hrithikpiyush/acl-arc huggingface dataset:
The difference between the number of types ( 2850 ) and tokens ( 573 ) in the extracted pairs ( which contain only open-class words ) shows that the same concepts recur , as expected in a didactic text . The syntactic structures of the input data are produced by a parser with good coverage and detailed syntactic information , DIPETT ( Delisle and Szpakowicz , 1995 ) . The parser , written in Prolog , implements a classic constituency English grammar from Quirk et al. ( 1985 ) .
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 . In particular , ( Gross , 1989 ) lists the converses of some 3 500 predicative nouns .
If no parse is found yet , then the parser attempts to strategically allow tokens to subsume a neighbor by making it a dependent ( first with a restricted root set , then without ) . This is similar to the `` deletion '' strategy employed by Zettlemoyer and Collins ( 2007 ) , but we do it directly in the grammar . We add unary rules of the form ( D ) - * u for every potential supertag u in the tree .
Nevertheless , their work bolsters our claims regarding the usefulness of generative models in extrinsic tasks , which we do not describe here . Although this study falls under the general topic of discourse modeling , our work differs from previous attempts to characterize text in terms of domainindependent rhetorical elements ( McKeown , 1985 ; Marcu and Echihabi , 2002 ) . Our task is closer to the work of Teufel and Moens ( 2000 ) , who looked at the problem of intellectual attribution in scientific texts .
tween two words ( Gurevych , 2005 ) .3 Dissimilar words can be semantically related , e.g. via functional relationships ( night -- dark ) or when they are antonyms ( high -- low ) . Many NLP applications require knowledge about semantic relatedness rather than just similarity ( Budanitsky and Hirst , 2006 ) . A number of competing approaches for computing semantic relatedness of words have been developed ( see Section 2 ) .
The Brown corpus comprises 24,242 trees compiled from a variety of text genres including popular lore , general fiction , science fiction , mystery and detective fiction , and humor . It has been shown ( Roland and Jurafsky 1998 ) that the subcategorization tendencies of verbs vary across linguistic domains . Our aim , therefore , is to increase the scope of the induced lexicon not only in terms of the verb lemmas for which there are entries , but also in terms of the frames with which they co-occur .
The system is implemented based on ( Galley et al. , 2006 ) and ( Marcu et al. 2006 ) . In the system , we extract both the minimal GHKM rules ( Galley et al. , 2004 ) , and the rules of SPMT Model 1 ( Galley et al. , 2006 ) with phrases up to length L = 5 on the source side . We then obtain the composed rules by composing two or three adjacent minimal rules .
Not much information has been published on abbreviation identification . One of the better-known approaches is described in Grefenstette and Tapanainen ( 1994 ) , which suggested that abbreviations first be extracted from a corpus using abbreviation-guessing heuristics akin to those described in Section 6 and then reused in further processing . This is similar to our treatment of abbreviation handling , but our strategy is applied on the document rather than corpus level .
The probability distribution trans ( ii , it ' ) is a word-to-word translation model . Unlike the models proposed by Brown et al. ( 1993b ) , this model is symmetric , because both word bags are generated together from a joint probability distribution . Brown and his colleagues ' models , reviewed in Section 4.3 , generate one half of the bitext given the other half , so they are represented by conditional probability distributions .
( Och and Ney , 2002 ; Blunsom et al. , 2008 ) used maximum likelihood estimation to learn weights for MT. ( Och , 2003 ; Moore and Quirk , 2008 ; Zhao and Chen , 2009 ; Galley and Quirk , 2011 ) employed an evaluation metric as a loss function and directly optimized it . ( Watanabe et al. , 2007 ; Chiang et al. , 2008 ; Hopkins and May , 2011 ) proposed other optimization objectives by introducing a margin-based and ranking-based indirect loss functions . All the methods mentioned above train a single weight for the whole development set , whereas our local training method learns a weight for each sentence .
