hrithikpiyush/acl-arc huggingface dataset:
A variety of statistical methods were proposed over the recent years for learning to produce a full parse of free-text sentences ( e.g. , Bod ( 1992 ) , Magerman ( 1995 ) , Collins ( 1997 ) , Ratnaparkhi ( 1997 ) , and Sekine ( 1998 ) ) . In parallel , a lot of work is being done on shallow parsing ( Abney , 1991 ; Greffenstette , 1993 ) , focusing on partial analysis of sentences at the level of local phrases and the relations between them .
rent discourse goal , we developed alternative strategies for achieving the goals in Figure 4 based on initiative distribution , as shown in Table 1 . The strategies employed when MIMIC has only dialogue initiative are similar to the mixed initiative dialogue strategies employed by many existing spoken dialogue systems ( e.g. , ( Bennacef et at. , 1996 ; Stent et al. , 1999 ) ) . To instantiate an attribute , MIMIC adopts the InfoSeek dialogue act to solicit the missing information .
Weight decay regularization was applied at the beginning of training but reduced to zero by the end of training . 7A11 our results are computed with the evalb program following the now-standard criteria in ( Collins , 1999 ) . tical left-corner parser ( Manning and Carpenter , 1997 ) , and a PCFG ( Charniak , 1997 ) .
This requires resolving sentence boundaries before tagging . We see no good reason , however , why such text spans should necessarily be sentences , since the majority of tagging paradigms ( e.g. , Hidden Markov Model [ HMM ] [ Kupiec 1992 ] , Brill 's [ Brill 1995a ] , and MaxEnt [ Ratnaparkhi 1996 ] ) do not attempt to parse an entire sentence and operate only in the local window of two to three tokens . The only reason why taggers traditionally operate on the sentence level is that a sentence naturally represents a text span in which POS information does not depend on the previous and following history .
Horizontal axis : average number of transferred edges per sentence . results are based on a corpus of movie subtitles ( Tiedemann 2007 ) , and are consequently shorter sentences , whereas the En → Es results are based on a corpus of parliamentary proceedings ( Koehn 2005 ) . We see in Figure 10 that for both domains , the models trained using posterior regularization perform better than the baseline model trained using EM .
The translation system used for testing the effectiveness of our U-trees is our in-house stringto-tree system ( abbreviated as s2t ) . 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 .
More sophisticated approaches have been proposed ( Hillard et al. , 2003 ) , including an extension that , in an interesting reversal of our problem , makes use of sentimentpolarity indicators within speech segments ( Galley et al. , 2004 ) . Also relevant is work on the general problems of dialog-act tagging ( Stolcke et al. , 2000 ) , citation analysis ( Lehnert et al. , 1990 ) , and computational rhetorical analysis ( Marcu , 2000 ; Teufel and Moens , 2002 ) . We currently do not have an efficient means to encode disagreement information as hard constraints ; we plan to investigate incorporating such information in future work .
Belief propagation improves non-projective dependency parsing with features that would make exact inference intractable . For projective parsing , it is significantly faster than exact dynamic programming , at the cost of small amounts of search error , We are interested in extending these ideas to phrase-structure and lattice parsing , and in trying other higher-order features , such as those used in parse reranking ( Charniak and Johnson , 2005 ; Huang , 2008 ) and history-based parsing ( Nivre and McDonald , 2008 ) . We could also introduce new variables , e.g. , nonterminal refinements ( Matsuzaki et al. , 2005 ) , or secondary links Mid ( not constrained by TREE/PTREE ) that augment the parse with representations of control , binding , etc. ( Sleator and Temperley , 1993 ; Buch-Kromann , 2006 ) .
Work has also been done on prosody and gestures in the specific domain of map-task dialogues , also targeted in this paper . Sridhar et al. ( 2009 ) obtain promising results in dialogue act tagging of the Switchboard-DAMSL corpus using lexical , syntactic and prosodic cues , while Gravano and Hirschberg ( 2009 ) examine the relation between particular acoustic and prosodic turn-yielding cues and turn taking in a large corpus of task-oriented dialogues . Louwerse et al. ( 2006 ) and Louwerse et al. ( 2007 ) study the relation between eye gaze , facial expression , pauses and dialogue structure in annotated English map-task dialogues ( Anderson et al. , 1991 ) and find correlations between the various modalities both within and across speakers .
