hrithikpiyush/acl-arc huggingface dataset:
Depending on the type of paraphrastic means involved , constructing a paraphrastic set ( the set of all lexical items related by a paraphrastic link be it parallel , shuffling or definitional ) is more or less easy as resources for that specific means may or may not be readily available . Cases of intracategorial synonymy are relatively straigthtforward as several electronic synonym dictionnaries for french are available ( Ploux , 1997 ) . Multi word expressions however remain a problem as they are often not or only partially included in such dictionnaries .
Besides , our system complexity is not increased because no additional language model is introduced . The feature of head word trigger which we apply to the log-linear model is motivated by the trigger-based approach ( Hasan and Ney , 2009 ) . Hasan and Ney ( 2009 ) introduced a second word to trigger the target word without considering any linguistic information .
norms . Other work on modeling the meanings of verbs using video recognition has also begun showing great promise ( Mathe et al. , 2008 ; Regneri et al. , 2013 ) . The Computer Vision community has also benefited greatly from efforts to unify the two modalities .
• Graph-based : An implementation of graph - based parsing algorithms with an arc-factored parameterization ( McDonald et al. , 2005 ) . We use the non-projective k-best MST algorithm to generate k-best lists ( Hall , 2007 ) , where k = 8 for the experiments in this paper .
However , the LDOCE coding of verbs is more comprehensive than elsewhere , so verbs are the obvious place to start in an evaluation of the usefulness of the coding system . No attempt has been made to map any closed class entries from LDOCE , as a 3,000 word lexicon containing most closed class items has been developed independently by one of the groups collaborating with us to develop the general purpose morphological and syntactic analyser ( see the Introduction and Russell et al. , 1986 ) . Initially the transformation of the LDOCE codes was performed on a code-by-code basis , within a code field associated with each individual word sense .
For instance , ( Lin and Pantel , 2001 ) acquire two-argument templates ( inference rules ) from corpora using an extended version of the distributional analysis in which paths in dependency trees that have similar arguments are taken to be close in meaning . Similarly , ( Barzilay and Lee , 2003 ) and ( Shinyanma et al. , 2002 ) learn sentence level paraphrase templates from a corpus of news articles stemming from different news source . And ( Glickman and Dagan , 2003 ) use clustering and similarity measures to identify similar contexts in a single corpus and extract verbal paraphrases from these contexts .
The sentence representation we chose for this work is rather simple , and was intended primarily to demonstrate the efficacy of our approach . In future work we plan to experiment with richer representations , e.g. including long-range n-grams ( Rosenfeld , 1996 ) , class n-grams ( Brown et al. , 1992 ) , grammatical features ( Amaya and Benedy , 2001 ) , etc ' . The main computational bottleneck in our approach is the generation of negative samples from the current model .
Our work is the first to show gains using agreement in MaltParser and in Arabic dependency parsing , and the first to use functional features for this task . Furthermore , we demonstrate that our results carry over successfully to another parser , the Easy-First Parser ( Goldberg and Elhadad 2010 ) ( Section 6 ) . Hohensee and Bender ( 2012 ) have conducted a study on dependency parsing for 21 languages using features that encode whether the values for certain attributes are equal or not for a node and its governor .
It should not come as a surprise that we can now use this apparatus for text/discourse analysis ; after all , many natural language inferences are based on defaults , and quite often they can be reduced to choosing most plausible interpretations of predicates . For instance , relating `` they '' to `` apples '' in the sentence ( cfXXX Haugeland 1985 p. 195 ; Zadrozny 1987a ) : We bought the boys apples because they were so cheap can be an example of such a most plausible choice .
In selecting features for Korean , we have to account for relatively free word order ( Chung et al. , 2010 ) . We follow our previous work ( Dickinson et al. , 2010 ) in our feature choices , using a fiveword window that includes the target stem and two words on either side for context ( see also Tetreault and Chodorow , 2008 ) . Each word is broken down into : stem , affixes , stem POS , and affixes POS .
