hrithikpiyush/acl-arc huggingface dataset:
With respect to these systems , the contribution of our work lies in the consideration of different kinds of corpus-based approaches ( namely , retrieval and prediction ) applied at different levels of granularity ( namely , document and sentence ) . Two applications that , like help-desk , deal with question -- answer pairs are : summarization of e-mail threads ( Dalli , Xia , and Wilks 2004 ; Shrestha and McKeown 2004 ) , and answer extraction in FAQs ( Frequently Asked Questions ) ( Berger and Mittal 2000 ; Berger et al. 2000 ; Jijkoun and de Rijke 2005 ; Soricut and Brill 2006 ) .
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 ) . Many approaches to multimodal research have succeeded by abstracting away raw perceptual information and using high-level representations instead .
Thus , using STSG , we can build more specific U-trees for translation . In addition , we find that the Bayesian SCFG grammar can not even significantly outperform the heuristic SCFG grammar ( Blunsom et al. 2009 ) 5 . This would indicate that the SCFG-based derivation tree as by-product is also not such good for tree-based translation models .
A derivation tree is a structural description in LTAG and represents the history of combinations of elementary trees . There are several grammars developed in the FB-LTAG formalism , including the XTAG English grammar , a large-scale grammar for English ( The XTAG Research Group , 2001 ) . The XTAG group ( Doran et al. , 2000 ) at the University of Pennsylvania is also developing Korean , Chinese , and Hindi grammars .
In related work , Miyao ( 1999 ) describes an approach to packing in which alternative feature structures are represented as packed , distributed disjunctions of feature structure fragments . Although the approach may have potential , the shifting of complex accounting into the unification algorithm is at variance with the findings of Kiefer et al. ( 1999 ) , who report large speed-ups from the elimination of disjunction processing during unification . Unfortunately , the reported evaluation measures and lack of discussion of parser control issues are insufficient to allow a precise comparison .
This encourages more transitions and hence shorter phrases . For the task of unsupervised dependency parsing , Smith and Eisner ( 2006 ) add a constraint of the form `` the average length of dependencies should be X '' to capture the locality of syntax ( at least half of the dependencies are between adjacent words ) , using a scheme they call structural annealing . They modify the model 's distribution over trees pe ( y ) by a penalty term as : pe ( y ) ∝ pe ( y ) e ( δ Ee ∈ y length ( e ) ) , where length ( e ) is the surface length of edge e .
A visual display gives direct feedback on some of these parameters . The speech and language processing architecture is based on that of the SRI CommandTalk system ( Moore et al. , 1997 ; Stent et a. , 1999 ) . The system comprises a suite of about 20 agents , connected together using the SRI Open Agent Architecture ( OAA ; ( Martin et al. , 1998 ) ) .
This suggests that the noise introduced by incorrect translations can be tackled by increasing the coverage of the paraphrase table . Second , in line with the findings of ( Mehdad et al. , 2010 ) , the results obtained over the MT-derived corpus are equal to those we achieve over the original RTE3 dataset ( i.e. 63.50 % ) . Third , the accuracy obtained over the CLTE corpus using combined phrase and paraphrase tables ( 62.88 % , as reported in Table 1 ) is comparable to the best result gained over the automatically translated dataset ( 63.50 % ) .
Due to the corpusbased approach , many domain-specific concept pairs are introduced into the test set . Therefore , inter-subject correlation is lower than the results obtained by Gurevych ( 2006 ) . In our experiment , intra-subject correlation was r = .670 for the first and r = .623 for the second individual who repeated the experiment , yielding a summarized intra-subject correlation of r = .647 .
Fixed roles are too coarsegrained to account for certain semantic distinctions -- the only recourse , to expand the inventory of roles , comes with the price of increased complexity , e.g. , in the syntaxto-semantics mapping . There is a general consensus among theoretical linguists that the proper representation of verbal argument structure is event structure -- representations grounded in a theory of events that decompose semantic roles in terms of primitive predicates representing concepts such as causality and inchoativity ( Dowty , 1979 ; Jackendoff , 1983 ; Pustejovsky , 1991b ; Rappaport Hovav and Levin , 1998 ) . Consider the following example :
The appropriate analysis-level style markers have to be defined according to the methodology used by the tool in order to analyze the text . For example , some similar measures have been used in stylistic experiments in information retrieval on the basis of a robust parser built for information retrieval purposes ( Strzalkowski 1994 ) . This parser produces trees to represent the structure of the sentences that compose the text .
