hrithikpiyush/acl-arc huggingface dataset:
In these situations the parser can use the semantic type of the verb to compute this relationship . Expanding on a suggestion of Michiels ( 1982 ) , we classify verbs as Subject Equi , Object Equi , Subject Raising or Object Raising for each sense which has a predicate complement code associated with it . These terms , which derive from Transformational Grammar , are used as convenient labels for what we regard as a semantic distinction ; the actual output of the program is a specification of the mapping from superficial syntactic form to an underlying logical representation .
For the corpus statistics , see Table 1 . For more information on CATiB , see Habash and Roth ( 2009 ) and Habash , Faraj , and Roth ( 2009 ) .
2 Co-occurrence With the exception of ( Fung , 1995b ) , previous methods for automatically constructing statistical translation models begin by looking at word cooccurrence frequencies in bitexts ( Gale & Church , 1991 ; Kumano & Hirakawa , 1994 ; Fung , 1995a ; Melamed , 1995 ) . A bitext comprises a pair of texts in two languages , where each text is a translation of the other .
We instead propose a novel approach that extracts rules from packed forests ( Section 3 ) , which compactly encodes many more alternatives than kbest lists . Experiments ( Section 5 ) show that forestbased extraction improves BLEU score by over 1 point on a state-of-the-art tree-to-string system ( Liu et al. , 2006 ; Mi et al. , 2008 ) , which is also 0.5 points better than ( and twice as fast as ) extracting on 30-best parses . When combined with our previous orthogonal work on forest-based decoding ( Mi et al. , 2008 ) , the forest-forest approach achieves a 2.5 BLEU points improvement over the baseline , and even outperforms the hierarchical system of Hiero , one of the best-performing systems to date .
Take your own . '' Our approach to extract and classify social events builds on our previous work ( Agarwal and Rambow , 2010 ) , which in turn builds on work from the relation extraction community ( Nguyen et al. , 2009 ) . Therefore , the task of relation extraction is most closely related to the tasks addressed in this paper .
Since the language understanding module utilizes unification , a wide variety of linguistic phenomena can be covered . For example , speech repairs , particle omission , and fillers can be dealt with in the framework of unification grammar ( Nakano et al. , 1994 ; Nakano and Shimazu , 1999 ) . The language generation module features Common Lisp functions , so there is no limitation on the description .
The obtained SCFG is further used in a phrase-based and hierarchical phrase-based system ( Chiang , 2007 ) . Levenberg et al. ( 2012 ) employed a Bayesian method to learn discontinuous SCFG rules . This study differs from their work because we concentrate on constructing tree structures for tree-based translation models .
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 . 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 ) . 5.10 Sample Output
Each corpus was divided into four distinct data sets : a set from which we extract the tag dictionaries , a set of raw ( unannotated ) sentences , a development set , and a test set . We use the same splits as Garrette et al. ( 2014 ) . Since these treebanks use special representations for conjunctions , we chose to rewrite the trees to use conjunction categories of the form ( X \ X ) / X rather than introducing special conjunction rules .
feature Cohen 's k corrected k face segment 69.89 91.37 face annotate 71.53 94.25 head mov segment 71.21 91.75 head mov annotate 71.65 95.14 These results are slightly worse than those obtained in previous studies using the same annotation scheme ( Jokinen et al. , 2008 ) , but are still sat - isfactory given the high number of categories provided by the scheme .
The result is a flattened structure that more accurately reflects the prosodic phrasing . In Chomsky and Halle ( 1968 ) , this flattening process is not part of the grammar . Rather , it is viewed as '' .
Furthermore , the need to answer questions related to patient care at the point of service has been well studied and documented ( Covell , Uman , and Manning 1985 ; Gorman , Ash , and Wykoff 1994 ; Ely et al. 1999 , 2005 ) . MEDLINE , the authoritative repository of abstracts from the medical and biomedical primary literature maintained by the National Library of Medicine , provides the clinically relevant sources for answering physicians ' questions , and is commonly used in that capacity ( Cogdill and Moore 1997 ; De Groote and Dorsch 2003 ) . However , studies have shown that existing systems for searching MEDLINE ( such as PubMed , the search service provided by the National Library of Medicine ) are often inadequate and unable to supply clinically relevant answers in a timely manner ( Gorman , Ash , and Wykoff 1994 ; Chambliss and Conley 1996 ) .