Much recent work in lexical semantics has been concerned with accounting for regular polysemy , i.e. , the regular and predictable sense alternations certain classes of words are subject to . Adjectives , more than other categories , are a striking example of regular polysemy since they are able to take on different meanings depending on their context , viz. , the noun or noun class they modify ( see Pustejovsky ( 1995 ) and the references therein ) . The adjective fast in ( 1 ) receives different interpretations when modifying the nouns programmer , plane and scientist .
The argument list can be empty , as in the PRED value for judge in Figure 1 . According to Dalrymple ( 2001 ) , LFG assumes the following universally available inventory of grammatical functions : SUBJ ( ect ) , OBJ ( ect ) , OBJe , COMP , XCOMP , OBL ( ique ) e , ADJ ( unct ) , XADJ . OBJe and OBLe represent families of grammatical functions indexed by their semantic role , represented by the theta subscript .
To avoid confusion , we will only use the terminology introduced in the text . 4 This interpretation of the signature is sometimes referred to as closed world ( Gerdemann and King 1994 ; Gerdemann 1995 ) . 5 An in-depth discussion including a comparison of both approaches is provided in Calcagno , Meurers , and Pollard ( in preparation ) .
Similarity-smoothed models can make use of the regularities across similar verbs , but not the finergrained stringand token-based features . Our training examples are similar to the data created for pseudodisambiguation , the usual evaluation task for SP models ( Erk , 2007 ; Keller and Lapata , 2003 ; Rooth et al. , 1999 ) . This data consists of triples ( v , n , n ′ ) where v , n is a predicateargument pair observed in the corpus and v , n ′ has not been observed .
The Generative Programming approach to NLP infrastructure development will allow tools such as sentence boundary detectors , POS taggers , chunkers and named entity recognisers to be rapidly composed from many elemental components . For instance , implementing an efficient version of the MXPOST POS tagger ( Ratnaparkhi , 1996 ) will simply involve composing and configuring the appropriate text file reading component , with the sequential tagging component , the collection of feature extraction components and the maximum entropy model component . The individual components will provide state of the art accuracy and be highly optimised for both time and space efficiency .
1http : / / stephenroller.com/research/ emnlp13 cue word and name the first ( or several ) associated words that come to mind ( e.g. , Nelson et al. ( 2004 ) ) , and feature norms , where subjects are given a cue word and asked to describe typical properties of the cue concept ( e.g. , McRae et al. ( 2005 ) ) . 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 .
To our knowledge , no work has explicitly explored the relation between the length of a constituent and its status in the discourse . Hirschberg and Litman ( 1987 ) and Litman and Hirschberg ( 1990 ) also examine the relation between discourse and prosodic phrasing . Their work succeeds in distinguishing the use of items like now , so , and well as discourse cues from their denotative lexical use on the basis of a complex combination of pitch accent type and phrasing .
Still , to date this is the largest number of verbs used in any of the evaluations of the systems for English described in Section 3 . Sarkar and Zeman ( 2000 ) evaluate 914 Czech verbs against a custom-made gold standard and record a token recall of 88 % . However , their evaluation does not examine the extracted subcategorization frames but rather the argument -- adjunct distinctions posited by their system .
Although this method of assessment is less informative than human-based evaluations , it enables us to evaluate the performance of our system with substantial amounts of data , and produce representative results for a large corpus such as ours . We use two measures from Information Retrieval to determine the quality of an automatically generated response : precision and F-score ( van Rijsbergen 1979 ; Salton and McGill 1983 ) . Precision measures how much of the information in an automatically generated response is correct ( i.e. , appears in the model response ) , and F-score measures the overall similarity between the automatically generated response and the model response .
So the hierarchical system in their paper performs a little worse than the traditional SRL systems , although it is more efficient . Xue and Palmer ( 2004 ) did very encouraging work on the feature calibration of semantic role labeling . They found out that different features suited for different sub tasks of SRL , i.e. semantic role identification and classification .
A number of applications have relied on distributional analysis ( Harris , 1971 ) in order to build classes of semantically related terms . This approach , which uses words that appear in the context of terms to formulate hypotheses on their semantic relatedness ( Habert et al. , 1996 , for example ) , does not specify the relationship itself . Hence , synonyms , co-hyponyms , hyperonyms , etc. are not differentiated .
