the referential level.

Background

acl_arc_intent_classification:train:1338

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: However, more recent work (Cahill et al. 2002; Cahill, McCarthy, et al. 2004) has presented efforts in evolving and scaling up annotation techniques to the Penn-II Treebank (Marcus et al. 1994), containing more than 1,000,000 words and 49,000 sentences. Most of the early work on automatic f-structure annotation (e.g., van Genabith, Way, and Sadler 1999; Frank 2000; Sadler, van Genabith, and Way 2000) was applied only to small data sets (fewer than 200 sentences) and was largely proof of concept. F-structures are attribute–value structures which represent abstract syntactic information, approximating to basic predicate–argument–modifier structures. Citation Sentence: However , more recent work ( Cahill et al. 2002 ; Cahill , McCarthy , et al. 2004 ) has presented efforts in evolving and scaling up annotation techniques to the Penn-II Treebank ( Marcus et al. 1994 ) , containing more than 1,000,000 words and 49,000 sentences . Context after the citation: We utilize the automatic annotation algorithm of Cahill et al. (2002) and Cahill, McCarthy, et al. (2004) to derive a version of Penn-II in which each node in each tree is annotated with LFG functional annotations in the form of attribute-value structure equations. The algorithm uses categorial, configurational, local head, and Penn-II functional and trace information. The annotation procedure is dependent on locating the head daughter, for which an amended version of Magerman (1994) is used. The head is annotated with the LFG equation r=J..

Background

acl_arc_intent_classification:train:1339

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Our work is more similar to NLG work that concentrates on structural constraints such as generative poetry (Greene et al., 2010) (Colton et al., 2012) (Jiang and Zhou, 2008) or song lyrics (Wu et al., 2013) (Ramakrishnan A et al., 2009), where specified meter or rhyme schemes are enforced. The majority of NLG focuses on the satisfaction of a communicative goal, with examples such as Belz (2008) which produces weather reports from structured data or Mitchell et al. (2013) which generates descriptions of objects from images. Citation Sentence: Our work is more similar to NLG work that concentrates on structural constraints such as generative poetry ( Greene et al. , 2010 ) ( Colton et al. , 2012 ) ( Jiang and Zhou , 2008 ) or song lyrics ( Wu et al. , 2013 ) ( Ramakrishnan A et al. , 2009 ) , where specified meter or rhyme schemes are enforced . Context after the citation: In these papers soft semantic goals are sometimes also introduced that seek responses to previous lines of poetry or lyric. Computational creativity is another subfield of NLG that often does not fix an a priori meaning in its output. Examples such as ¨Ozbal et al. (2013) and Valitutti et al. (2013) use template filling techniques guided by quantified notions of humor or how catchy a phrase is. Our motivation for generation of material for language education exists in work such as Sumita et al. (2005) and Mostow and Jang (2012), which deal with automatic generation of classic fill in the blank questions.

CompareOrContrast

acl_arc_intent_classification:train:134

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The current system learns finite state flowcharts whereas typical learning systems usually acquire coefficient values as in Minsky and Papert (1969), assertional statements as in Michalski (1980), or semantic nets as in Winston (1975). The VNLCE processor may be considered to be a learning system of the tradition described, for example, in Michalski et al. (1984). It self activates to bias recognition toward historically observed patterns but is not otherwise observable. Citation Sentence: The current system learns finite state flowcharts whereas typical learning systems usually acquire coefficient values as in Minsky and Papert ( 1969 ) , assertional statements as in Michalski ( 1980 ) , or semantic nets as in Winston ( 1975 ) . Context after the citation: That is, the current system learns procedures rather than data structures. There is some literature on procedure acquisition such as the LISP synthesis work described in Biermann et al. (1984) and the PROLOG synthesis method of Shapiro (1982). However, the latter methodologies have not been applied to dialogue acquisition.