It has been used for transformations of conceptual and syntactic structures during generation in monolingual and multilingual natural language generation ( NLG ) and for transfer in machine translation ( MT ) . Our work extends directions taken in systems such as Ariane ( Vauquois and Boitet , 1985 ) , FoG ( Kittredge and Polguere , 1991 ) , JOYCE ( Rambow and Korelsky , 1992 ) , and LFS ( Iordanskaja et al. , 1992 ) . Although it adopts the general principles found in the abovementioned systems , the approach presented in this paper is more practical , and we believe , would eventually integrate better with emerging statistics-based approaches to MT. * The work performed on the framework by this coauthor was done while at CoGenTex , Inc. .
Association Norms ( AN ) is a collection of association norms collected by Schulte im Walde et al. ( 2012 ) . In association norm experiments , subjects are presented with a cue word and asked to list the first few words that come to mind .
There are a variety of works on efficient parsing with HPSG , which allow the use of HPSGbased processing in practical application contexts ( Flickinger et al. , 2000 ) . Stanford University is developing the English Resource Grammar , an HPSG grammar for English , as a part of the Linguistic Grammars Online ( LinGO ) project ( Flickinger , 2000 ) . In practical context , German , English , and Japanese HPSG-based grammars are developed and used in the Verbmobil project ( Kay et al. , 1994 ) .
Before doing the annotation , though , some preprocessing of the data was required to maximize the matching between our corpus and WordNet . The changes made were inspired by those described in Stetina and Nagao ( 1997 , page 75 ) . To lemmatize the words we used `` morpha , '' a lemmatizer developed by John A. Carroll and freely available at the address : http://www.informatics.susx.ac.uk./research/nlp/carroll/morph.html .
Once the grammar codes have been restructured , it still remains to be shown that the information they encode is going to be of some utility for natural language processing . The grammar code system used in LDOCE is based quite closely on the descriptive grammatical framework of Quirk et al. ( 1972 , 1985 ) . The codes are doubly articulated ; capital letters represent the grammatical relations which hold between a verb and its arguments and numbers represent subcategorisation frames which a verb can appear in .
Given that we were targeting all envisaged access routes from LDOCE to systems implemented in Lisp , and since the natural data structure for Lisp is the s-expression , we adopted the approach of converting the tape source into a set of list structures , one per entry . Our task was made possible by the fact that while far from being a database in the accepted sense of the word , the LDOCE typesetting tape is the only truly computerised dictionary of English ( Michiels , 1983 ) . The logical structure of a dictionary entry is reflected on the tape as a sequence of typed records ( see Figure 1 ) , each with additional internal segmentation , where records and fields correspond to separate units in an entry , such as headword , pronunciation , grammar code , word senses , and so forth .
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 . A detailed description of the kinds of expectation mechanisms appearing in these systems appears in Fink ( 1983 ) . 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 ) .
Obviously , towards an s-node for sampling , the two values of ` P would define two different U-trees . Using the GHKM algorithm ( Galley et al. 2004 ) , we can get two different STSG derivations from the two U-trees based on the fixed word alignment . Each derivation carries a set of STSG rules ( i.e. , minimal GHKM translation rules ) of its own .
For a variety of reasons , medicine is an interesting domain of research . The need for information systems to support physicians at the point of care has been well studied ( Covell et al. , 1985 ; Gorman et al. , 1994 ; Ely et al. , 2005 ) . Retrieval techniques can have a large impact on how physicians access and leverage clinical evidence .
We present an extension of the hierarchical Dirichlet process ( HDP ) model which is able to represent each observable object ( i.e. , event mention ) by a finite number of feature types L . Our HDP extension is also inspired from the Bayesian model proposed by Haghighi and Klein ( 2007 ) . However , their model is strictly customized for entity coreference resolution , and therefore , extending it to include additional features for each observable object is a challenging task ( Ng , 2008 ; Poon and Domingos , 2008 ) .