Similarly , ( Barzilay and Lee , 2003 ) and ( Shinyanma et al. , 2002 ) learn sentence level paraphrase templates from a corpus of news articles stemming from different news source . And ( Glickman and Dagan , 2003 ) use clustering and similarity measures to identify similar contexts in a single corpus and extract verbal paraphrases from these contexts . Such machine learning approaches have known pros and cons .
This idea of preserving properties can be considered an instance of the well-known frame problem in AT ( McCarthy and Hayes 1969 ) , and we will therefore refer to the specifications left implicit by the linguist as the frame specification , or simply frame , of a lexical rule . Not having to represent the frame explicitly not only enables the linguist to express only the relevant things , but also allows a more compact representation of lexical rules where explicit framing would require the rules to be split up ( Meurers 1994 ) . One thus needs to distinguish the lexical rule specification provided by the linguist from the fully explicit lexical rule relations integrated into the theory .
Watanabe ( 1993 ) combines lexical and dependency mappings to form his generalizations . Other similar approaches include those of Cicekli and G ¨ uvenir ( 1996 ) , McTait and Trujillo ( 1999 ) , Carl ( 1999 ) , and Brown ( 2000 ) , inter alia . In our system , in some cases the smallest chunk obtainable via the marker-based segmentation process may be something like ( 27 ) : ( 27 ) <DET> the good man : le bon homme In such cases , if our system were confronted with a good man , it would not be able to translate such a phrase , assuming this to be missing from the marker lexicon .
To sum up , this work has been carried out to automatically classify Arabic documents using the NB algorithm , with the use of a different data set , a different number of categories , and a different root extraction algorithm from those used in ( Yahyaoui , 2001 ) . In this work , the average accuracy over all categories is : 68.78 % in cross validation and 62 % in evaluation set experiments .
The research reported here is the first that we know of to automatically analyze a corpus of logs from a spoken dialogue system for the purpose of learning to predict problematic situations . Our work builds on earlier research on learning to identify dialogues in which the user experienced poor speech recognizer performance ( Litman et al. , 1999 ) . However , that work was based on a much smaller set of experimental dialogues where the notion of a good or bad dialogue was automatically approximated rather than being labelled by humans .
We now give an overview of the training algorithm , which is crucial to both the speed and accuracy of the resulting decoder . CCGBank ( Hockenmaier and Steedman 2007 ) is used to train the model . For each training sentence , the corresponding CCGBank derivation together with all its sub-derivations are treated as gold-standard hypotheses .
Hence , synonyms , co-hyponyms , hyperonyms , etc. are not differentiated . More recent work on terminology structuring has focussed on formal similarity to develop hypotheses on the semantic relationships between terms : Daille ( 2003 ) uses derivational morphology ; Grabar and Zweigenbaum ( 2002 ) use , as a starting point , a number of identical characters . Up to now , the focus has been on nouns and adjectives , since these structuring methods have been applied to lists of extracted candidate terms ( Habert et al. , 1996 ; Daille , 2003 ) or to lists of admitted terms ( Grabar and Zweigenbaum , 2002 ) .
Other representations use the link structure ( Malin , 2005 ) or generate graph representations of the extracted features ( Kalashnikov et al. , 2007 ) . Some researchers ( Cucerzan , 2007 ; Nguyen and Cao , 2008 ) have explored the use of Wikipedia information to improve the disambiguation process . Wikipedia provides candidate entities that are linked to specific mentions in a text .
The ElixirFM lexical resource used previously provided functional NUMBER feature values but no functional GENDER values , nor RAT ( rationality , or humanness ) values . To address this issue , we use a version of the PATB3 training and dev sets manually annotated with functional gender , number , and rationality ( Alkuhlani and Habash 2011 ) .18 This is the first resource providing all three features ( ElixirFm only provides functional number , and to some extent functional gender ) . We conducted experiments with gold features to assess the potential of these features , and with predicted features , obtained from training a simple maximum likelihood estimation classifier on this resource ( Alkuhlani and Habash 2012 ) .19 The first part of Table 8 shows that the RAT ( rationality ) feature is very relevant ( in gold ) , but suffers from low accuracy ( no gains in machine-predicted input ) .