CompareOrContrast

acl_arc_intent_classification:train:1340

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: It also shows the structural identity to bilingual grammars as used in (Wu, 1996). In section 2.4 this property is used to convert a bilingual corpus into a set of translation patterns which are formulated in terms of words and category labels. We write the labels at first position as these translations patterns can be used in the reverse direction, i.e. from target language to source language. Citation Sentence: It also shows the structural identity to bilingual grammars as used in ( Wu , 1996 ) . Context after the citation:

Uses

acl_arc_intent_classification:train:1341

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Relationships between the unlabeled items Carvalho and Cohen (2005) consider sequential relations between different types of emails (e.g., between requests and satisfactions thereof) to classify messages, and thus also explicitly exploit the structure of conversations. We currently do not have an efficient means to encode disagreement information as hard constraints; we plan to investigate incorporating such information in future work. Also relevant is work on the general problems of dialog-act tagging (Stolcke et al., 2000), citation analysis (Lehnert et al., 1990), and computational rhetorical analysis (Marcu, 2000; Teufel and Moens, 2002). Citation Sentence: Relationships between the unlabeled items Carvalho and Cohen ( 2005 ) consider sequential relations between different types of emails ( e.g. , between requests and satisfactions thereof ) to classify messages , and thus also explicitly exploit the structure of conversations . Context after the citation: Previous sentiment-analysis work in different domains has considered inter-document similarity (Agarwal and Bhattacharyya, 2005; Pang and Lee, 2005; Goldberg and Zhu, 2006) or explicit inter-document references in the form of hyperlinks (Agrawal et al., 2003). Notable early papers on graph-based semisupervised learning include Blum and Chawla (2001), Bansal et al. (2002), Kondor and Lafferty (2002), and Joachims (2003). Zhu (2005) maintains a survey of this area.

Background

acl_arc_intent_classification:train:1342

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: 100000 word stems of German (Neumann et al., 1997). We are using a lexicon of approx. MorphAna: Morphological Analysis provided by smes yields the word stems of nouns, verbs and adjectives, as well as the full forms of unknown words. Citation Sentence: 100000 word stems of German ( Neumann et al. , 1997 ) . Context after the citation: STP-Heuristics: Shallow parsing techniques are used to heuristically identify sentences containing relevant information. The e-mails usually contain questions and/or descriptions of problems. The manual analysis of a sample of the data suggested some linguistic constructions frequently used to express the problem. We expected that content words in these constructions should be particularly influential to the categorization.

Uses

acl_arc_intent_classification:train:1343

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The computational treatment of lexical rules proposed can be seen as an extension to the principled method discussed by Gotz and Meurers (1995, 1996, 1997b) for encoding the main building block of HPSG grammars—the implicative constraints—as a logic program. The resulting encoding allows the execution of lexical rules on-the-fly, i.e., coroutined with other constraints at some time after lexical lookup. Definite relations are a convenient way of encoding the interaction of lexical rules, as they readily support various program transformations to improve the encoding: We show that the definite relations produced by the compiler can be refined by program transformation techniques to increase efficiency. Citation Sentence: The computational treatment of lexical rules proposed can be seen as an extension to the principled method discussed by Gotz and Meurers ( 1995 , 1996 , 1997b ) for encoding the main building block of HPSG grammars -- the implicative constraints -- as a logic program . Context after the citation: The structure of the paper is as follows: We start with a brief introduction of the formal background on which our approach is based in Section 2. We then describe (Section 3) how lexical rules and their interaction can be encoded in a definite clause encoding that expresses systematic covariation in lexical entries. We show how the encoding of lexical rule interaction can be improved by specializing it for different word classes and, in Section 4, focus on an improvement of this specialization step by means of program transformation techniques. A further improvement relevant to on-the-fly application of lexical rules is presented in Section 5.