The function δ ( · , · ) denotes the Kronecker delta . The resulting training procedure is analogous to the one presented in ( Brown et al. , 1993 ) and ( Tillmann and Ney , 1997 ) . The next section presents variants of the basic unconstrained model by putting restrictions on the valid regions of triggers ( in-phrase vs. out-ofphrase ) and using alignments obtained from either GIZA + + training or forced alignments in order to reduce the model size and to incorporate knowledge already obtained in previous training steps .
The recognizer can continue to propose hypotheses until one successfully parses , or until a quitting criterion is reached , such as an upper bound on N. Whereas in the loosely coupled system the parser acts as a filter only on completed candidate solutions ( Zue et al. 1991 ) , the tightly coupled system allows the parser to discard partial theories that have no way of continuing . Following the Viterbi search , each partial theory is first extended by the parser to specify possible next words , which are then scored by the recognizer .
Compared to their work , we do not rely on any Tree-bank resources and focus on generating effective unsupervised tree structures for tree-based translation models . Zollmann and Venugopal ( 2006 ) substituted the non-terminal X in hierarchical phrase-based model by extended syntactic categories . Zollmann and Vogel ( 2011 ) further labeled the SCFG rules with POS tags and unsupervised word classes .
Last , but not least , application-driven research in areas such as automatic abstracting and information extraction independently highlighted the importance of anaphora and coreference resolution , boosting research in this area . Much of the earlier work in anaphora resolution heavily exploited domain and linguistic knowledge ( Sidner 1979 ; Carter 1987 ; Rich and LuperFoy 1988 ; Carbonell and Brown 1988 ) , which was difficult both to represent and to process , and which required considerable human input . However , the pressing need for the development of robust and inexpensive solutions to meet the demands of practical NLP systems encouraged many researchers to move away from extensive domain and linguistic knowledge and to embark instead upon knowledge-poor anaphora resolution strategies .
Generally , people avoid commenting , preferring to edit each document . The paradigm is `` write many , read many '' ( Cunningham and Leuf , 2001 ) . We believe that ownership has an important role and we do not want to force our users to take a
Phonetic and prosodic segmentation and annotation were performed independently and in parallel by two annotators and then an agreed upon version was produced with the supervision of an expert annotator , for more information see Gr cents nnum ( 2006 ) . The Praat tool was used ( Boersma and Weenink , 2009 ) . The feedback expressions we analyse here are Yes and No expressions , i.e. in Danish words like ja ( yes ) , jo ( yes in a negative context ) , jamen ( yes but , well ) , nej ( no ) , nceh ( no ) .
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 ) . the adjective to be semantically equivalent to the superlative form ( and , analogously , the comparative ) : The n large mice = The largest n mice The large mice = The largest mice The large mouse = The largest mouse .
We can also load a lexicon into memory that is shared between all of the components , reducing the memory use . The implementation has been inspired by experience in extracting information from very large corpora ( Curran and Moens , 2002 ) and performing experiments on maximum entropy sequence tagging ( Curran and Clark , 2003 ; Clark et al. , 2003 ) . We have already implemented a POS tagger , chunker , CCG supertagger and named entity recogniser using the infrastructure .
The ind value 5SVMlight is available at svmlight.joachims.org . Default parameters were used , although experimentation with different parameter settings is an important direction for future work ( Daelemans and Hoste , 2002 ; Munson et al. , 2005 ) . for each speech segment s was based on the signed distance d ( s ) from the vector representing s to the trained SVM decision plane :
This section describes our joint coreference resolution and mention head detection framework . Our work is inspired by the latent left-linking model in Chang et al. ( 2013 ) and the ILP formulation from Chang et al. ( 2011 ) . The joint learning and inference model takes as input mention head candidates
( NLM ) , which also serves as a readily available corpus of abstracts for our experiments . 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 . McKnight and Srinivasan ( 2003 ) have previously examined the task of categorizing sentences in medical abstracts using supervised discriminative machine learning techniques .
The text planning component selects and organizes the information to be expressed in some internal representation and then sends it to the linguistic component , which converts the information from the internal form into grammatical sentences of some natural language . The names given to the components vary ; they have been called `` strategic '' and `` tactical '' components ( e.g. , McKeown 1985 ; Thompson 1977 ; Danlos 1987 ) 1 , `` planning '' and `` realization '' ( e.g. , McDonald 1983 ; Hovy 1988a ) , or simply `` what to say '' versus `` how to say it '' ( e.g. , Danlos 1987 ; Reithinger 1990 ) . The precise division of work between the components can also vary , as can the extent to which the text planner is responsible for selecting ( as opposed to merely organizing ) the information to be expressed .