His work is taxonomic , in that he performs detailed descriptive analyses of paragraphs . He lists , classifies , and discusses various types of inference , by which he means , generally , `` the linguistic-logical notions of consequent and presupposition '' Crothers ( 1979:112 ) have collected convincing evidence of the existence of language chunks -- real structures , not just orthographic conventions -- that are smaller than a discourse , larger than a sentence , generally composed of sentences , and recursive in nature ( like sentences ) . These chunks are sometimes called `` episodes , '' and sometimes `` paragraphs . ''
For example , CogNIAC ( Baldwin , 1997 ) , a system based on seven ordered heuristics , generates high-precision resolution ( over 90 % ) for some cases of pronominal reference . For this research , we used a coreference resolution system ( ( Harabagiu and Maiorano , 1999 ) ) that implements different sets of heuristics corresponding to various forms of coreference . This system , called COCKTAIL , resolves coreference by exploiting several textual cohesion constraints ( e.g. term repetition ) combined with lexical and textual coherence cues ( e.g. subjects of communication verbs are more likely to refer to the last person mentioned in the text ) .
Their goal is considerably different ; they want to incorporate additional features into their model and define an objective function which allows them to do so ; whereas , we are interested in allowing for multiple objective functions in order to adapt the parser model parameters to downstream tasks or alternative intrinsic ( parsing ) objectives . The work that is most similar to ours is that of Chang et al. ( 2007 ) , who introduced the Constraint Driven Learning algorithm ( CODL ) . Their algorithm specifically optimizes a loss function with
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 ? Maximum-likelihood estimation guesses 0ˆ to be the 0 maximizing Hi fθ ( xi , yi ) .
We use more complex context features . Fraser ( 2009 ) tried to solve the inflection prediction problem by simply building an SMT system for translating from stems to inflected forms . Bojar and Kos ( 2010 ) improved on this by marking prepositions with the case they mark ( one of the most important markups in our system ) .
This code was extensively optimized to take advantage of the very sparse sentence representation described above . As shown in ( Okanohara and Tsujii , 2007 ) , using this representation , a linear classifier can not distinguish sentences sampled from a trigram and real sentences . Therefore , we used a 3rd order polynomial kernel , which was found to give good results .
In this case , the complete event structure of a word can be compositionally derived from its component morphemes . This framework , where the `` semantic load '' is spread more evenly throughout the lexicon to lexical categories not typically thought to bear semantic content , is essentially the model advocated by Pustejovsky ( 1991a ) , among many others . Note that such an approach is no longer lexicalist : each lexical item does not fully encode its associated syntactic and semantic structures .
The number of frame types extracted ( i.e. , an elementary tree without a specific lexical anchor ) ranged from 2,366 to 8,996 . Xia ( 1999 ) also presents a similar method for the extraction of a TAG from the Penn Treebank . The extraction procedure consists of three steps : First , the bracketing of the trees in the Penn Treebank is corrected and extended based on the approaches of Magerman ( 1994 ) and Collins ( 1997 ) .
That is to say , the CURRENT-FOCUS is a feature , like verb-mode , that is blocked when an [ end ] node is encountered . To a first approximation , a CURRENT-FOCUS reaches only nodes that are c-commanded ( Chomsky 1977 ) by its generator . Finally , certain blocker nodes block the transfer of the FLOAT-OBJECT to their children .
We propose a unified approach that solves both problems simultaneously . A previous work along this line is Sproat et al. ( 1996 ) , which is based on weighted finite-state transducers ( FSTs ) . Our approach is similarly motivated but is based on a different mechanism : linear mixture models .
A powerful strategy for using gains is to build a model iteratively by adding at each step the feature which gives the highest gain with respect to those already added . Berger et al ( 1996 ) describe an efficient algorithm for accomplishing this in which approximations to Pst ( TIS ) are computed in parallel for all ( new ) features ft by holding all weights in the existing model fixed and optimizing only over a8t . However , this method requires many expensive passes over the corpus to optimize the weights for the set of features under consideration at each step , and it adds only one feature per step , so it is not practical for constructing models containing thousands of features or more .
However , if we assume , for example , that we already have word equations for four languages , and all we want is to compute the translations into a fifth language , then we can simply extend our approach to what we call the product-of-ranks algorithm . As suggested in Rapp & Zock ( 2010 ) this can be done by looking up the ranks of each of the four given words ( i.e. the words occurring in a particular word equation ) within the association vector of a translation candidate , and by multiplying these ranks . So for each candidate we obtain a product of ranks .
This article represents an extension of our previous work on unsupervised event coreference resolution ( Bejan et al. 2009 ; Bejan and Harabagiu 2010 ) . In this work , we present more details on the problem of solving both withinand cross-document event coreference as well as describe a generic framework for solving this type of problem in an unsupervised way .
There have in fact been attempts to develop modified modular designs that allow generators to handle interactions between the components . These include devices such as interleaving the components ( McDonald 1983 ; Appelt 1983 ) , backtracking on failure ( Appelt 1985 ; Nogier 1989 ) , allowing the linguistic component to interrogate the planner ( Mann 1983 ; Sondheimer and Nebel 1986 ) , and Hovy 's notion of restrictive ( i.e. , bottom-up ) planning ( Hovy 1988a , 1988c ) . All of these approaches , though , require that potential interactions be determined either by the tactical component or by the system designer in advance .