Extends

acl_arc_intent_classification:train:1344

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Identical to the standard perceptron proof, e.g., Collins (2002), by inserting in loss-separability for normal separability. Proof. If training is run indefinitely, then m < R2 γ2. Citation Sentence: Identical to the standard perceptron proof , e.g. , Collins ( 2002 ) , by inserting in loss-separability for normal separability . Context after the citation: Like the original perceptron theorem, this implies that the algorithm will converge. However, unlike the original theorem, it does not imply that it will converge to a parameter vector θ such that for all (x, y) E D, if yˆ = arg maxg θ ·`F(ˆy) then L(ˆy, y) = 0. Even if we assume for every x there exists an output with zero loss, Theorem 1 still makes no guarantees. Consider a training set with one instance (x, y).

Background

acl_arc_intent_classification:train:1345

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: The goal of the JAVOX toolkit is to speech-enable traditional desktop applications — this is similar to the goals of the MELISSA project (Schmidt et al., 1998). Previous systems to assist in the development of spoken-language systems (SLSs) have focused on building stand-alone, customized applications, such as (Sutton et al., 1996) and (Pargellis et al., 1999). JAVOX has been successfully demonstrated with several GUI-based applications. Citation Sentence: The goal of the JAVOX toolkit is to speech-enable traditional desktop applications -- this is similar to the goals of the MELISSA project ( Schmidt et al. , 1998 ) . Context after the citation: It is intended to both speed the development of SLSs and to localize the speech-specific code within the application. JAVOX allows developers to add speech interfaces to applications at the end of the development process; SLSs no longer need to be built from the ground up. We will briefly present an overview of how JAVOX works, including its major modules. First, we 'Java and Java Speech are registered trademarks of Sun Microsystems, Inc. will examine TRANSLATOR, the implemented JAVOX natural language processing (NLP) component; its role is to translate from natural language utterances to the JAVOX Scripting Language (JSL).

CompareOrContrast

acl_arc_intent_classification:train:1346

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: experiments Context before the citation: The simplest strategy for ordering adjectives is what Shaw and Hatzivassiloglou (1999) call the direct evidence method. Citation Sentence: The simplest strategy for ordering adjectives is what Shaw and Hatzivassiloglou ( 1999 ) call the direct evidence method . Context after the citation: To order the pair {a,b}, count how many times the ordered sequences (a,b) and (b,a) appear in the training data and output the pair in the order which occurred more often. This method has the advantage of being conceptually very simple, easy to implement, and highly accurate for pairs of adjectives which actually appear in the training data. Applying this method to the adjectives sequences taken from the BNC yields better than 98% accuracy for pairs that occurred in the training data. However, since as we have seen, the majority of pairs occur only once, the overall accuracy of this method is 59.72%, only slightly better than random guessing.

Background

acl_arc_intent_classification:train:1347

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: The numeral (whether it is implicit, as in (3), or explicit) can be construed as allowing the reader to draw inferences about the standards employed (Kyburg and Morreau 2000; DeVault and Stone 2004): (3), for example, implies a standard that counts 10 cm as large and 8 cm as not large. Clearly, what it takes for the adjective to be applicable has not been cast in stone, but is open to fiat: the speaker may decide that 8 cm is enough, or the speaker may set the standards higher (cfXXX, Kennedy 1999). (3) The large mouse (= the one whose size is 10 cm) (4) The two large mice (= the two whose sizes are 8 and 10 cm) Citation Sentence: The numeral ( whether it is implicit , as in ( 3 ) , or explicit ) can be construed as allowing the reader to draw inferences about the standards employed ( Kyburg and Morreau 2000 ; DeVault and Stone 2004 ) : ( 3 ) , for example , implies a standard that counts 10 cm as large and 8 cm as not large . Context after the citation: Our own proposal will abstract away from the effects of linguistic context. We shall ask how noun phrases like the ones in (3) and (4) can be generated, without asking how they constrain, and are constrained by, other uses of large and related words. This will allow us to make the following simplification: In a definite description that expresses only properties that are needed for singling out a referent, we take the base form of 2 The reader is asked to focus on any reasonable size measurement, for example, the maximal horizontal or vertical distance, or some combination of dimensions (Kamp 1975; also Section 8.1 of the present article).