In the shorter term , we intend to make the extracted subcategorization lexicons from Penn-II and Penn-III available as a downloadable public-domain research resource . We have also applied our more general unification grammar acquisition methodology to the TIGER Treebank ( Brants et al. 2002 ) and Penn Chinese Treebank ( Xue , Chiou , and Palmer 2002 ) , extracting wide-coverage , probabilistic LFG grammar approximations and lexical resources for German ( Cahill et al. 2003 ) and Chinese ( Burke , Lam , et al. 2004 ) .
Now we present the `` discrete '' structures followed by the kernel we used . We use the structures previously used by Nguyen et al. ( 2009 ) , and propose one new structure . Although we experimented with all of their structures ,3 here we only present the ones that perform best for our classification task .
Robinson , 1982 ; Bobrow , 1978 ) consult relatively small lexicons , typically generated by hand . Two exceptions to this generalisation are the Linguistic String Project ( Sager , 1981 ) and the IBM CRITIQUE ( formerly EPISTLE ) Project ( Heidorn et al. , 1982 ; Byrd , 1983 ) ; the former employs a dictionary of approximately 10,000 words , most of which are specialist medical terms , the latter has well over 100,000 entries , gathered from machine readable sources . In addition , there are a number of projects under way to develop substantial lexicons from machine readable sources ( see Boguraev , 1986 for details ) .
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 . A number of proposals in the 1990s deliberately limited the extent to which they relied on domain and/or linguistic knowledge and reported promising results in knowledge-poor operational environments ( Dagan and Itai 1990 , 1991 ; Lappin and Leass 1994 ; Nasukawa 1994 ; Kennedy and Boguraev 1996 ; Williams , Harvey , and Preston 1996 ; Baldwin 1997 ; Mitkov 1996 , 1998b ) . The drive toward knowledge-poor and robust approaches was further motivated by the emergence of cheaper and more reliable corpus-based NLP tools such as partof-speech taggers and shallow parsers , alongside the increasing availability of corpora and other NLP resources ( e.g. , ontologies ) .
Hence , it is very much desired to define the correspondence in a way to be able to handle the non-standard cases ( e.g. non-projective correspondence ) , see Figure 1 . Towards this aim , a flexible annotation structure called Structured String-Tree Correspondence ( SSTC ) was introduced in Boitet & Zaharin ( 1988 ) to record the string of terms , its associated representation structure and the mapping between the two , which is expressed by the sub-correspondences recorded as part of a SSTC . 1 The Meaning-Text Theory ( MTT ) was put forward in ( Zolkovski & Mel ` cuk ( 1965 ) , in the framework of research in Machine translation .
We have built an experimental text-to-speech system that uses our analysis of prosody to generate phrase boundaries for the Olive -- Liberman synthesizer ( Olive and Liberman 1985 ) . Two concerns motivated our implementation .
For languages with rich derivational morphology , this problem is often critical : the standard architectural view of morphological analysis as a preprocessor presents difficulties in handling semantically meaningful affixes . In this paper , I present a computational implementation of Distributed Morphology ( Halle and Marantz , 1993 ) , a non-lexicalist linguistic theory that erases the distinction between syntactic derivation and morphological derivation . This framework leads to finer-grained semantics capable of better capturing linguistic generalizations .
Some recent GRE algorithms have done away with the separation between content determination and linguistic realization , interleaving the two processes instead ( Stone and Webber 1998 ; Krahmer and Theune 2002 ) . We have separated the two phases because , in the case of vague descriptions , interleaving would tend to be difficult .
Instead , an experimenter in a separate room typed in the utterances as spoken by the subject . Subsequent processing by the natural language and response generation components was done automatically by the computer ( Zue et al. 1989 ) . We were able to collect a total , of nearly 5000 utterances in this fashion .
We tackle capitalized words in a similar fashion as we tackled the abbreviations : through a document-centered approach that analyzes on the fly the distribution of ambiguously capitalized words in the entire document . This is implemented as a cascade of simple strategies , which were briefly described in Mikheev ( 1999 ) .
