hrithikpiyush/acl-arc huggingface dataset:
A positive answer would not be psychologically plausible , since x and y are indistinguishable ; but a negative answer would prohibit any binary distinction between objects that are large and objects that are not , given that one can always construct objects x and y , one of which falls just below the divide while the other falls just above it . This is the strongest version of the sorites paradox ( e.g. , Hyde 2002 ) . Our approach to vague descriptions allows a subtle response : that the offending statement may be correct yet infelicitous .
Because our approach is orthogonal to the base model used , the constraints described here could be applied in principle to IBM Model 4 if exact inference was efficient , hopefully yielding similar improvements . We used a standard implementation of IBM Model 4 ( Och and Ney 2003 ) and because changing the existing code is not trivial , we could not use the same stopping criterion to avoid overfitting and we are not able to produce precision/recall curves . We trained IBM Model 4 using the default configuration of the
Cross-lingual Textual Entailment ( CLTE ) has been proposed by ( Mehdad et al. , 2010 ) as an extension of Textual Entailment ( Dagan and Glickman , 2004 ) that consists in deciding , given two texts T and H in different languages , if the meaning of H can be inferred from the meaning of T . The task is inherently difficult , as it adds issues related to the multilingual dimension to the complexity of semantic inference at the textual level .
The language grounding problem has come in many different flavors with just as many different approaches . Some approaches apply semantic parsing , where words and sentences are mapped to logical structure meaning ( Kate and Mooney , 2007 ) . Others provide automatic mappings of natural language instructions to executable actions , such as interpreting navigation directions ( Chen and Mooney , 2011 ) or robot commands ( Tellex et al. , 2011 ; Matuszek et al. , 2012 ) .
They are intended as an illustration of the power of abduction , which in this framework helps determine the universe of the model ( that is the set of entities that appear in it ) . Other factors , such as the role of focus ( Grosz 1977 , 1978 ; Sidner 1983 ) or quantifier scoping ( Webber 1983 ) must play a role , too . Determining the relative importance of those factors , the above metarules , and syntactic clues , appears to be an interesting topic in itself .
Indexing is performed by approximately 100 indexers with at least bachelor 's degrees in life sciences and formal training in indexing provided by NLM . Since mid-2002 , the Library has been employing software that automatically suggests MeSH headings based on content ( Aronson et al. 2004 ) . Nevertheless , the indexing process remains firmly human-centered .
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 .
The similarity-smoothed examples will be undefined if SIMS ( w ) is empty . Also , the Keller and Lapata ( 2003 ) approach will be undefined if the pair is unobserved on the web . As a reasonable default for these cases , we assign them a negative decision .
The automation of help-desk responses has been previously tackled using mainly knowledge-intensive paradigms , such as expert systems ( Barr and Tessler 1995 ) and case-based reasoning ( Watson 1997 ) . Such technologies require significant human input , and are difficult to create and maintain ( Delic and Lahaix 1998 ) .
To create the positives , we automatically parse a large corpus , and then extract the predicate-argument pairs that have a statistical association in this data . We measure this association using pointwise Mutual Information ( MI ) ( Church and Hanks , 1990 ) . The MI between a
Some methods of semantic relation analysis rely on predefined templates filled with information from processed texts ( Baker et al. , 1998 ) . In other methods , lexical resources are specifically tailored to meet the requirements of the domain ( Rosario and Hearst , 2001 ) or the system ( Gomez , 1998 ) .
2.1 Corpora for Evaluation There are two corpora normally used for evaluation in a number of text-processing tasks : the Brown corpus ( Francis and Kucera 1982 ) and the Wall Street Journal ( WSJ ) corpus , both part of the Penn Treebank ( Marcus , Marcinkiewicz , and Santorini 1993 ) . The Brown corpus represents general English .
tween two speakers , such as explicit assertions ( `` I second that ! '' ) or quotation of messages in emails or postings ( see Mullen and Malouf ( 2006 ) but cfXXX Agrawal et al. ( 2003 ) ) . Agreement evidence can be a powerful aid in our classification task : for example , we can easily categorize a complicated ( or overly terse ) document if we find within it indications of agreement with a clearly positive text .