Background

acl_arc_intent_classification:train:1348

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: related work Context before the citation: Such systems extract information from some types of syntactic units (clauses in (Fillmore and Atkins, 1998; Gildea and Jurafsky, 2002; Hull and Gomez, 1996); noun phrases in (Hull and Gomez, 1996; Rosario et al., 2002)). In other methods, lexical resources are specifically tailored to meet the requirements of the domain (Rosario and Hearst, 2001) or the system (Gomez, 1998). Some methods of semantic relation analysis rely on predefined templates filled with information from processed texts (Baker et al., 1998). Citation Sentence: Such systems extract information from some types of syntactic units ( clauses in ( Fillmore and Atkins , 1998 ; Gildea and Jurafsky , 2002 ; Hull and Gomez , 1996 ) ; noun phrases in ( Hull and Gomez , 1996 ; Rosario et al. , 2002 ) ) . Context after the citation: Lists of semantic relations are designed to capture salient domain information. In the Rapid Knowledge Formation Project (RKF) a support system was developed for domain experts. It helps them build complex knowledge bases by combining components: events, entities and modifiers (Clark and Porter, 1997). The system’s interface facilitates the expert’s task of creating and manipulating structures which represent domain concepts, and assigning them relations from a relation dictionary.

Background

acl_arc_intent_classification:train:1349

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: For the cases where retrieval took place, we used F-score (van Rijsbergen 1979; Salton and McGill 1983) to determine the similarity between the response from the top-ranked document and the real response (the formulas for F-score and its contributing factors, recall and precision, appear in Section 4.2). We see that matching on requests yields more retrieved documents than matching on responses, and that matching on request–response pairs yields even more retrieved documents. The second column shows the proportion of requests for which one or more documents were retrieved (using our applicability threshold). Citation Sentence: For the cases where retrieval took place , we used F-score ( van Rijsbergen 1979 ; Salton and McGill 1983 ) to determine the similarity between the response from the top-ranked document and the real response ( the formulas for F-score and its contributing factors , recall and precision , appear in Section 4.2 ) . Context after the citation: The third column in Table 1 shows the proportion of requests for which this similarity is non-zero. Again, the third variant (matching on request–response pairs) retrieves the highest proportion of responses that bear some similarity to the real responses. The fourth column shows the average similarity between the top retrieved response and the real response for the cases where retrieval took place. Here too the third variant yields the best similarity score (0.52).

Uses

acl_arc_intent_classification:train:135

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: &quot;petty conversational implicature&quot; (Hobbs 1979), or the metarules of Section 5.2? Can one deal effectively with the problem of reference without axiomatized Gricean maxims, for instance by using only 6.1.1 Was the Use of a Gricean Maxim Necessary? Citation Sentence: `` petty conversational implicature '' ( Hobbs 1979 ) , or the metarules of Section 5.2 ? Context after the citation: It seems to us that the answer is no. As a case in point, consider the process of finding the antecedent of the anaphor &quot;he&quot; in the sentences

Background

acl_arc_intent_classification:train:1350

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: Unlike other POS taggers, this POS tagger (Mikheev 2000) was also trained to disambiguate sentence boundaries. To test our hypothesis that DCA can be used as a complement to a local-context approach, we combined our main configuration (evaluated in row D of Table 4) with a POS tagger. Citation Sentence: Unlike other POS taggers , this POS tagger ( Mikheev 2000 ) was also trained to disambiguate sentence boundaries . Context after the citation: 10.1 Training a POS Tagger In our markup convention (Section 2), periods are tokenized as separate tokens regardless of whether they stand for fullstops or belong to abbreviations. Consequently a POS tagger can naturally treat them similarly to any other ambiguous words. There is, however, one difference in the implementation of such a tagger.