So we will use a very simple formalism , like the one above , resembling the standard first order language . But , obviously , there are other possibilities -- for instance , the discourse representation structures ( DRS 's ) of Kamp ( 1981 ) , which have been used to translate a subset of English into logical formulas , to model text ( identified with a list of sentences ) , to analyze a fragment of English , and to deal with anaphora . The logical
We conjecture based on our analysis that the EM training algorithm is able to exploit the information available in both gold and automatically labeled data with more complex grammars while being less affected by over-fitting . Better results would be expected by combining the PCFG-LA parser with discriminative reranking approaches ( Charniak and Johnson , 2005 ; Huang , 2008 ) for self training . Self-training should also benefit other discriminatively trained parsers with latent annotations ( Petrov and Klein , 2008 ) , although training would be much slower compared to using generative models , as in our case .
In fact , they were used expecially in academic writing with some success . Intermedia is no more developed and nobody of us had the opportunity to try it ( Landow , 1994 ) . Storyspace is currently distributed by Eastgate ( 2005 ) , and we have used it for a time .
1In this paper , we use the term LTAG to refer to FBLTAG , if not confusing . ment ( Sarkar and Wintner , 1999 ; Doran et al. , 2000 ; Makino et al. , 1998 ) . These works are restricted to each closed community , and the relation between them is not well discussed .
see that we can widen a highway , we learn that we can also widen a sidewalk , bridge , runway , etc. . We also made use of the person-name/instance pairs automatically extracted by Fleischman et al. ( 2003 ) .2 This data provides counts for pairs such as `` Edwin Moses , hurdler '' and `` William Farley , industrialist . '' We have features for all concepts and therefore learn their association with each verb .
S-SSTC very well suited for the construction of a BKB , which is needed for the EBMT applications . Al-Adhaileh and Tang ( 2001 ) presented an approach for constructing a BKB based on the S-SSTC . In S-SSTC , the synchronous correspondence is defined in a way to ensure a flexible representation for both lexical and structural correspondences : iNode -- to -- node correspondence ( lexical correspondence ) , which is recorded in terms of pair of intervals ( Xs , Xt ) where Xs and Xt is SNODE interval/s for the source and the target SSTC respectively , iiSubtree -- to -- Subtree correspondence ( structural correspondence ) , which is very much needed for relating the two different languages at a level higher than the lexical level , a level of phrases .
Efficient training is required because the amount of data available for training will increase significantly . Also , advanced methods often require many training iterations , for example active learning ( Dagan and Engelson ,1995 ) and co-training ( Blum and Mitchell , 1998 ) . Processing text needs to be extremely efficient since many new applications will require very large quantities of text to be processed or many smaller quantities of text to be processed very quickly .
Griffiths et al. ( 2007 ) helped pave the path for cognitive-linguistic multimodal research , showing that Latent Dirichlet Allocation outperformed Latent Semantic Analysis ( Deerwester et al. , 1990 ) in the prediction of association norms . Andrews et al. ( 2009 ) furthered this work by showing that a bimodal topic model , consisting of both text and feature norms , outperformed models using only one modality on the prediction of association norms , word substitution errors , and semantic interference tasks . In a similar vein , Steyvers ( 2010 ) showed that a different feature-topic model improved predictions on a fill-in-the-blank task .
However , since the models tested apparently differed in other aspects , it is hard to determine how much of this gain can be attributed to the use of ME . It can be shown ( Berger et al. , 1996 ) that the use of this model with maximum likelihood parameter estimation is justified on information-theoretic grounds when q represents some prior knowledge about the true distribution and when the expected values of f in the training corpus are identical to their true expected values .3 There is no requirement that the components of f represent disjoint or statistically independent events . This result motivates the use of MEMD models , but it offers only weak guidance on how to select q or f .
This system extracts relational information , such as `` who is where '' or `` who bought what '' , from issues of the Wall Street Journal ( source code and sample data are included on the CD-ROM ) . Shortly after the publication of The Sound Pattern of English ( Chomsky and Halle 1968 ) , Kornai points out , `` Johnson ( 1970 ) demonstrated that the context-sensitive machinery of SPE ... [ could ] be replaced by a much simpler one , based on finite-state transducers ( FSTs ) ; the same conclusion was reached independently by Kaplan and Kay , whose work remained an underground classic until it was finally published in Kaplan and Kay ( 1994 ) . '' These works inspired Koskenniemi 's two-level system , and the Xerox rule compiler ( Dalrymple et al. 1987 ) .
