Datasets:

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citation_intent-tlm

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Figure 2 ( a ) shows the frame-based semantic representation for the utterance `` What time is Analyze This playing 2 See ( Nakatani and Chu-Carroll , 2000 ) for how MIMIC 's dialoguelevel knowledge is used to override default prosodic assignments for concept-to-speech generation .

For Berkeley system , we use the reported results from Durrett and Klein ( 2014 ) .

Instead , we will adopt the nomenclature of the Automatic Content Extraction program ( NIST , 2004 ) : we will call the instances of textual references to objects/abstractions mentions , which can be either named ( e.g. John Mayor ) , nominal ( the president ) or pronominal ( she , it ) .

Such a component would serve as the first stage of a clinical question answering system ( Demner-Fushman and Lin , 2005 ) or summarization system ( McKeown et al. , 2003 ) .

In the areas of Natural Language Processing ( NLP ) and computational linguistics , proposals have been made for using the computational Grid for data-intensive NLP and text-mining for eScience ( Carroll et al. , 2005 ; Hughes et al , 2004 ) .

Ingria ( 1984 ) comprehensively compares different approaches to complementation within grammatical theory providing a touchstone against which the LDOCE scheme can be evaluated .

To prove that our method is effective , we also make a comparison between the performances of our system and Xue and Palmer ( 2005 ) , Xue ( 2008 ) .

The obtained SCFG is further used in a phrase-based and hierarchical phrase-based system ( Chiang , 2007 ) .

32 In certain cases an extension of the constraint language with named disjunctions or contexted constraints ( Maxwell and Kaplan 1989 ; Eisele and Dorre 1990 ; Griffith 1996 ) can be used to circumvent constraint propagation .

Blunsom et al. ( 2008 , 2009 , 2010 ) utilized Bayesian methods to learn synchronous context free grammars ( SCFG ) from a parallel corpus .

Since the arguments can provide useful semantic information , the SRL is crucial to many natural language processing tasks , such as Question and Answering ( Narayanan and Harabagiu 2004 ) , Information Extraction ( Surdeanu et al. 2003 ) , and Machine Translation ( Boas 2002 ) .

In addition to the model based upon a dictionary of stems and words , we also experimented with models based upon character n-grams , similar to those used for Chinese segmentation ( Sproat et al. , 1996 ) .

Notable early papers on graph-based semisupervised learning include Blum and Chawla ( 2001 ) , Bansal et al. ( 2002 ) , Kondor and Lafferty ( 2002 ) , and Joachims ( 2003 ) .

Due to using a global model like CRFs , our previous work in ( Zhao et al. , 2006 ; Zhao and Kit , 2008c ) reported the best results over the evaluated corpora of Bakeoff-2 until now7 .

As already mentioned in the literature , see for example ( Lapata and Lascarides , 2003 ) , knowledge about implicit predicates could be potentially useful for a variety of NLP tasks such as language generation , information extraction , question answering or machine translation .

We use the Columbia Arabic Treebank ( CATiB ) ( Habash and Roth 2009 ) .

This problem may be similar to the situation in which current formal grammars allow nonsensical but parsable collections of words ( e.g. , `` colorless green ideas ... '' ) , while before the advent of Chomskyan formalisms , a sentence was defined as the smallest meaningful collection of words ; Fowler ( 1965 , p. 546 ) gives 10 definitions of a sentence .

Another technique for making better use of unlabeled data is cotraining ( Blum and Mitchell 1998 ) , in which two sufficiently different learners help each other learn by labeling training data for one another .

For example , modeling CASE in Czech improves Czech parsing ( Collins et al. 1999 ) : CASE is relevant , not redundant , and can be predicted with sufficient accuracy .

Table look-up using an explicit translation lexicon is sufficient and preferable for many multilingual NLP applications , including `` crummy '' MT on the World Wide Web ( Church & Hovy , 1993 ) , certain machine-assisted translation tools ( e.g. ( Macklovitch , 1994 ; Melamed , 1996b ) ) , concordancing for bilingual lexicography ( Catizone et al. , 1993 ; Gale & Church , 1991 ) , computerassisted language learning , corpus linguistics ( Melby .