Andrews et al. ( 2009 ) extend LDA to allow for the inference of document and topic distributions in a multimodal corpus . In their model , a document consists of a set of ( word , feature ) pairs ,4 rather than just words , and documents are still modeled as mixtures of shared topics .
The shallow parser used is the SNoW-based CSCL parser ( Punyakanok and Roth , 2001 ; Munoz et al. , 1999 ) . SNoW ( Carleson et al. , 1999 ; Roth , 1998 ) is a multi-class classifier that is specifically tailored for learning in domains in which the potential number of information sources ( features ) taking part in decisions is very large , of which NLP is a principal example . It works by learning a sparse network of linear functions over a pre-defined or incrementally learned feature space .
The lexicon is entered as phonetic pronunciations that are then augmented to account for a number of phonological rules . The search algorithm is the standard Viterbi search ( Viterbi 1967 ) , except that the match involves a network-to-network alignment problem rather than sequence-to-sequence . When we first integrated this recognizer with TINA , we used a `` wire '' connection , in that the recognizer produced a single best output , which was then passed to TINA for parsing .
Gale , Church , and Yarowsky ( 1992 ) showed that words strongly tend to exhibit only one sense in a document or discourse ( `` one sense per discourse '' ) . Since then this idea has been applied to several tasks , including word sense disambiguation ( Yarowsky 1995 ) and named-entity recognition ( Cucerzan and Yarowsky 1999 ) . Gale , Church , and Yarowsky 's observation is also used in our DCA , especially for the identification of abbreviations .
6 For Sent-Pred we also experimented with grammatical and sentence-based syntactic features , such as number of syntactic phrases , grammatical mood , and grammatical person ( Marom and Zukerman 2006 ) , but the simple binary bag-of-lemmas representation yielded similar results . 7 We employed the LIBSVM package ( Chang and Lin 2001 ) . 1 .
This KS could do not evaluate the role of induced SC knowledge be useful for ACE coreference , since ACE is conin coreference resolution : many of them evaluate cerned with resolving only NPs that are mentions . coreference performance on perfect mentions ( e.g. , Incorporate the two knowledge sources in a Luo et al. ( 2004 ) ) ; and for those that do report percoreference resolver . Next , we investigate whether formance on automatically extracted mentions , they these two KSs can improve a learning-based basedo not explain whether or how the induced SC inforline resolver that employs a fairly standard feature mation is used in their coreference algorithms .
He likes spinach . According to Hobbs ( 1979 , p. 67 ) , these two sentences are incoherent . However , the same fragment , augmented with the third sentence Mary told him yesterday that the French spinach crop failed and Turkey is the only country ... ( ibid . )
However , the `` flowcharts '' in the current project are probabilistic in nature and the problems associated with matching incoming sentences to existing nodes has not been previously addressed . Another dialogue acquisition system has been developed by Ho ( 1984 ) . However , that system has different goals : to enable the user to consciously design a dialogue to embody a particular human-machine interaction .
Lexical functional grammar ( Kaplan and Bresnan 1982 ; Bresnan 2001 ; Dalrymple 2001 ) is a member of the family of constraint-based grammars . It posits minimally two levels of syntactic representation :2 c ( onstituent ) - structure encodes details of surface syntactic constituency , whereas f ( unctional ) - structure expresses abstract syntactic information about predicate -- argument -- modifier relations and certain morphosyntactic properties such as tense , aspect , and case .
The reordering cost , evaluation 1Our rules are similar to those from Xu et al. ( 2009 ) . criteria and data used in our experiments are based on the work of Talbot et al. ( 2011 ) .
They used a 0-10 range of relatedness scores , but did not give further details about their experimental setup . In psycholinguistics , relatedness of words can also be determined through association tests ( Schulte im Walde and Melinger , 2005 ) . Results of such experiments are hard to quantify and can not easily serve as the basis for evaluating SR measures .
functional inflectional number feature , but not full functional gender ( only for adjectives and verbs but not for nouns ) , nor rationality . In this article , we use an in-house system which provides functional gender , number , and rationality features ( Alkuhlani and Habash 2012 ) . See Section 5.2 for more details .