This type of error and inconsistency arises because grammatical codes are constructed by hand and no automatic checking procedure is attempted ( see Michiels , 1982 , for further comment ) . One approach to this problem is that taken by the ASCOT project ( Akkerman et al. , 1985 ; Akkerman , 1986 ) . In this project , a new lexicon is being manually derived from LDOCE .
We consider the Creative Commons model as the most suitable one to let each author choose the rights to reserve ( Lessig , 2004 ) . Narrative writings or essays are creative works and they generally treat ownership as authorship , even for the most enthusiastic fellows of free culture ( Stallman , 2001 ) .
For example , DIOGENES ( Nirenburg et al. 1988 ) , EPICURE ( Dale 1989 ) , SPOKESMAN ( Meteer 1989 ) , Sibun 's work on local organization of text ( Sibun 1991 ) , and COMET ( Fisher and McKeown 1990 ) all are organized this way . McDonald has even argued for extending the model to a large number of components ( McDonald 1988 ) , and several systems have indeed added an additional component between the planner and the linguistic component ( Meteer 1994 ; Panaget 1994 ; Wanner 1994 ) . Reiter describes a pipelined modular approach as a consensus architecture underlying most recent work in generation ( Reiter 1994 ) .
The architecture remains applicable to a large variety of languages . According to current tagger comparisons ( van Halteren et al. , 1998 ; Zavrel and Daelemans , 1999 ) , and according to a comparsion of the results presented here with those in ( Ratnaparkhi , 1996 ) , the Maximum Entropy framework seems to be the only other approach yielding comparable results to the one presented here . It is a very interesting future research topic to determine the advantages of either of these approaches , to find the reason for their high accuracies , and to find a good combination of both .
Bashir ( 1993 ) tried to construct a semantic analysis based on `` prepared '' and `` unprepared mind '' . Similar findings have been proposed by Pandharipande ( 1993 ) that points out V1 and V2 are paired on the basis of their semantic compatibility , which is subject to syntactic constraints . Paul ( 2004 ) tried to represent Bangla CVs in terms of HPSG formalism .
The simplest and most straightforward subliminal modification of text is to substitute selected words with their synonyms . The first lexical substitution method was proposed by Chapman and Davida ( 1997 ) . Later works , such as Atallah et al. ( 2001a ) , Bolshakov ( 2004 ) , Taskiran et al. ( 2006 ) and Topkara et al. ( 2006b ) , further made use of part-ofspeech taggers and electronic dictionaries , such as WordNet and VerbNet , to increase the robustness of the method .
We shall focus on the more challenging case where the output of the generator is less precise than the input , as is the case in FOG and DYD . This can be a hazardous affair , since vague expressions tend to be interpreted in different ways by different people ( Toogood 1980 ) , sometimes in stark contrast with the intention of the speaker/writer ( Berry , Knapp , and Raynor 2002 ) . We shall therefore focus -- unlike earlier computational accounts -- on vague descriptions , that is , vague expressions in definite descriptions .
Suppose , for example , that the KB contains information about height as well as width , then we have inequalities of the forms ( a ) height > x , ( b ) height < x , ( c ) width > x , and ( d ) width < x. Which of these should come first ? Hermann and Deutsch ( 1976 ; also reported in Levelt 1989 ) show that greater differences are most likely to be chosen , presumably because they are more striking . In experiments involving candles of different heights and widths , if the referent is both the tallest and the fattest candle , subjects tended to say ` the tall candle ' when the tallest candle is much taller than all others whereas the same candle is only slightly wider than the others ; if the reverse is the case , the preference switches to ` the fat candle . '
The acquisition of such knowledge is time-consuming , difficult , and error-prone . Nevertheless , recent results show that knowledge-poor methods perform with amazing accuracy ( cfXXX ( Mitkov , 1998 ) , ( Kennedy and Boguraev , 1996 ) ( Kameyama , 1997 ) ) . For example , CogNIAC ( Baldwin , 1997 ) , a system based on seven ordered heuristics , generates high-precision resolution ( over 90 % ) for some cases of pronominal reference .