17 We also applied the manipulations described in Section A. 3 to FNNUM , giving us the variants FNNUMDGT and FNNUMDGTBIN , which we tested similarly . 18 In this article , we use a newer version of the corpus by Alkuhlani and Habash ( 2011 ) than the one we used in Marton , Habash , and Rambow ( 2011 ) . 19 The paper by Alkuhlani and Habash ( 2012 ) presents additional , more sophisticated models that we do not use in this article .
After embedding the secret message , modified deep structure forms are converted into the surface structure format via language generation tools . Atallah et al. ( 2001b ) and Topkara et al. ( 2006a ) attained the embedding capacity of 0.5 bits per sentence with the syntactic transformation method .
lattice ) . Another possibility that often works better is to use Minimum Bayes-Risk ( MBR ) decoding ( Kumar and Byrne 2002 ; Liang , Taskar , and Klein 2006 ; Ganchev , and Taskar 2007 ) . Using this decoding we include an alignment link i j if the posterior probability that word i aligns to word j is above some threshold .
In LFG , the subcategorization requirements of a particular predicate are expressed by its semantic form : FOCUS ( ( r SUBJ ) ( r OBLon ) ) in Figure 1 . The subcategorization requirements expressed by semantic forms are enforced at f-structure level through completeness and coherence well-formedness conditions on f-structure ( Kaplan and Bresnan 1982 ) : An f-structure is locally complete iff it contains all the governable grammatical functions that its predicate governs . An f-structure is complete iff it and all its subsidiary f-structures are locally complete .
Our aim is to use the analysis of hypertexts for interesting insights , useful for blogs and wikis too . Following the example of Landow ( 1994 ) , we will call the autonomous units of a hypertext lexias ( from ` lexicon ' ) , a word coined by Roland Barthes ( 1970 ) . Consequently , a hypertext is a set of lexias .
Stephanie Seneff TINA : A Natural Language System for Spoken Language Applications subject-tagging for verbs , and long distance movement ( often referred to as gaps , or the trace , as in '' ( which article ) , do you think I should read ( t1 ) ? '' ) ( Chomsky 1977 ) . The gap mechanism resembles the Hold register idea of ATNs ( Woods 1970 ) and the treatment of bounded domination metavariables in lexical functional grammars ( LFGs ) ( Bresnan 1982 , p. 235 ff . )
Feature selection techniques have been widely used in information retrieval as a means for coping with the large number of words in a document ; a selection is made to keep only the more relevant words . Various feature selection techniques have been used in automatic text categorization ; they include document frequency ( DF ) , information gain ( IG ) ( Tzeras and Hartman , 1993 ) , minimum description length principal ( Lang , 1995 ) , and the X2 statistic . ( Yang and Pedersen , 1997 ) has found strong correlations between DF , IG and the X2 statistic for a term .
Politically-oriented text Sentiment analysis has specifically been proposed as a key enabling technology in eRulemaking , allowing the automatic analysis of the opinions that people submit ( Shulman et al. , 2005 ; Cardie et al. , 2006 ; Kwon et al. , 2006 ) . There has also been work focused upon determining the political leaning ( e.g. , `` liberal '' vs. `` conservative '' ) of a document or author , where most previously-proposed methods make no direct use of relationships between the documents to be classified ( the `` unlabeled '' texts ) ( Laver et al. , 2003 ; Efron , 2004 ; Mullen and Malouf , 2006 ) .
Consider , for example , the case of questions . Petrov et al. ( 2010 ) observed that dependency parsers tend to do quite poorly when parsing questions due to their limited exposure to them in the news corpora from the PennTreebank . Table 2 shows the accuracy of two parsers ( LAS , UAS and the F1 of the root dependency attachment ) on the QuestionBank test data .