The phrasal matching score for each n-gram level is calculated as follows : To combine the phrasal matching scores obtained at each n-gram level , and optimize their relative weights , we trained a Support Vector Machine classifier , SVMlight ( Joachims , 1999 ) , using each score as a feature .
In this section , however , we will attempt to draw connections to other clinical information systems ( although not necessarily for question answering ) and related domain-specific question-answering systems . For an overview of systems designed to answer open-domain factoid questions , the TREC QA track overview papers are a good place to start ( Voorhees and Tice 1999 ) . In addition , there has been much work on the application of linguistic and semantic knowledge to information retrieval ; see Lin and Demner-Fushman ( 2006a ) for a brief overview .
Promising features for classification include part of speech , frequency of co-occurrence , relative word position , and translational entropy ( Melamed , 1997 ) . 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 .
One possible response would be to abandon the separation ; the generator could be a single component that handles all of the work . This approach has occasionally been taken , as in Kantrowitz and Bates ( 1992 ) and Danlos ( 1987 ) and , at least implicitly , in Paris and Scott ( 1994 ) and Delin et al. ( 1994 ) ; however , under this approach , all of the flexibility and simplicity of modular design is lost . The opposite approach is to simply ignore the limitations of a modular design and proceed as if there need be no interactions between the components .
The introduction of ILP methods has influenced the coreference area too ( Chang et al. , 2011 ; Denis and Baldridge , 2007 ) . In this paper , we use the Constrained Latent Left-Linking Model ( CL3M ) described in Chang et al. ( 2013 ) in our experiments . The task of mention detection is closely related to Named Entity Recognition ( NER ) .
Following G&G , we require that the prosody rules build a binary tree whose terminals are phonological words and whose node labels are indices that mark boundary salience . An alternative representation based on Liberman and Prince ( 1977 ) is presented in Selkirk ( 1984 ) , which contends that prosody , including prosodic phrasing , is more properly represented as a grid instead of a tree . Although a grid may be more descriptively suitable for some aspects of prosody ( for example , Sproat and Liberman ( 1987 ) use the grid representation for their implementation of stress assignment in compound nominals ) , we are not aware of any evidence for or against a grid representation of discourseneutral phrasing .
For the segmentation task , we also compare our beam-search framework with alternative decoding algorithms including an exact dynamic-programming method , showing that the beam-search method is significantly faster with comparable accuracy . For the joint segmentation and POS-tagging task , we present a novel solution using the framework in this article , and show that it gives comparable accuracies to our previous work ( Zhang and Clark 2008a ) , while being more than an order of magnitude faster . In Section 7 we provide further discussion of the framework based on the studies of the individual tasks .
A study of the query log of the AllTheWeb and Altavista search sites gives an idea of the relevance of the people search task : 11-17 % of the queries were composed of a person name with additional terms and 4 % were identified as person names ( Spink et al. , 2004 ) . According to the data available from 1990 U.S. Census Bureau , only 90,000 different names are shared by 100 million people ( Artiles et al. , 2005 ) . As the amount of information in the WWW grows , more of these people are mentioned in different web pages .
• A deterministic algorithm for building labeled projective dependency graphs ( Nivre , 2006 ) . • History-based feature models for predicting the next parser action ( Black et al. , 1992 ) . • Support vector machines for mapping histories to parser actions ( Kudo and Matsumoto , 2002 ) .
L ( u , v ) represents the likelihood that u and v can be mutual translations . For each co-occurring pair of word types u and v , these likelihoods are initially set proportional to their co-occurrence frequency ( „ , v ) and inversely proportional to their marginal frequencies n ( u ) and n ( v ) 1 , following ( Dunning , 1993 ) 2 . When the L ( u , v ) are re-estimated , the model 's hidden parameters come into play .
