hrithikpiyush/acl-arc huggingface dataset:
inter-document references in the form of hyperlinks ( Agrawal et al. , 2003 ) . Notable early papers on graph-based semisupervised learning include Blum and Chawla ( 2001 ) , Bansal et al. ( 2002 ) , Kondor and Lafferty ( 2002 ) , and Joachims ( 2003 ) . Zhu ( 2005 ) maintains a survey of this area .
As full features are used , the former and the latter provide the similar performance . 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 . Though those results are slightly better than the results here , we still see that the results of character-level dependency parsing approach ( Scheme E ) are comparable to those state-of-the-art ones on each evaluated corpus .
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 . Many applications of semantic relations in NLP are connected to paraphrasing or query expansion , see for example ( Voorhees , 1994 ) .
We use the Columbia Arabic Treebank ( CATiB ) ( Habash and Roth 2009 ) . Specifically , we use the portion converted from Part 3 of the PATB to the CATiB format , which enriches the CATiB dependency trees with full PATB morphological information .
Still , our definition of coherence may not be restrictive enough : two collections of sentences , one referring to `` black '' ( about black pencils , black pullovers , and black poodles ) , the other one about `` death '' ( war , cancer , etc. ) , connected by a sentence referring to both of these , could be interpreted as one paragraph about the new , broader topic `` black + death . '' 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 . It then seems worth differentiating between the creation of a new concept like `` black + death , '' with a meaning given by a paraphrase of the example collection of sentences , and the acceptance of the new concept -- storing it in R .
This approach assumes that there are enough existing labeled data to train the individual parsers . 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 . The work of Sarkar ( 2001 ) and Steedman , Osborne , et al. ( 2003 ) suggests that co-training can be helpful for statistical parsing .
In the past , it has been shown that if we can recognize the relevant morphological features in assignment configurations well enough , then they contribute to parsing accuracy . 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 . It has been more difficult showing that agreement morphology helps parsing , however , with negative results for dependency parsing in several languages ( Eryigit , Nivre , and Oflazer 2008 ; Nivre , Boguslavsky , and Iomdin 2008 ; Nivre 2009 ) .
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 . A set of candidate orderings is produced by creating different permutations of these lists .
The example in Figure 1 ( b ) illustrates a situation where specific words in the request ( docking station and install ) are also mentioned in the response . 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 ) . However , specific words in the request do not always match a response well , and sometimes do not match a response at all , as demonstrated by the examples in Figures 1 ( a ) and 1 ( c ) , respectively .
One solution to this problem is to add more complexity to the model to better reflect the translation process . 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 ) . Unfortunately , these changes make the models probabilistically deficient and intractable , requiring approximations and heuristic learning and inference prone to search errors .
The system should collate concurrences , that is , if multiple abstracts arrive at the same conclusion -- it need not be repeated unless the physician wishes to `` drill down '' ; the system should reconcile contradictions , for example , if two abstracts disagree on a particular treatment because they studied different patient populations . 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 . It is clear that the type of answers desired by physicians require a level of semantic analysis that is beyond the current state of the art , even with the aid of existing medical ontologies .
Clearly , there are other syntactic and semantic tests for this distinction , ( see eg . Perlmutter and Soames , 1979:472 ) , but these are the only ones which are explicit in the LDOCE coding system . Once the semantic type for a verb sense has been determined , the sequence of codes in the associated code field is translated , as before , on a code-by-code basis .
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 . The RenTAL system automatically converts an FB-LTAG grammar into a strongly equivalent HPSG-style grammar ( Yoshinaga and Miyao , 2001 ) .
What is required is that QLFs are , as here , expressed in a typed higher-order logic , augmented with constructs representing the interpretation of context-dependent elements ( pronouns , ellipsis , focus , etc. ) . 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 ) . Syntactic properties relevant for binding constraints , parallelism , scope constraints , and so on , are not directly represented at QLF ( again unlike standard QLFs ) but are assumed to be available as components of the linguistic context . '
The usefulness of the methodology described above was tested in the implementation of a connected speech understanding system . 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 ) . The expectation system provided the intermediate processing between the errorful output of the speech recognizer and the deep semantics of NLC .
Others provide automatic mappings of natural language instructions to executable actions , such as interpreting navigation directions ( Chen and Mooney , 2011 ) or robot commands ( Tellex et al. , 2011 ; Matuszek et al. , 2012 ) . 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 ) . 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 ) .