The DSP parameters for eat , for example , place high weight on features like Pr ( nlbraise ) , Pr ( nlration ) , and Pr ( nlgarnish ) . Lin ( 1998a ) 's similar word list for eat misses these but includes sleep ( ranked 6 ) and sit ( ranked 14 ) , because these have similar subjects to eat . Discriminative , context-specific training seems to yield a better set of similar predicates , e.g. the highest-ranked contexts for DSP „ oo , on the verb join ,3 lead 1.42 , rejoin 1.39 , form 1.34 , belong to 1.31 , found 1.31 , quit 1.29 , guide 1.19 , induct 1.19 , launch ( subj ) 1.18 , work at 1.14 give a better SIMS ( join ) for Equation ( 1 ) than the top similarities returned by ( Lin , 1998a ) : participate 0.164 , lead 0.150 , return to 0.148 , say 0.143 , rejoin 0.142 , sign 0.142 , meet 0.142 , include 0.141 , leave 0.140 , work 0.137 Other features are also weighted intuitively .
More specifically , the notion of the phrasal lexicon ( used first by Becker 1975 ) has been used successfully in a number of areas : • Learnability ( Zernik and Dyer 1987 ) • Text generation ( Hovy 1988 ; Milosavljevic , Tulloch , and Dale 1996 ) • Speech generation ( Rayner and Carter 1997 ) • Localization ( Sch ¨ aler 1996 ) More recently , Simard and Langlais ( 2001 ) have proposed the exploitation of TMs at a subsentential level , while Carl , Way , and Sch ¨ aler ( 2002 ) and Sch ¨ aler , Way , and Carl ( 2003 , pages 108 -- 109 ) describe how phrasal lexicons might come to occupy a central place in a future hybrid integrated translation environment .
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 ) . For example , a ` web page ' is more similar to an infinite canvas than a written page ( McCloud , 2001 ) . Moreover , what seems to be lost is the relations , like the texture underpinning the text itself .
Typed feature grammars can be used as the basis for implementations of Head-driven Phrase Structure Grammar ( Pollard and Sag , 1994 ) .3 ( Meurers and Minnen , 1997 ) propose a compilation of lexical rules into TIT definite clauses 2This view of typed feature structures differs from the perspective on typed feature structures as modeling partial information as in ( Carpenter , 1992 ) . Typed feature structures as normal form TFC terms are merely syntactic objects .
Then the system could propose to a human expert a set of filters for each node , based on its observations , and the human could make the final decision on whether to accept the proposals . This approach resembles the work by Grishman et al. ( 1986 ) and Hirschman et al. ( 1975 ) on selectional restrictions . The semantic conditions that pass could even ultimately be associated with probabilities , obtained by frequency counts on their occurrences .
LTAG ( Schabes et al. , 1988 ) is a grammar formalism that provides syntactic analyses for a sentence by composing elementary trees with two opera - tions called substitution and adjunction .
It consists of 1600 pairs derived from the RTE3 development and test sets ( 800 +800 ) . Translations have been generated by the CrowdFlower3 channel to Amazon Mechanical Turk4 ( MTurk ) , adopting the methodology proposed by ( Negri and Mehdad , 2010 ) . The method relies on translation-validation cycles , defined as separate jobs routed to MTurk 's workforce .
The aim of this work is to gain some insight as to whether Arabic document categorization ( using NB ) is sensitive to the root extraction algorithm used or to different data sets . This work is a continuation of that initiated in ( Yahyaoui , 2001 ) , which reports an overall NB classification correctness of 75.6 % , in cross validation experiments , on a data set that consists of 100 documents for each of 12 categories ( the data set is collected from different Arabic portals ) . A 50 % overall classification accuracy is also reported when testing with a separately collected evaluation set ( 3 documents for each of the 12 categories ) .
For unsupervised tree structure induction , DeNero and Uszkoreit ( 2011 ) adopted a parallel parsing model to induce unlabeled trees of source sentences for syntactic pre-reordering . Our previous work ( Zhai et al. , 2012 ) designed an EMbased method to construct unsupervised trees for tree-based translation models . This work differs from the above work in that we design a novel Bayesian model to induce unsupervised U-trees , and prior knowledge can be encoded into the model more freely and effectively .
To build the above s2t system , we first use the parse tree , which is generated by parsing the English side of the bilingual data with the Berkeley parser ( Petrov et al. , 2006 ) . Then , we binarize the English parse trees using the head binarization approach ( Wang et al. , 2007 ) and use the resulting binary parse trees to build another s2t system . For the U-trees , we run the Gibbs sampler for 1000 iterations on the whole corpus .