Uses

acl_arc_intent_classification:train:1351

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: On the other hand, experiments indicate that mental representation and processing of morphologically complex words are not quite language independent (Taft, 2004). Moreover, Indian languages show some distinct phenomena like, compound and composite verbs for which no such investigations have been conducted yet. are morphologically richer than many of their Indo-European cousins. Citation Sentence: On the other hand , experiments indicate that mental representation and processing of morphologically complex words are not quite language independent ( Taft , 2004 ) . Context after the citation: Therefore, the findings from experiments in one language cannot be generalized to all languages making it important to conduct similar experimentations in other languages. This work aims to design cognitively motivated computational models that can explain the organization and processing of Bangla morphologically complex words in the mental lexicon. Presently we will concentrate on the following two aspects: • Organization and processing of Bangla Polymorphemic words: our objective here is to determine whether the mental lexicon decomposes morphologically complex words into its constituent morphemes or does it represent the unanalyzed surface form of a word.

Background

acl_arc_intent_classification:train:1352

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: method Context before the citation: ASARES has been previously applied to the acquisition of word pairs sharing semantic relations defined in the Generative Lexicon framework (Pustejovsky, 1995) and called qualia relations (Bouillon et al., 2001). extraction of N-V pairs from the corpus with the inferred patterns. inference of extraction patterns with ASARES; and 3. Citation Sentence: ASARES has been previously applied to the acquisition of word pairs sharing semantic relations defined in the Generative Lexicon framework ( Pustejovsky , 1995 ) and called qualia relations ( Bouillon et al. , 2001 ) . Context after the citation: Here, we propose to use ASARES in a quite similar way to retrieve our valid N-V pairs. However, the N-V combinations sought are more specific than those that were identified in these previous experiments. Formally, ILP aims at inferring logic programs (sets of Horn clauses, noted H) from a set of facts (examples and counter-examples of the concept to be learnt) and background knowledge (B), such that the program H logically entails the examples with respect to the background knowledge and rejects (most of) the counterexamples. This is transcribed by the two logical formulae B n H �= E+, B n H � E−, which set the aim of an ILP algorithm.

Background

acl_arc_intent_classification:train:1353

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: People are much more likely to consult such evaluative statements than the actual text of a bill or law under discussion, given the dense nature of legislative language and the fact that (U.S.) bills often reach several hundred pages in length (Smith et al., 2005). Evaluative and persuasive documents, such as a politician’s speech regarding a bill or a blogger’s commentary on a legislative proposal, form a particularly interesting type of politically oriented text. Regardless of whether one views such claims as clear-sighted prophecy or mere hype, it is obviously important to help people understand and analyze politically oriented text, given the importance of enabling informed participation in the political process. Citation Sentence: People are much more likely to consult such evaluative statements than the actual text of a bill or law under discussion , given the dense nature of legislative language and the fact that ( U.S. ) bills often reach several hundred pages in length ( Smith et al. , 2005 ) . Context after the citation: Moreover, political opinions are exsional bills and related data was launched in January 1995, when Mosaic was not quite two years old and Altavista did not yet exist. plicitly solicited in the eRulemaking scenario. In the analysis of evaluative language, it is fundamentally necessary to determine whether the author/speaker supports or disapproves of the topic of discussion. In this paper, we investigate the following specific instantiation of this problem: we seek to determine from the transcripts of U.S. Congressional floor debates whether each “speech” (continuous single-speaker segment of text) represents support for or opposition to a proposed piece of legislation.

Background

acl_arc_intent_classification:train:1354

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: None Context before the citation: For english, there is for instance the 15 year old HewlettPackard test suite, a simple text file listing test sentences and grouping them according to linguistics phenomena (Flickinger et al., 1987); and more recently, the much more sophisticated TSNLP (Test Suite for Natural Language Processing) which includes some 9500 test items for English, French and German, each of them being annotated with syntactic and application related information (Oepen and Flickinger, 1998). Another known method consists in developing and using a test suite that is, a set of negative and positive items against which the grammar can be systematically tested. While corpus driven efforts along the PARSEVAL lines (Black et al., 1991) are good at giving some measure of a grammar coverage, they are not suitable for finer grained analysis and in particular, for progress evaluation, regression testing and comparative report generation. Citation Sentence: For english , there is for instance the 15 year old HewlettPackard test suite , a simple text file listing test sentences and grouping them according to linguistics phenomena ( Flickinger et al. , 1987 ) ; and more recently , the much more sophisticated TSNLP ( Test Suite for Natural Language Processing ) which includes some 9500 test items for English , French and German , each of them being annotated with syntactic and application related information ( Oepen and Flickinger , 1998 ) . Context after the citation: Yet because they do not take into account the semantic dimension, none of these tools are adequate for evaluating the paraphrastic power of a grammar. To remedy this, we propose to develop a paraphrase test suite based on the paraphrase typology described in the previous section. In such a testsuite, test items pair a semantic representation with a set of paraphrases verbalising this semantics. The construction and annotation of the paraphrases reflects the paraphrase typology.