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 . The tasks were selected so as to demonstrate the benefit of using internal structure
Applying his technique to approximately four million words of New York Times newswire , Manning acquired 4,900 verb-subcategorization frame pairs for 3,104 verbs , an average of 1.6 frames per verb . 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 . The frames incorporate control information and details of specific prepositions .
We will enforce an invariant : the weight of any pathset H must be ( & EΠ P ( π ) , & EΠ P ( π ) val ( π ) ) E R > 0 x V , from which ( 1 ) is trivial to compute . Berstel and Reutenauer ( 1988 ) give a sufficiently general finite-state framework to allow this : weights may fall in any set K ( instead of R ) . Multiplication and addition are replaced by binary operations ® and ® on K. Thus ® is used to combine arc weights into a path weight and ® is used to combine the weights of alternative paths .
Adjunction grafts an auxiliary tree with the root node and foot node labeled x onto an internal node of another tree with the same symbol x ( Figure 4 ) . FBLTAG ( Vijay-Shanker , 1987 ; Vijay-Shanker and Joshi , 1988 ) is an extension of the LTAG formalism . In FB-LTAG , each node in the elementary trees has a feature structure , containing grammatical constraints on the node .
This approach limits how much credit the retrieval algorithm can give to a single term in the original query and prevents the translations of one or a few terms from swamping the whole query . There are several variations of such a method ( Ballesteros and Croft , 1998 ; Pirkola , 1998 ; Hull 1997 ) . One such method is to treat different translations of the same term as synonyms .
The result is displayed description language . 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 ) . The reader interested in that language and its precise interpretation can find the relevant details in that paper .
The issues of control are not so important for us at this point ; we restrict ourselves to describing the logic . 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 . As a logical postulate it is not very radical ; it is possible within a finitary framework to develop that part of mathematics that is used or has potential applications in natural science , such as mathematical analysis ( cfXXX Mycielski 1981 ) .
This shows that our method was successful in producing a more focused lexicon probability distribution . We performed translation experiments with an implementation of the IBM-4 translation model ( Brown et al. , 1993 ) . A description of the system can be found in ( Tillmann and Ney , 2002 ) .
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 correspondence between the string and its associated representation tree structure is defined in terms of the sub-correspondence between parts of the string ( substrings ) and parts of the tree structure ( subtrees ) , which can be interpreted for both analysis and generation .
These four lists can be acquired completely automatically from raw ( unlabeled ) texts . 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 ) . We used these texts because the approach described in this article was initially designed to be part of a named-entity recognition system ( Mikheev , Grover , and Moens 1998 ) developed for MUC-7 .
We perform our comparison using two state-ofthe-art parsers . For the full parser , we use the one developed by Michael Collins ( Collins , 1996 ; Collins , 1997 ) -- one of the most accurate full parsers around . It represents a full parse tree as a set of basic phrases and a set of dependency relationships between them .
Detecting agreement We used a simple method to learn to identify cross-speaker references indicating agreement . 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 ) .
For example , McKnight and Srinivasan ( 2003 ) describe a machine learning approach to automatically label sentences as belonging to introduction , methods , results , or conclusion using structured abstracts as training data ( see also Lin et al. 2006 ) . Tbahriti et al. ( 2006 ) have demonstrated that differential weighting of automatically labeled sections can lead to improved retrieval performance . Note , however , that such labels are orthogonal to PICO frame elements , and hence are not directly relevant to knowledge extraction for clinical question answering .
Furthermore , the improvement on Trec5C appears to be caused by big improvements for a small number of queries . The one-sided t-test ( Hull , 1993 ) at significance level 0.05 indicated that the improvement on Trec5C is not statistically significant . It seems surprising that disambiguation does not help at all for Trec6C .
circumstances can make each inquiry unique , and hence care must be taken to compose a response that does not confuse , irritate , or mislead the customer . It is therefore no surprise that early attempts at response automation were knowledge-driven ( Barr and Tessler 1995 ; Watson 1997 ; Delic and Lahaix 1998 ) . These systems were carefully designed to produce relevant and correct responses , but required significant human input and maintenance ( Delic and Lahaix 1998 ) .