This corpus annotation bottleneck becomes even more problematic for voluminous data sets drawn from the web . The use of the web as a corpus for teaching and research on language has been proposed a number of times ( Kilgarriff , 2001 ; Robb , 2003 ; Rundell , 2000 ; Fletcher , 2001 , 2004b ) and received a special issue of the journal Computational Linguistics ( Kilgarriff and Grefenstette , 2003 ) . Studies have used several different methods to mine web data .
Several authors in communication studies have pointed out that head movements are relevant to feedback phenomena ( see McClave ( 2000 ) for an overview ) . Others have looked at the application of machine learning algorithms to annotated multimodal corpora .
One of the advantages is that it can adapt the weights for each of the test sentences . Our method resorts to some translation examples , which is similar as example-based translation or translation memory ( Watanabe and Sumita , 2003 ; He et al. , 2010 ; Ma et al. , 2011 ) . Instead of using translation examples to construct translation rules for enlarging the decoding space , we employed them to discriminatively learn local weights .
3.1.1 can be verified empirically on both ACE-2004 and OntoNotes-5 .0 datasets . Baseline Systems We choose three publicly available state-of-the-art end-to-end coreference systems as our baselines : Stanford system ( Lee et al. , 2011 ) , Berkeley system ( Durrett and Klein , 2014 ) and HOTCoref system ( Bj ¨ orkelund and Kuhn , 2014 ) . Developed Systems Our developed system is built on the work by Chang et al. ( 2013 ) , using Constrained Latent Left-Linking Model ( CL3M ) as our mention-pair coreference model in the joint framework10 .
Nevertheless , the full document text is present in most systems , sometimes as the only feature ( Sugiyama and Okumura , 2007 ) and sometimes in combination with others see for instance ( Chen and Martin , 2007 ; Popescu and Magnini , 2007 ) - . Other representations use the link structure ( Malin , 2005 ) or generate graph representations of the extracted features ( Kalashnikov et al. , 2007 ) . Some researchers ( Cucerzan , 2007 ; Nguyen and Cao , 2008 ) have explored the use of Wikipedia information to improve the disambiguation process .
Furthermore , because of the complexity of the task , a combination of phrase based structures and dependency-based structures perform the best . This revalidates the observation of Nguyen et al. ( 2009 ) that phrase structure representations and dependency representations add complimentary value to the learning task . We also introduced a new sequence structure ( SqGRW ) which plays a role in achieving the best accuracy for both , social event detection and social event classification tasks .
The parsing algorithm used for all languages is the deterministic algorithm first proposed for unlabeled dependency parsing by Nivre ( 2003 ) and extended to labeled dependency parsing by Nivre et al. ( 2004 ) . The algorithm builds a labeled dependency graph in one left-to-right pass over the input , using a stack to store partially processed tokens and adding arcs using four elementary actions ( where top is the token on top of the stack and next is the next token ) :
SemCat ( semantic category ) of predicate , SemCat of first word , SemCat of head word , SemCat of last word , SemCat of predicate + SemCat of first word , SemCat of predicate + SemCat of last word , predicate + SemCat of head word , SemCat of predicate + head word . The semantic categories of verbs and other words are extracted from the Semantic Knowledge-base of Contemporary Chinese ( Wang et al. 2003 ) . verb AllFrameSets , the combination of all the framesets of a predicate .
In this section , we investigate whether these observations hold true for training statistical parsing models as well . Moreover , in order to determine whether the performances of the predictive criteria are consistent across different learning models within the same domain , we have performed the study on two parsing models : one based on a context-free variant of tree-adjoining grammars ( Joshi , Levy , and Takahashi 1975 ) , the Probabilistic Lexicalized Tree Insertion Grammar ( PLTIG ) formalism ( Schabes and Waters 1993 ; Hwa 1998 ) , and Collins 's Model 2 parser ( 1997 ) . Although both models are lexicalized , statistical parsers , their learning algorithms are different .