Following our previous work ( Karamanis and Manurung 2002 ; Althaus , Karamanis , and Koller 2004 ) , the input to information ordering is an unordered set of informationbearing items represented as CF lists .

There has been some controversy , at least for simple stemmers ( Lovins , 1968 ; Porter , 1980 ) , about the effectiveness of morphological analysis for document retrieval ( Harman , 1991 ; Krovetz , 1993 ; Hull , 1996 ) .

This situation suggests a response-automation approach that follows the document retrieval paradigm ( Salton and McGill 1983 ) , where a new request is matched with existing response documents ( e-mails ) .

All current approaches to monolingual TE , either syntactically oriented ( Rus et al. , 2005 ) , or applying logical inference ( Tatu and Moldovan , 2005 ) , or adopting transformation-based techniques ( Kouleykov and Magnini , 2005 ; Bar-Haim et al. , 2008 ) , incorporate different types of lexical knowledge to support textual inference .

This is the approach taken by IBM Models 4 + ( Brown et al. 1993b ; Och and Ney 2003 ) , and more recently by the LEAF model ( Fraser and Marcu 2007 ) .

We have noted that many of these desiderata make complex question answering quite similar to multi-document summarization ( Lin and Demner-Fushman 2005b ) , but these features are also beyond the capabilities of current summarization systems .

Perlmutter and Soames , 1979:472 ) , but these are the only ones which are explicit in the LDOCE coding system .

This paper describes an approach for sharing resources in various grammar formalisms such as Feature-Based Lexicalized Tree Adjoining Grammar ( FB-LTAG1 ) ( Vijay-Shanker , 1987 ; Vijay-Shanker and Joshi , 1988 ) and Head-Driven Phrase Structure Grammar ( HPSG ) ( Pollard and Sag , 1994 ) by a method of grammar conversion .

These constructs correspond as directly as possible to properties of the linguistic structure that express them and are , to as small an extent as possible , dependent on the requirements of contextual resolution ( unlike , say , the metavariables of standard QLFs [ Alshawi and Crouch 1992 ] , or the labels of UDRS [ Reyle 1996 ] , which are motivated entirely by the mechanisms that operate on them after grammatical processing ) .

Prototypes of Internet search engines for linguists , corpus linguists and lexicographers have been proposed : WebCorp ( Kehoe and Renouf , 2002 ) , KWiCFinder ( Fletcher , 2004a ) and the Linguist 's Search Engine ( Kilgarriff , 2003 ; Resnik and Elkiss , 2003 ) .

Table look-up using an explicit translation lexicon is sufficient and preferable for many multilingual NLP applications , including `` crummy '' MT on the World Wide Web ( Church & Hovy , 1993 ) , certain machine-assisted translation tools ( e.g. ( Macklovitch , 1994 ; Melamed , 1996b ) ) , concordancing for bilingual lexicography ( Catizone et al. , 1993 ; Gale & Church , 1991 ) , computerassisted language learning , corpus linguistics ( Melby .

An off-the-shelf speech recognition device , a Nippon Electric Corporation DP-200 , was added to an existing natural language processing system , the Natural Language Computer ( NLC ) ( Ballard 1979 , Biermann and Ballard 1980 ) .

Some efforts have tackled tasks such as automatic image caption generation ( Feng and Lapata , 2010a ; Ordonez et al. , 2011 ) , text illustration ( Joshi et al. , 2006 ) , or automatic location identification of Twitter users ( Eisenstein et al. , 2010 ; Wing and Baldridge , 2011 ; Roller et al. , 2012 ) .

The system was trained on the Penn Treebank ( Marcus et al. , 1993 ) WSJ Sections 221 and tested on Section 23 ( Table 1 ) , same as used by Magerman ( 1995 ) , Collins ( 1997 ) , and Ratnaparkhi ( 1997 ) , and became a common testbed .

Briscoe and Carroll ( 1997 ) predefine 163 verbal subcategorization frames , obtained by manually merging the classes exemplified in the COMLEX ( MacLeod , Grishman , and Meyers 1994 ) and ANLT ( Boguraev et al. 1987 ) dictionaries and adding around 30 frames found by manual inspection .