The system simply attaches the translation with the highest weight to the existing chunk ordinateurs personnels to produce the mistranslation in ( 50 ) : ( 50 ) * la ordinateurs personnels The problem of boundary friction is clearly visible here : We have inserted a feminine singular determiner into a chunk that was generalized from a masculine plural NP . However , rather than output this wrong translation directly , we use a post hoc validation and ( if required ) correction process based on Grefenstette ( 1999 ) . Grefenstette shows that the Web can be used as a filter on translation quality simply by searching for competing translation candidates and selecting the one that is found most often .
The powerful mechanism of lexical rules ( Carpenter 1991 ) has been used in many natural language processing systems . In this section we briefly discuss some of the more prominent approaches and compare them with the treatment proposed in this paper .
We can implement a deductive theory of quantifier scope using the conditional equivalence mechanism . The version proposed here combines a basic insight from Lewin ( 1990 ) with higher-order unification to give an analysis that has a strong resemblance to that proposed in Pereira ( 1990 , 1991 ) , with some differences that are commented on below . Like Pereira 's approach , it avoids the need for a free variable constraint , nor does it need the explicit recursion on the quantifier restriction imposed by Lewin .
Furthermore , we also evaluate the method on alternate extrinsic loss functions . Liang et al. ( 2006 ) presented a perceptron-based algorithm for learning the phrase-translation parameters in a statistical machine translation system . Similar to the inline-ranker loss function presented here , they use a k-best lists of hypotheses in order to identify parameters which can improve a global objective function : BLEU score .
NLG has to do more than select a distinguishing description ( i.e. , one that unambiguously denotes its referent ; Dale 1989 ) : The selected expression should also be felicitous . Consider the question , discussed in the philosophical logic literature , of whether it is legitimate , for a gradable adjective , to distinguish between `` observationally indifferent '' entities : Suppose two objects x and y , are so similar that it is impossible to distinguish their sizes ; can it ever be reasonable to say that x is large and y is not ?
Given that the marker hypothesis is arguably universal , it is clear that benefits may accrue by using it to facilitate subsentential alignment of ( source , target ) chunks . Juola ( 1994 , 1997 ) conducts some small experiments using his METLA system to show the viability of this approach for English − > French and English − > Urdu . For the English − > French language pair , Juola gives results of 61 % correct translation when the system is tested on the training corpus , and 36 % accuracy when it is evaluated with test data .
( Pollard and Sag [ 1994 , 3141 , following Flickinger [ 19871 ) . This idea of preserving properties can be considered an instance of the well-known frame problem in AT ( McCarthy and Hayes 1969 ) , and we will therefore refer to the specifications left implicit by the linguist as the frame specification , or simply frame , of a lexical rule . Not having to represent the frame explicitly not only enables the linguist to express only the relevant things , but also allows a more compact representation of lexical rules where explicit framing would require the rules to be split up ( Meurers 1994 ) .
• Graph transformations for recovering nonprojective structures ( Nivre and Nilsson , 2005 ) . All experiments have been performed using MaltParser ( Nivre et al. , 2006 ) , version 0.4 , which is made available together with the suite of programs used for preand post-processing .1 1www .
Shi and Wang ( 2007 ) also chunked the sentences before doing 10-fold cross validation , but used an uneven split . We chose to follow Ng and Low ( 2004 ) and split the sentences evenly to facilitate further comparison . Compared with Ng and Low ( 2004 ) , our baseline model gave slightly better accuracy , consistent with our previous observations about the word segmentors ( Zhang and Clark , 2007 ) .
Also , a document matching different translations of one term in the original query may be ranked higher than a document that matches translations of different terms in the original query . That is , a document that contains terms al , a2 and a3 may be ranked higher than a document which contains terms al and b.f. However , the second document is more likely to be relevant since correct translations of the query terms are more likely to co-occur ( Ballesteros and Croft , 1998 ) . A second method is to structure the translated query , separating the translations for one term from translations for other terms .