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 .
Out of this , 1500 verb sequences are unique to each of them and rest 500 are overlapping . We measure the inter annotator agreement using the Fleiss Kappa ( Fleiss et al. , 1981 ) measure ( x ) where the agreement lies around 0.79 . Next , out of the 500 common verb sequences that were annotated by all the three linguists , we randomly choose 300 V1 + V2 pairs and presented them to 36 native Bangla speakers .
The task we used to compare different generalisation techniques is similar to that used by Pereira et al. ( 1993 ) and Rooth et al. ( 1999 ) . The task is to decide which of two verbs , v and vi , is more likely to take a given noun , n , as an object .
Against the background of a growing interest in multilingual NLP , multilingual anaphora / coreference resolution has gained considerable momentum in recent years ( Aone and McKee 1993 ; Azzam , Humphreys , and Gaizauskas 1998 ; Harabagiu and Maiorano 2000 ; Mitkov and Barbu 2000 ; Mitkov 1999 ; Mitkov and Stys 1997 ; Mitkov , Belguith , and Stys 1998 ) . Other milestones of recent research include the deployment of probabilistic and machine learning techniques ( Aone and Bennett 1995 ; Kehler 1997 ; Ge , Hale , and Charniak 1998 ; Cardie and Wagstaff 1999 ; the continuing interest in centering , used either in original or in revised form ( Abracos and Lopes 1994 ; Strube and Hahn 1996 ; Hahn and Strube 1997 ; Tetreault 1999 ) ; and proposals related to the evaluation methodology in anaphora resolution ( Mitkov 1998a , 2001b ) . For a more detailed survey of the state of the art in anaphora resolution , see Mitkov ( forthcoming ) .
The presented method concerns primarily with phrases , which can be represented by a tree structure . It is not aimed at handling dependencies , which require heavy use of lexical information ( Hindle and Rooth , 1993 , for PP attachment ) . As ( Daelemans et al. , 1999 ) show , lexical information improves on NP and VP chunking as well .
The accuracy and speed of state-of-the-art dependency parsers has motivated a resumed interest in utilizing the output of parsing as an input to many downstream natural language processing tasks . This includes work on question answering ( Wang et al. , 2007 ) , sentiment analysis ( Nakagawa et al. , 2010 ) , MT reordering ( Xu et al. , 2009 ) , and many other tasks . In most cases , the accuracy of parsers degrades when run on out-of-domain data ( Gildea , 2001 ; McClosky et al. , 2006 ; Blitzer et al. , 2006 ; Petrov et al. , 2010 ) .
As shown in ( Minnen , 1996 ) • The presented research was carried out at the University of Tubingen , Germany , as part of the Sonderforschungsbereich 340 . I A more detailed discussion of various aspects of the proposed parser can be found in ( Minnen , 1998 ) . magic is an interesting technique with respect to natural language processing as it incorporates filtering into the logic underlying the grammar and enables elegant control independent filtering improvements .
However , he used undergraduate , non-professional subjects , and did not consider re-tuning . Our experimental design with professional bilingual translators follows our previous work Green et al. ( 2013a ) comparing scratch translation to post-edit . Many research translation UIs have been proposed including TransType ( Langlais et al. , 2000 ) , Caitra ( Koehn , 2009b ) , Thot ( Ortiz-Martínez and Casacuberta , 2014 ) , TransCenter ( Denkowski et al. , 2014b ) , and CasmaCat ( Alabau et al. , 2013 ) .
Automatically trainable software is generally seen as a way of producing systems that are quickly retrainable for a new corpus , for a new domain , or even for another language . Thus , the second class of SBD systems employs machine learning techniques such as decision tree classifiers ( Riley 1989 ) , neural networks ( Palmer and Hearst 1994 ) , and maximum-entropy modeling ( Reynar and Ratnaparkhi 1997 ) . Machine learning systems treat the SBD task as a classification problem , using features such as word spelling , capitalization , suffix , and word class found in the local context of a potential sentence-terminating punctuation sign .