Recently , several alternative , often quite sophisticated approaches to collective classification have been proposed ( Neville and Jensen , 2000 ; Lafferty et al. , 2001 ; Getoor et al. , 2002 ; Taskar et al. , 2002 ; Taskar et al. , 2003 ; Taskar et al. , 2004 ; McCallum and Wellner , 2004 ) .

Berstel and Reutenauer ( 1988 ) give a sufficiently general finite-state framework to allow this : weights may fall in any set K ( instead of R ) .

FBLTAG ( Vijay-Shanker , 1987 ; Vijay-Shanker and Joshi , 1988 ) is an extension of the LTAG formalism .

There are several variations of such a method ( Ballesteros and Croft , 1998 ; Pirkola , 1998 ; Hull 1997 ) .

12 In order to focus on the computational aspects of the covariation approach , in this paper we will not go into a discussion of the full lexical rule specification language introduced in Meurers ( 1995 ) .

Many investigators ( e.g. Allen 1976 ; Elowitz et al. 1976 ; Luce et al. 1983 ; Cahn 1988 ) have suggested that the poor prosody of synthetic speech , in comparison with natural speech , is the primary factor leading to difficulties in the comprehension of fluent synthetic speech .

This Principle of Finitism is also assumed by Johnson-Laird ( 1983 ) , Jackendoff ( 1983 ) , Kamp ( 1981 ) , and implicitly or explicitly by almost all researchers in computational linguistics .

We performed translation experiments with an implementation of the IBM-4 translation model ( Brown et al. , 1993 ) .

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 .

For the development of these lists we used a collection of texts of about 300,000 words derived from the New York Times ( NYT ) corpus that was supplied as training data for the 7th Message Understanding Conference ( MUC-7 ) ( Chinchor 1998 ) .

For the full parser , we use the one developed by Michael Collins ( Collins , 1996 ; Collins , 1997 ) -- one of the most accurate full parsers around .

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 ) .

Tbahriti et al. ( 2006 ) have demonstrated that differential weighting of automatically labeled sections can lead to improved retrieval performance .

The one-sided t-test ( Hull , 1993 ) at significance level 0.05 indicated that the improvement on Trec5C is not statistically significant .

mers ( Lovins , 1968 ; Porter , 1980 ) demonstrably improve retrieval performance .

It is therefore no surprise that early attempts at response automation were knowledge-driven ( Barr and Tessler 1995 ; Watson 1997 ; Delic and Lahaix 1998 ) .

The framework was originally developed for the realization of deep-syntactic structures in NLG ( Lavoie and Rambow , 1997 ) .

These features are very much desired in the design of an annotation scheme , in particular for the treatment of linguistic phenomena , which are non-standard , e.g. crossed dependencies ( Tang & Zaharin , 1995 ) .

Few approaches to parsing have tried to handle disfluent utterances ( notable exceptions are Core & Schubert , 1999 ; Hindle , 1983 ; Nakatani & Hirschberg , 1994 ; Shriberg , Bear , & Dowding , 1992 ) .

5 The open source Moses ( Hoang et al. 2007 ) toolkit from www.statmt.org/moses/ .

There have been several efforts aimed at developing a domain-independent method for generating responses from a frame representation of user requests ( Bobrow et al. , 1977 ; Chu-Carroll , 1999 ) .

The maximum entropy approach ( Berger et al. , 1996 ) presents a powerful framework for the combination of several knowledge sources .

Other molecular biology databases We also included several model organism databases or nomenclature databases in the construction of the dictionary , i.e. , mouse Mouse Genome Database ( MGD ) [ 18 ] , fly FlyBase [ 19 ] , yeast Saccharomyces Genome Database ( SGD ) [ 20 ] , rat -- Rat Genome Database ( RGD ) [ 21 ] , worm -- WormBase [ 22 ] , Human Nomenclature Database ( HUGO ) [ 23 ] , Online Mendelian Inheritance in Man ( OMIM ) [ 24 ] , and Enzyme Nomenclature Database ( ECNUM ) [ 25 , 26 ] .

We then use the program Snob ( Wallace and Boulton 1968 ; Wallace 2005 ) to cluster these experiences .

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 ) .

Our implementation of the NP-based QA system uses the Empire noun phrase finder , which is described in detail in Cardie and Pierce ( 1998 ) .