The framework represents a generalization of several predecessor NLG systems based on Meaning-Text Theory : FoG ( Kittredge and Polguere , 1991 ) , LFS ( Iordanskaja et al. , 1992 ) , and JOYCE ( Rambow and Korelsky , 1992 ) . The framework was originally developed for the realization of deep-syntactic structures in NLG ( Lavoie and Rambow , 1997 ) . It was later extended for generation of deep-syntactic structures from conceptual interlingua ( Kittredge and Lavoie , 1998 ) .
The SSTC is a general structure that can associate an arbitrary tree structure to string in a language as desired by the annotator to be the interpretation structure of the string , and more importantly is the facility to specify the correspondence between the string and the associated tree which can be nonprojective ( Boitet & Zaharin , 1988 ) . 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 ) . Figure 2 illustrates the sentence `` John picks the box up '' with its corresponding SSTC .
This sort of processing requires quite a different architecture and different mechanisms for ambiguity resolution than one that begins processing only at the end of a complete and well-formed utterance . 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 ) . The few psycholinguistic experiments that have examined human processing of disfluent speech also throw into question the assumption that disfluent speech is harder to process than fluent speech .
We tested three values of the threshold ( 0.2 , 0.4 , 0.6 ) which try to capture different tradeoffs 5 The open source Moses ( Hoang et al. 2007 ) toolkit from www.statmt.org/moses/ . 6 www.statmt.org/wmt08/baseline.html .
WIT , however , does not provide task-independent facilities that make it easier to employ such dialogue strategies . 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 ) . Incorporating such techniques would decrease the system developer workload .
The maximum entropy approach ( Berger et al. , 1996 ) presents a powerful framework for the combination of several knowledge sources . This principle recommends to choose the distribution which preserves as much uncertainty as possible in terms of maximizing the entropy .
The Semantic Network contains information about the types or categories ( e.g. , `` Disease or Syndrome '' , `` Virus '' ) to which all META concepts have been assigned . 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 train the system by clustering the `` experiences '' of the response-generation methods in addressing requests , where each experience is characterized by the value of the confidence measure employed by a method and its subsequent performance , reflected by precision and recall ( Equations ( 7 ) and ( 8 ) , respectively ) . We then use the program Snob ( Wallace and Boulton 1968 ; Wallace 2005 ) to cluster these experiences . Figure 8 ( a ) is a projection of the centroids of the clusters produced by Snob into the three most significant dimensions discovered by Principal Component Analysis ( PCA ) -- these dimensions account for 95 % of the variation in the data .
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 ) . Furthermore , it is clear that traditional document retrieval technology applied to MEDLINE abstracts is insufficient for satisfactory information access ; research and experience point to the need for systems that automatically analyze text and return only the relevant information , appropriately summarizing and fusing segments from multiple texts .
The NP-based QA System . Our implementation of the NP-based QA system uses the Empire noun phrase finder , which is described in detail in Cardie and Pierce ( 1998 ) . Empire identifies base NPs -- non-recursive noun phrases -- using a very simple algorithm that matches part-of-speech tag sequences based on a learned noun phrase grammar .
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 . We used the same training / devtest split as in Zitouni , Sorensen , and Sarikaya ( 2006 ) ; and we further split the devtest into two equal parts : a development ( dev ) set and a blind test set .
The MI approach discovers the words that are highly associated with Agree/Disagree categories and these words turn to be useful features for classification . 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 ) . • Sentiment Lexicon ( SL ) : Two features are designed using a sentiment lexicon ( Hu and Liu , 2004 ) where the first feature represents the number of times the Callout and the Target contain a positive emotional word and the second feature represents the number of the negative emotional words .
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 ) . The MUMIN scheme is a general framework for the study of gestures in interpersonal communication .
We thus interpret this property statistically in the following way : We expect a lexically fixed verb + noun combination to appear much more frequently than its variants in general . 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 ) . We use the automatically built thesaurus of Lin ( 1998 ) to find words similar to each constituent , in order to automatically generate variants .2 Variants are generated by replacing either
The VNLCE processor may be considered to be a learning system of the tradition described , for example , in Michalski et al. ( 1984 ) . 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 ) . That is , the current system learns procedures rather than data structures .
A greedy search method was utilized and it achieved higher translation performance than the baseline . 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 . The alignments used in that work were generated from different aligners ( HMM , block model , and maximum entropy model ) .
The current model uses the context in a very straightforward way , i.e. the two words left and right of the current word , but in the future we would like to explore more advanced methods to improve the similarity estimates . 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 . The other semi-supervised methods proposed here were less successful , although all improved on the supervised model for small training sizes .