Instead of defining a total preference ordering , Kibble argues that a partial ordering emerges from the interaction among cohesion ( maintaining the same center ) , salience ( realizing the center as subject ) , and cheapness ( realizing the anticipated center of a following utterance as subject ) . The last years have seen considerable advances in the field of anaphora resolution , but a number of outstanding issues either remain unsolved or need more attention and , as a consequence , represent major challenges to the further development of the field ( Mitkov 2001a ) . A fundamental question that needs further investigation is how far the performance of anaphora resolution algorithms can go and what the limitations of knowledge-poor methods are .
Each transition in the automaton is translated into a definite relation in which the corresponding lexical rule predicate is called , and each final state is encoded by a unit clause . Using an accumulator passing technique ( O'Keefe 1990 ) , we ensure that upon execution of a call to the interaction predicate q_1 a new lexical entry is derived as the result of successive application of a number of lexical rules . Because of the word class specialization step discussed in Section 3.3 , the execution avoids trying out many lexical rule applications that are guaranteed to fail .
Different strategies have been developed in order to identify pairs of terms that share a specific semantic relationship ( such as hyperonymy or meronymy ) or to build classes of terms . However , most strategies are based on `` internal '' or `` external methods '' ( Grabar and Zweigenbaum , 2002 ) , i.e. methods that rely on the form of terms or on the information gathered from contexts . ( In some cases , an additional resource , such as a dictionary or a thesaurus , is used during the identification process . )
An interval is assigned to each word in the sentence , i.e. ( 0-1 ) for `` John '' , ( 1-2 ) for `` picks '' , ( 2-3 ) for `` the '' , ( 3-4 ) for `` box '' and ( 4-5 ) for `` up '' . A substring in the sentence that corresponds to a node in the representation tree is denoted by assigning the interval of the substring to SNODE of 2 These definitions are based on the discussion in ( Tang , 1994 ) and Boitet & Zaharin ( 1988 ) . the node , e.g. the node `` picks up '' with SNODE intervals ( 1-2 +4 -5 ) corresponds to the words `` picks '' and `` up '' in the string with the similar intervals.The correspondence between subtrees and substrings are denoted by the interval assigned to the STREE of each node , e.g. the subtree rooted at node `` picks up '' with STREE interval ( 0-5 ) corresponds to the whole sentence `` John picks the box up '' .
In some cases , the existence of a predominant person ( such as a celebrity or a historical figure ) makes it likely to dominate the ranking of search results , complicating the task of finding information about other people sharing her name . The Web People Search task , as defined in the first WePS evaluation campaign ( Artiles et al. , 2007 ) , consists of grouping search results for a given name according to the different people that share it . Our goal in this paper is to study which document features can contribute to this task , and in particular to find out which is the role that can be played by named entities ( NEs ) : ( i ) How reliable is NEs overlap between documents as a source of evidence to cluster pages ?
For example , in the set born in ( Mozart , • ) there is no contradiction between the y values `` Salzburg '' and `` Austria '' , but `` Salzburg '' conflicts with `` Vienna '' . Although this is only true in cases where y occurs in an upward monotone context ( MacCartney and Manning , 2007 ) , in practice genuine contradictions between y-values sharing a meronym relationship are extremely rare . We therefore simply assigned contradictions between meronyms a probability close to zero .
The test set includes 72 files , which are chtb_001 to chtb_041 , and chtb_900 to chtb_931 . We use the same data setting with Xue ( 2008 ) , however a bit different from Xue and Palmer ( 2005 ) .
One key issue is how to define the set of similar words , SIMS ( w ) . Erk ( 2007 ) compared a number of techniques for creating similar-word sets and found that both the Jaccard coefficient and Lin ( 1998a ) 's information-theoretic metric work best . Similarity-smoothed models are simple to compute , potentially adaptable to new domains , and require no manually-compiled resources such as WordNet .
Baldridge further notes that due to the natural associativity of CCG , adjacent categories tend to be combinable . We previously showed that incorporating this intuition into a Bayesian prior can help train a CCG supertagger ( Garrette et al. , 2014 ) . In this paper , we present a novel parsing model that is designed specifically for the capacity to capture both of these universal , intrinsic properties of CCG .