Over the last decade there has been a lot of interest in developing tutorial dialogue systems that understand student explanations ( Jordan et al. , 2006 ; Graesser et al. , 1999 ; Aleven et al. , 2001 ; Buckley and Wolska , 2007 ; Nielsen et al. , 2008 ; VanLehn et al. , 2007 ) , because high percentages of selfexplanation and student contentful talk are known to be correlated with better learning in humanhuman tutoring ( Chi et al. , 1994 ; Litman et al. , 2009 ; Purandare and Litman , 2008 ; Steinhauser et al. , 2007 ) . However , most existing systems use pre-authored tutor responses for addressing student errors .
We use the TRIPS dialogue parser ( Allen et al. , 2007 ) to parse the utterances . The parser provides a domain-independent semantic representation including high-level word senses and semantic role labels .
Further , user-based evaluations of the output produced by our system require the subjects to read relatively long request -- response e-mails , which quickly becomes tedious . In order to address these limitations in a practical way , we conducted a small user study where we asked four judges ( graduate students from the Faculty of Information Technology at Monash University ) to assess the responses generated by our system ( Marom and Zukerman 2007a ) . Our judges were instructed to position themselves as help-desk customers who know that they are receiving an automated response , and that such a response is likely to arrive quicker than a response composed by an operator .
The language understanding module receives word hypotheses from the speech recognition module and incrementally understands the sequence of the word hypotheses to update the dialogue state , in which the result of understanding and discourse information are represented by a frame ( i.e. , attribute-value pairs ) . The understanding module utilizes ISSS ( Incremental Significant-utterance Sequence Search ) ( Nakano et al. , 1999b ) , which is an integrated parsing and discourse processing method . ISSS enables the incremental understanding of user utterances that are not segmented into sentences prior to parsing by incrementally finding the most plausible sequence of sentences ( or signcant utterances in the ISSS terms ) out of the possible sentence sequences for the input word sequence .
LiLFeS is one of the fastest inference engines for processing feature structure logic , and efficient HPSG parsers have already been built on this system ( Nishida et al. , 1999 ; Torisawa et al. , 2000 ) . We applied our system to the XTAG English grammar ( The XTAG Research Group , 2001 ) 3 , which is a large-scale FB-LTAG grammar for English . 2The RenTAL system is available at : http://www-tsujii.is.s.u-tokyo.ac.jp/rental/ 3We used the grammar attached to the latest distribution of an LTAG parser which we used for the parsing experiment .
With this method , all the different phrases in one language that are aligned with the same phrase in the other language are extracted as paraphrases . After the extraction , pruning techniques ( Snover et al. , 2009 ) can be applied to increase the precision of the extracted paraphrases . In our work we used available2 paraphrase databases for English and Spanish which have been extracted using the method previously outlined .
In this paper , we extend two classes of model adaptation methods ( i.e. , model interpolation and error-driven learning ) , which have been well studied in statistical language modeling for speech and natural language applications ( e.g. , Bacchiani et al. , 2004 ; Bellegarda , 2004 ; Gao et al. , 2006 ) , to ranking models for Web search applications . We have evaluated our methods on two adaptation experiments over a wide variety of datasets where the in-domain datasets bear different levels of similarities to their background datasets .
For instance , GATE currently provides a POS tagger , named entity recogniser and gazetteer and ontology editors ( Cunningham et al. , 2002 ) . GATE goes beyond earlier systems by using a component-based infrastructure ( Cunningham , 2000 ) which the GUI is built on top of . This allows components to be highly configurable and simplifies the addition of new components to the system .
The reader may consult recent papers on this subject ( e.g. Moens and Steedman 1987 ; Webber 1987 ) to see what a formal interpretation of events in time might look like . Since sentences can refer to events described by other sentences , we may need also a quotation operator ; Perlis ( 1985 ) describes how first order logic can be augmented with such an operator . Extending and revising Jackendoff 's ( 1983 ) formalism seems to us a correct method to achieve the correspondence between syntax and semantics expressed in the grammatical constraint ( `` that one should prefer a semantic theory that explains otherwise arbitrary generalizations about the syntax and the lexicon '' -- ibid . )
The system uses a knowledge base implemented in the KM representation language ( Clark and Porter , 1999 ; Dzikovska et al. , 2006 ) to represent the state of the world . At present , the knowledge base represents 14 object types and supports the curriculum containing over 200 questions and 40 different circuits .