Background

acl_arc_intent_classification:train:1355

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Its significance is reflected both in the growing interest in annotation software for word sense tagging (Edmonds and Kilgarriff, 2002) and in the long-standing use of part-of-speech taggers, parsers and morphological analysers for data from English and many other languages. Linguistic annotation of corpora contributes crucially to the study of language at several levels: morphology, syntax, semantics, and discourse. Citation Sentence: Its significance is reflected both in the growing interest in annotation software for word sense tagging ( Edmonds and Kilgarriff , 2002 ) and in the long-standing use of part-of-speech taggers , parsers and morphological analysers for data from English and many other languages . Context after the citation: Linguists, lexicographers, social scientists and other researchers are using ever larger amounts of corpus data in their studies. In corpus linguistics the progression has been from the 1 millionword Brown and LOB corpora of the 1960s, to the 100 million-word British National Corpus of the 1990s. In lexicography this progression is paralleled, for example, by Collins Dictionaries’ initial 10 million word corpus growing to their current corpus of around 600 million words. In addition, the requirement for megaand even giga-corpora1 extends to other applications, such as lexical frequency studies, neologism research, and statistical natural language processing where models of sparse data are built.

Background

acl_arc_intent_classification:train:1356

You will be presented with a citation segment from the section of an NLP research paper, as well as the context surrounding that citation. Classify the intent behind this citation by choosing from one of the following categories: - Background: provides context or foundational information related to the topic. - Extends: builds upon the cited work. - Uses: applies the methods or findings of the cited work. - Motivation: cites the work as inspiration or rationale for the research. - CompareOrContrast: compares or contrasts the cited work with others. - FutureWork: cites the work as a direction for future research. Your answer should be a single word from the following list of options: ["Background", "Extends", "Uses", "Motivation", "CompareOrContrast", "FutureWork"]. Do not include any other text in your response. Section Title: introduction Context before the citation: Each of these Values has equal status, so the notion of a basic-level Value cannot play a role (cfXXX, Dale and Reiter 1995). More importantly, it will now become normal for an object to have many Values for the same Attribute; c4, for example, has the Values > 6 cm, > 10 cm, and > 12 cm. For one thing, it is convenient to view properties of the form size(x) < α as belonging to a different Attribute than those of the form size(x) > α, because this causes the Values of an Attribute to be linearly ordered: Being larger than 12 cm implies being larger than 10 cm, and so on. Citation Sentence: Each of these Values has equal status , so the notion of a basic-level Value can not play a role ( cfXXX , Dale and Reiter 1995 ) . Context after the citation: If we abstract away from the role of basic-level Values, then Dale and Reiter’s FindBestValue chooses the most general Value that removes the maximal van Deemter GRE with Gradable Properties number of distractors, as we have seen. The problem at hand suggests a simpler approach that will always prefer logically stronger inequalities over logically weaker ones, even when they do not remove more distractors.5 (Thus, size(x) > m is preferred over size(x) > n iff m > n; conversely, size(x) < m is preferred over size(x) < n iff m < n.) This is reflected by the order in which the properties are listed above: Once a sizerelated property is selected, later size-related properties do not remove any distractors and will therefore not be included in the description. Let us return to our example.

Background

acl_arc_intent_classification:train:1357