Magic is a compilation technique originally developed for goal-directed bottom-up processing of logic programs . See , among others , ( Ramakrishnan et al. 1992 ) . As shown in ( Minnen , 1996 ) • The presented research was carried out at the University of Tubingen , Germany , as part of the Sonderforschungsbereich 340 .
Table 2 shows the average parsing time with the LTAG and HPSG parsers . In Table 2 , lem refers to the LTAG parser ( Sarkar et al. , 2000 ) , ANSI C implementation of the two-phase parsing algorithm that performs the head corner parsing ( van Noord , 1994 ) without features ( phase 1 ) , and then executes feature unification ( phase 2 ) . TNT refers to the HPSG parser ( Torisawa et al. , 2000 ) , C++ implementation of the two-phase parsing algorithm that performs filtering with a compiled CFG ( phase 1 ) and then executes feature unification ( phase 2 ) .
Our U-trees are learned based on STSG , which is more appropriate for tree-based translation models than SCFG . Burkett and Klein ( 2008 ) and Burkett et al. ( 2010 ) focused on joint parsing and alignment . They utilized the bilingual Tree-bank to train a joint model for both parsing and word alignment .
When Gutenberg invented the printing press and Aldo Manuzio invented the book as we know it , new forms of writings arose . For example , when books should n't be copied by hand any longer , authors took the advantage and start writing original books and evaluation -- i.e. literary criticism -- unlike in the previous times ( Eisenstein , 1983 ) . Nowadays the use of computers for writing has drammatically changed , expecially after their interconnection via the internet , since at least the foundation of the web ( Berners-Lee , 1999 ) .
In typical question-answer systems , the user has the initiative when asking questions and the system has it when answering . Since the language generation module works in parallel with the language understanding module , utterance generation is possible even while the system is listening to user utterances and that utterance understanding is possible even while it is speaking ( Nakano et al. , 1999a ) . Thus the system can respond immediately after user pauses when the user has the initiative .
Tesni`ere ( 1959 ) , who proposes a grouping of verb arguments into actants and circumstances , gives a set of rules to connect specific types of actants -- for example , agent or instrument -- to such grammatical elements as subject , direct object , indirect object . This idea was expanded to include nouns and their modifiers through verb nominalizations ( Chomsky , 1970 ; Quirk et al. , 1985 ) . We work with sentences , clauses , phrases and words , using syntactic structures generated by a parser .
Our final experiments verify this point and we will conduct a much detailed analysis in future . 9 We only use the minimal GHKM rules ( Galley et al. , 2004 ) here to reduce the complexity of the sampler . Under this initial STSG , the sampler modifies the initial U-trees ( initial sample ) to create a series of new ones ( new samples ) by the Gibbs operators .
From the master LDOCE file , we have computed alternative indexing information , which allows access into the dictionary via different routes . In addition to headwords , dictionary search through the pronunciation field is available ; Carter ( 1987 ) has merged information from the pronunciation and hyphenation fields , creating an enhanced phonological representation which allows access to entries by broad phonetic class and syllable structure ( Huttenlocher and Zue , 1983 ) . In addition , a fully flexible access system allows the retrieval of dictionary entries on the basis of constraints specifying any combination of phonetic , lexical , syntactic , and semantic information ( Boguraev et al. , 1987 ) .
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 ) . 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 ) .
Much recent research in SMT , eg ( Garcia-Varea et al. , 1998 ; Niessen et al. , 1998 ; Och et al. , 1999 ; Wang and Waibel , 1998 ) deals with the decoding problem , either directly or indirectly because of constraints imposed on the form of the translation model . A statistical technique which has recently become popular for NLP is Maximum Entropy/Minimum Divergence ( MEMD ) modeling ( Berger et al. , 1996 ) . One of the main strengths of MEMD is that it allows information from different sources to be combined in a principled and effective way , so it is a natural choice for modeling p ( wlh , s ) In this paper , I describe a MEMD model for p ( wlh , s ) and compare its performance to that of an equivalent linear model .
Our baseline coreference system uses the C4 .5 decision tree learner ( Quinlan , 1993 ) to acquire a classifier on the training texts for determining whether two NPs are coreferent . Following previous work ( e.g. , Soon et al. ( 2001 ) and Ponzetto and Strube ( 2006 ) ) , we generate training instances as follows : a positive instance is created for each anaphoric NP , NPj , and its closest antecedent , NPi ; and a negative instance is created for NPj paired with each of the intervening NPs , NPi +1 , NPi +2 , ... , NPj_1 .