It may be that our notion of distance to lexical resource entries is too naive . A possible future direction would be to compare the query string to retrieved results using a method similar to that of Tsuruoka and Tsujii ( 2003 ) . This would enable us to retain the advantage of fast approximate string matching , thus being able to utilise larger lexical resources than if we were to calculate sophisticated alignments for each lexical entry .
Two formalizations of lexical rules as used by HPSG linguists have been proposed , the meta-level lexical rules ( MLRs ; Calcagno 1995 ; Calcagno and Pollard 1995 ) and the . description-level lexical rules ( DLRs ; Meurers 1995 ) .5 2.2.1 Meta-Level Lexical Rules . The MLR approach sees lexical rules in the more traditional sense as relations between lexical entries , i.e. , descriptions of word objects .
Deriving Translation Resources from Web-Based MT Systems All EBMT systems , from the initial proposal by Nagao ( 1984 ) to the recent collection of Carl and Way ( 2003 ) , are premised on the availability of subsentential alignments derived from the input bitext . There is a wealth of literature on trying to establish subsentential translations from a bilingual corpus .3 Kay and R ¨ oscheisen ( 1993 ) attempt to extract a bilingual dictionary using a hybrid method of sentence and word alignment on the assumption that the ( source , target ) words have a similar distribution .
This equality i = i ' can not be proven , but it may be reasonably assumed -- we know that in this case the infection i ' caused the illness , which , in turn , caused the death . The necessity of this kind of merging of arguments has been recognized before : Charniak and McDermott ( 1985 ) call it abductive unification/matching , Hobbs ( 1978 , 1979 ) refers to such operations using the terms knitting or petty conversational implicature . Neither Hobbs nor Charniak and McDermott tried then to make this notion precise , but the paper by Hobbs et al. ( 1988 ) moves in that direction .
Lexical rules have not gone unchallenged as a mechanism for expressing generalizations over lexical information . In a number of proposals , lexical generalizations are captured using lexical underspecification ( Kathol 1994 ; Krieger and Nerbonne 1992 ; Riehemann 1993 ; Oliva 1994 ; Frank 1994 ; Opalka 1995 ; Sanfilippo 1995 ) .
( 5 ) WN CLASS : For each keyword w shown in the right column of Table 1 , we determine whether the head noun of NPZ is a hyponym of w in WordNet , using only the first WordNet sense of NPZ .1 If so , we create a WN CLASS feature with w as its value . These keywords are potentially useful features because some of them are subclasses of the ACE SCs shown in the left column of Table 1 , while others appear to be correlated with these ACE SCs .2 ( 6 ) INDUCED CLASS : Since the first-sense heuristic used in the previous feature may not be accurate in capturing the SC of an NP , we employ a corpusbased method for inducing SCs that is motivated by research in lexical semantics ( e.g. , Hearst ( 1992 ) ) . Given a large , unannotated corpus3 , we use IdentiFinder to label each NE with its NE type and MINIPAR to extract all the appositive relations .
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 .
Withindocument coreference resolution has been applied to produce summaries of text surrounding occurrences of the name ( Bagga and Baldwin , 1998 ; Gooi and Allan , 2004 ) . 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 ) .
An additional possibility is to use word-types , such as a special tag for be-verbs , or for prepositions like ` of ' which attaches mainly to nouns ( Sekine and Grishman , 1995 ) . In a similar vain to Skut and Brants ( 1998 ) and Buchholz et al. ( 1999 ) , the method extends an existing flat shallow-parsing method to handle composite structures . It yields a significant improvement over the flat method , especially for long and more complex structures .
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 . 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 ) ) . 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 .
We built a two-stage baseline system , using the perceptron segmentation model from our previous work ( Zhang and Clark , 2007 ) and the perceptron POS tagging model from Collins ( 2002 ) . We use baseline system to refer to the system which performs segmentation first , followed by POS tagging ( using the single-best segmentation ) ; baseline segmentor to refer to the segmentor from ( Zhang and Clark , 2007 ) which performs segmentation only ; and baseline POStagger to refer to the Collins tagger which performs POS tagging only ( given segmentation ) .
The function α ( t ) maps a MeSH term to a positive score if the term is a positive indicator for that particular task type , or a negative score if the term is a negative indicator for the clinical task . Note that although our current system uses MeSH headings assigned by human indexers , manually assigned terms can be replaced with automatic processing if needed ( Aronson et al. 2004 ) . Below , we enumerate the relevant indicator terms by clinical task .