Through corpus annotation , we wish to answer the question of what will be the probability of a given piece of information occupying a given syntactic position ( a value of type ) on the basis of the semantic and pragmatic properties of that information and relevant NP features , for example , whether a certain color attribute should be expressed by means of a prenominal adjective or a prepositional phrase in a definite NP . Notice that it is not possible to use corpus annotation to determine the likelihood of a given property to be chosen , unless we know in advance all of the properties that can be attributed to a given object , as in the case of Jordan 's work on the COCONUT domain ( Jordan , 2000 ) . Below we briefly introduce the major values of the three modifier features .
Researchers have proposed many computational models for resolving PPattachment ambiguities . Some well-known approaches include rule-based models ( Brill and Resnik 1994 ) , backed-off models ( Collins and Brooks 1995 ) , and a maximumentropy model ( Ratnaparkhi 1998 ) . Following the tradition of using learning PPattachment as a way to gain insight into the parsing problem , we first apply sample selection to reduce the amount of annotation used in training a PP-attachment model .
In particular , both developer-oriented ( e.g. , related to the selection of optimal resolution factors ) and application-oriented ( e.g. , related to the requirement of the application , as in the case of information extraction , where a proper name antecedent is needed ) evaluation metrics should be considered . Tetreault 's contribution features comparative evaluation involving the author 's own centering-based pronoun resolution algorithm called the Left-Right Centering algorithm ( LRC ) as well as three other pronoun resolution methods : Hobbs 's naive algorithm ( Hobbs 1978 ) , BFP ( Brennan , Friedman , and Pollard 1987 ) , and Strube 's 5list approach ( Strube 1998 ) . The LRC is an alternative to the original BFP algorithm in that it processes utterances incrementally .
In addition , rather than including actual response sentences in a reply , their system matches response sentences to pre-existing templates and returns the templates . Lapalme and Kosseim ( 2003 ) investigated three approaches to the automatic generation of response e-mails : text classification , case-based reasoning , and question answering . Text classification was used to group request e-mails into broad categories , some of which , such as requests for financial reports , can be automatically addressed .
For example , headwords in both languages are chosen to force a synchronized alignment ( for better or worse ) in order to simplify cases involving so-called head-switching . This contrasts with one of the traditional approaches ( e.g. , Dorr 1994 ; Watanabe 1995 ) to posing the translation problem , i.e. , the approach in which translation problems are seen in terms of bridging the gap between the most natural monolingual representations underlying the sentences of each language . The training method has four stages : ( i ) Compute co-occurrence statistics from the training data .
Stanford University is developing the English Resource Grammar , an HPSG grammar for English , as a part of the Linguistic Grammars Online ( LinGO ) project ( Flickinger , 2000 ) . In practical context , German , English , and Japanese HPSG-based grammars are developed and used in the Verbmobil project ( Kay et al. , 1994 ) . Our group has developed a wide-coverage HPSG grammar for Japanese ( Mitsuishi et al. , 1998 ) , which is used in a high-accuracy Japanese dependency analyzer ( Kanayama et al. , 2000 ) .
While IA is generally thought to be consistent with findings on human language production ( Hermann and Deutsch 1976 ; Levelt 1989 ; Pechmann 1989 ; Sonnenschein 1982 ) , the hypothesis that incrementality is a good model of human GRE seems unfalsifiable until a preference order is specified for the properties on which it operates . ( Wildly redundant descriptions can result if the ` wrong ' preference order are chosen . )
In the Rapid Knowledge Formation Project ( RKF ) a support system was developed for domain experts . It helps them build complex knowledge bases by combining components : events , entities and modifiers ( Clark and Porter , 1997 ) . The system 's interface facilitates the expert 's task of creating and manipulating structures which represent domain concepts , and assigning them relations from a relation dictionary .
In practical context , German , English , and Japanese HPSG-based grammars are developed and used in the Verbmobil project ( Kay et al. , 1994 ) . Our group has developed a wide-coverage HPSG grammar for Japanese ( Mitsuishi et al. , 1998 ) , which is used in a high-accuracy Japanese dependency analyzer ( Kanayama et al. , 2000 ) .