For all the experiments reported in this article , we used the training portion of PATB Part 3 v3 .1 ( Maamouri et al. 2004 ) , converted to the CATiB Treebank format , as mentioned in Section 2.5 .

In addition , we consider several types of lexical features ( LexF ) inspired by previous work on agreement and disagreement ( Galley et al. , 2004 ; Misra and Walker , 2013 ) .

All communicative head gestures in the videos were found and annotated with ANVIL using a subset of the attributes defined in the MUMIN annotation scheme ( Allwood et al. , 2007 ) .

Specifically , we examine the strength of association between the verb and the noun constituent of a combination ( the target expression or its lexical variants ) as an indirect cue to its idiomaticity , an approach inspired by Lin ( 1999 ) .

The current system learns finite state flowcharts whereas typical learning systems usually acquire coefficient values as in Minsky and Papert ( 1969 ) , assertional statements as in Michalski ( 1980 ) , or semantic nets as in Winston ( 1975 ) .

More recently , an alignment selection approach was proposed in ( Huang , 2009 ) , which computes confidence scores for each link and prunes the links from multiple sets of alignments using a hand-picked threshold .

Lin ( 1998 ) for example discusses a method where a syntactic parse of the text is performed and the context of a word is modeled using dependency triples .

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 ) ) .

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 ) ) .

7A11 our results are computed with the evalb program following the now-standard criteria in ( Collins , 1999 ) .

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 .

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 ) .

The system is implemented based on ( Galley et al. , 2006 ) and ( Marcu et al. 2006 ) .

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 ) .

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 ) .

Word alignments are used primarily for extracting minimal translation units for machine translation ( MT ) ( e.g. , phrases [ Koehn , Och , and Marcu 2003 ] and rules [ Galley et al. 2004 ; Chiang et al. 2005 ] ) as well as for

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 .

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

We use the structures previously used by Nguyen et al. ( 2009 ) , and propose one new structure .

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 .

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 ) .

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 .

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 ) .

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 .

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 ) .

Subsequent processing by the natural language and response generation components was done automatically by the computer ( Zue et al. 1989 ) .

This is implemented as a cascade of simple strategies , which were briefly described in Mikheev ( 1999 ) .

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 .

Better results would be expected by combining the PCFG-LA parser with discriminative reranking approaches ( Charniak and Johnson , 2005 ; Huang , 2008 ) for self training .

Intermedia is no more developed and nobody of us had the opportunity to try it ( Landow , 1994 ) .

Morphological alterations of a search term have a negative impact on the recall performance of an information retrieval ( IR ) system ( Choueka , 1990 ; J ¨ appinen and Niemist ¨ o , 1988 ; Kraaij and Pohlmann , 1996 ) , since they preclude a direct match between the search term proper and its morphological variants in the documents to be retrieved .

ment ( Sarkar and Wintner , 1999 ; Doran et al. , 2000 ; Makino et al. , 1998 ) .

Finally , feedback expressions ( head nods and shakes ) are successfully predicted from speech , prosody and eye gaze in interaction with Embodied Communication Agents as well as human communication ( Fujie et al. , 2004 ; Morency et al. , 2005 ; Morency et al. , 2007 ; Morency et al. , 2009 ) .

Another line of research approaches grounded language knowledge by augmenting distributional approaches of word meaning with perceptual information ( Andrews et al. , 2009 ; Steyvers , 2010 ; Feng and Lapata , 2010b ; Bruni et al. , 2011 ; Silberer and Lapata , 2012 ; Johns and Jones , 2012 ; Bruni et al. , 2012a ; Bruni et al. , 2012b ; Silberer et al. , 2013 ) .

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 . ''

Al-Adhaileh and Tang ( 2001 ) presented an approach for constructing a BKB based on the S-SSTC .

However , since work in this direction has started , a significant progress has also been made in the research on statistical learning of full parsers , both in terms of accuracy and processing time ( Charniak , 1997b ; Charniak , 1997a ; Collins , 1997 ; Ratnaparkhi , 1997 ) .

Also , advanced methods often require many training iterations , for example active learning ( Dagan and Engelson ,1995 ) and co-training ( Blum and Mitchell , 1998 ) .

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 .