The design and implementation of our current system leaves many open avenues for future exploration , one of which concerns our assumptions about the query interface . Previously , a user study ( Lin et al. 2003 ) has shown that people are reluctant to type full natural language questions , even after being told that they were using a questionanswering system and that typing complete questions would result in better performance . We have argued that a query interface based on structured PICO frames will yield better-formulated queries , although it is unclear whether physicians would invest
We will examine the worst-case complexity of interpretation as well as generation to shed some light on the hypothesis that vague descriptions are more difficult to process than others because they involve a comparison between objects ( Beun and Cremers 1998 , Krahmer and Theune 2002 ) . Before we do this , consider the tractability of the original IA .
The idea behind the Doc-Pred method is similar to Bickel and Scheffer 's ( 2004 ) : Response documents are grouped into clusters , one of these clusters is predicted for a new request on the basis of the request 's features , and the response that is most representative of the predicted cluster ( closest to the centroid ) is selected . In our case , the clustering is performed by the program Snob , which implements mixture modeling combined with model selection based on the Minimum Message Length ( MML ) criterion ( Wallace and Boulton 1968 ; Wallace 2005 ) . We chose this program because the number of clusters does not have to be specified in advance , and it returns a probabilistic interpretation for its clusters ( this interpretation is used by the Sent-Pred method , Section 3.2.2 ) .
Sanderson , 1994 studied the issue of disambiguation for mono-lingual M. The third approach to cross-lingual retrieval is to map queries and documents to some intermediate representation , e.g latent semantic indexing ( LSI ) ( Littman et al , 1998 ) , or the General Vector space model ( GVSM ) , ( Carbonell et al , 1997 ) . We believe our approach is computationally less costly than ( LSI and GVSM ) and assumes less resources ( WordNet in Diekema et al. , 1999 ) .
There are a number of generalised NLP systems in the literature . Many provide graphical user interfaces ( GUI ) for manual annotation ( e.g. General Architecture for Text Engineering ( GATE ) ( Cunningham et al. , 1997 ) and the Alembic Workbench ( Day et al. , 1997 ) ) as well as NLP tools and resources that can be manipulated from the GUI . For instance , GATE currently provides a POS tagger , named entity recogniser and gazetteer and ontology editors ( Cunningham et al. , 2002 ) .
We compute SURF keypoints for every image in our data set using SimpleCV3 and randomly sample 1 % of the keypoints . The keypoints are clustered into 5,000 visual codewords ( centroids ) using k-means clustering ( Sculley , 2010 ) , and images are then quantized over the 5,000 codewords . All images for a given word are summed together to provide an average representation for the word .
The inferences process is cascaded , and a clear improvement was obtained by passing results across cascades . Another approach for partial parsing was presented by Skut and Brants ( 1998 ) . Their method is an extension of that of Church ( 1988 ) for finding NP 's , achieved by extending the feature space to include structural information .
In the past decade , knowledge-lean approaches have significantly influenced research in noun phrase ( NP ) coreference resolution -- the problem of determining which NPs refer to the same real-world entity in a document . In knowledge-lean approaches , coreference resolvers employ only morpho-syntactic cues as knowledge sources in the resolution process ( e.g. , Mitkov ( 1998 ) , Tetreault ( 2001 ) ) . 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 .
Patient information is no doubt important to answering clinical questions , and our work could certainly benefit from experiences gained in the PERSIVAL project . The application of domain models and deep semantic knowledge to question answering has been explored by a variety of researchers ( e.g. , Jacquemart and Zweigenbaum 2003 , Rinaldi et al. 2004 ) , and was also the focus of recent workshops on question answering in restricted domains at ACL 2004 and AAAI 2005 . Our work contributes to this ongoing discourse by demonstrating a specific application in the domain of clinical medicine .
The 2D models employing feature norms and association norms do significantly better than the text-only model ( two-tailed t-test ) . This result is consistent with other works using this model with these features ( Andrews et al. , 2009 ; Silberer and Lapata , 2012 ) . We also see that the SURF visual words are able to provide notable , albeit not significant , improvements over the text-only modality .