Note that in this figure the alignment between x5 = suggests from the hypothesis and u4 = opinion from the conversation segment is a pseudo alignment , which directly maps a verb term in the hypothesis to an utterance term represented by its dialogue act . This alignment is obtained by following the same set of rules learned from the development dataset as in ( Zhang and Chai , 2009 ) .
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 . When the CL3M coreference system uses gold mentions or heads , we call the system Gold ; when it uses predicted mentions or heads , we call the system Predicted .
The best performance on the WSJ corpus was achieved by a combination of the SATZ system ( Palmer and Hearst 1997 ) with the Alembic system ( Aberdeen et al. 1995 ) : a 0.5 % error rate . The best performance on the Brown corpus , a 0.2 % error rate , was reported by Riley ( 1989 ) , who trained a decision tree classifier on a 25-million-word corpus . In the disambiguation of capitalized words , the most widespread method is POS tagging , which achieves about a 3 % error rate on the Brown corpus and a 5 % error rate on the WSJ corpus , as reported in Mikheev ( 2000 ) .
Bolter ( 1991 ) was the first scholar who stressed the impact of the digital revolution to the medium of writing . Terms as ` chapter ' , ` page ' or ` footnote ' simply become meaningless in the new texts , or they highly change their meaning .
There is now a consensus about the fact that natural language should be described as correspondences between different levels of representation . Much of theoretical linguistics can be formulated in a very natural manner as stating correspondences ( translations ) between layers of representation structures ( Rambow & Satta , 1996 ) . In this paper , a flexible annotation schema called Structured String-Tree Correspondence ( SSTC ) ( Boitet & Zaharin , 1988 ) will be introduced to capture a natural language text , its corresponding abstract linguistic representation and the mapping ( correspondence ) between these two .
The lexicon is derived by a fully automatic extraction method which utilizes a clustering technique called tree-cut ( Li and Abe , 1998 ) . In our previous work ( Tomuro , 2000 ) , we applied this method to a small subset of WordNet nouns and showed potential applicability . In the current work , we applied the method to all nouns and verbs in WordNet , and built a lexicon in which word senses are partitioned by systematic polysemy .
the mention class ( generic , specific , negatively quantified , etc. ) 4 . the mention sub-type , which is a sub-category of the mention type ( ACE , 2004 ) ( e.g. OrgGovernmental , FacilityPath , etc. ) .
The particle `` up '' is featurised into the verb `` pick '' and in discontinuous manner ( e.g. `` up '' ( 4-5 ) in `` pick-up '' ( 1-2 +4 -5 ) ) in the sentence `` He picks the box up '' . For more details on the proprieties of SSTC , see Boitet & Zaharin ( 1988 ) .
mers ( Lovins , 1968 ; Porter , 1980 ) demonstrably improve retrieval performance . This has been reported for other languages , too , dependent on the generality of the chosen approach ( J ¨ appinen and Niemist ¨ o , 1988 ; Choueka , 1990 ; Popovic and Willett , 1992 ; Ekmekc ¸ ioglu et al. , 1995 ; Hedlund et al. , 2001 ; Pirkola , 2001 ) . 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 .
Hence , throughout the course of this project , the different methods had minor implementational variations , which do not affect the overall insights of this research . Specifically , we used Decision Graphs ( Oliver 1993 ) for Doc-Pred , and SVMs ( Vapnik 1998 ) for Sent-Pred .11 Additionally , we used unigrams for clustering documents and sentences , and unigrams and bigrams for predicting document clusters and sentence clusters ( Sections 3.1.2 and 3.2.2 ) . Because this variation was uniformly implemented for both approaches , it does not affect their relative performance .
The corpus is a subset of the newscommentary dataset provided at WMT4 and contains input French sentences , MT outputs , postedited outputs and English references . To prepare SMT outputs for post-editing , the creators of the corpus used their own WMT10 system ( Potet et al. , 2010 ) , based on the Moses phrase-based decoder ( Koehn et al. , 2007 ) with dense features . We replicated a similar Moses system using the same monolingual and parallel data : a 5-gram language model was estimated with the KenLM toolkit ( Heafield , 2011 ) on news.en data ( 48.65 M sentences , 1.13 B tokens ) , pre-processed with the tools from the cdec toolkit ( Dyer et al. , 2010 ) .
To model o ( Li , S → T ) , o ( Ri , S → T ) , i.e. the reordering of the neighboring phrases of a function word , we employ the orientation model introduced by Setiawan et al. ( 2007 ) . Formally , this model takes the form of probability distribution Pori ( o ( Li , S → T ) , o ( Ri , S → T ) | Yi , S → T ) , which conditions the reordering on the lexical identity of the function word alignment ( but independent of the lexical identity of its neighboring phrases ) .