hrithikpiyush/acl-arc huggingface dataset:
Such an intelligent component could enable peer-review systems to 1 ) control the quality of peer reviews that are sent back to authors , so authors can focus on the helpful ones ; and 2 ) provide feedback to reviewers with respect to their reviewing performance , so students can learn to write better reviews . In our prior work ( Xiong and Litman , 2011 ) , we examined whether techniques used for predicting the helpfulness of product reviews ( Kim et al. , 2006 ) could be tailored to our peer-review domain , where the definition of helpfulness is largely influenced by the educational context of peer review . While previously we used the average of two expert-provided ratings as our gold standard of peer-review helpfulness1 , there are other types of helpfulness rating ( e.g. author perceived helpfulness ) that could be the gold standard , and that could potentially impact the features used to build the helpfulness model .
The topic model using the bimodal vocabulary outperforms a purely textual based model in word association and word similarity prediction . Bruni et al. ( 2012a ) show how a BoVW model may be easily combined with a distributional vector space model of language using only vector concatenation . Bruni et al. ( 2012b ) show that the contextual visual words ( i.e. the visual features around an object , rather than of the object itself ) are even more useful at times , suggesting the plausibility of a sort of distributional hypothesis for images .
A more detailed discussion of the various available Arabic tag sets can be found in Habash ( 2010 ) . The following are the various tag sets we use in this article : ( a ) the core POS tag sets CORE44 and the newly introduced CORE12 ; ( b ) CATiB Treebank tag set ( CATIB6 ) ( Habash and Roth 2009 ) and its newly introduced extension of CATIBEX created using simple regular expressions on word form , indicating particular morphemes such as the prefix JI Al + or the suffix v ' + wn ; this tag set is the best-performing tag set for Arabic on predicted values as reported in Section 4 ; ( c ) the PATB full tag set with complete morphological tag ( BW ) ( Buckwalter 2004 ) ; and two extensions of the PATB reduced tag set ( PENN POS , a.k.a. RTS , size 24 [ Diab , Hacioglu , and Jurafsky 2004 ] ) , both outperforming it : ( d ) Kulick , Gabbard , and Marcus ( 2006 ) 's tag set ( KULICK ) , size 43 , one of whose most important extensions is the marking of the definite article clitic , and ( e ) Diab and Benajiba 's ( in preparation ) EXTENDED RTS tag set ( ERTS ) , which marks gender , number , and definiteness , size 134 .
Clearly , what it takes for the adjective to be applicable has not been cast in stone , but is open to fiat : the speaker may decide that 8 cm is enough , or the speaker may set the standards higher ( cfXXX , Kennedy 1999 ) . The numeral ( whether it is implicit , as in ( 3 ) , or explicit ) can be construed as allowing the reader to draw inferences about the standards employed ( Kyburg and Morreau 2000 ; DeVault and Stone 2004 ) : ( 3 ) , for example , implies a standard that counts 10 cm as large and 8 cm as not large . Our own proposal will abstract away from the effects of linguistic context .
The error rate on sentence boundaries in the Brown corpus was not significantly worse than the lowest quoted before ( Riley 1989 : 0.28 % vs. 0.20 % error rate ) . On the WSJ corpus our system performed slightly better than the combination of the Alembic and SATZ systems described in Palmer and Hearst ( 1997 ) ( 0.44 % vs. 0.5 % error rate ) . Although these error rates seem to be very small , they are quite significant .
Other studies on the value of disambiguation for cross-lingual IR include Hiemstra and de Jong , 1999 ; Hull , 1997 . Sanderson , 1994 studied the issue of disambiguation for mono-lingual M. The third approach to cross-lingual retrieval is to map queries and documents to some intermediate representation , e.g latent semantic indexing ( LSI ) ( Littman et al , 1998 ) , or the General Vector space model ( GVSM ) , ( Carbonell et al , 1997 ) .
0 is a ground instance of a formula 0 , if 0 contains no variables , and 0 = 00 , for some substitution O. Thus , we do not require Th ( T ) to be closed under substitution instances of tautologies . Although in this paper we take modus ponens as the main rule of inference , in general one can consider deductive closures with respect to weaker , nonstandard logics , ( cfXXX Levesque 1984 ; Frisch 1987 ; Patel-Schneider 1985 ) . But we wo n't pursue this topic further here .
In a number of proposals , lexical generalizations are captured using lexical underspecification ( Kathol 1994 ; Krieger and Nerbonne 1992 ; Riehemann 1993 ; Oliva 1994 ; Frank 1994 ; Opalka 1995 ; Sanfilippo 1995 ) . The lexical entries are only partially specified , and various specializations are encoded via the type hierarchy , definite clause attachments , or a macro hierarchy .
Two applications that , like help-desk , deal with question -- answer pairs are : summarization of e-mail threads ( Dalli , Xia , and Wilks 2004 ; Shrestha and McKeown 2004 ) , and answer extraction in FAQs ( Frequently Asked Questions ) ( Berger and Mittal 2000 ; Berger et al. 2000 ; Jijkoun and de Rijke 2005 ; Soricut and Brill 2006 ) . An important difference between these applications and help-desk is that help-desk request e-mails are not simple queries .
From the Meaning-Text Theory ( MTT ) 1 point of view , Natural Language ( NL ) is considered as a correspondence between meanings and texts ( Kahane , 2001 ) . The MTT point of view , even if it has been introduced in different formulations , is more or less accepted by the whole linguistic community .
Unless explicitly stated otherwise , we assume that formulas are expressed in a certain ( formal ) language L without equality ; the extension L ( =-RRB- of L is going to be used only in Section 5 for dealing with noun phrase references . This means that natural language expressions such as `` A is B , '' `` A is the same as B , '' etc. are not directly represented by logical equality ; similarly , `` not '' is often not treated as logical negation ; cfXXX Hintikka ( 1985 ) . All logical notions that we are going to consider , such as theory or model , will be finitary .
Most of the studies are designed to support one of the two mutually exclusive paradigms : the full-listing and the morphemic model . The full-listing model claims that polymorphic words are represented as a whole in the human mental lexicon ( Bradley , 1980 ; Butterworth , 1983 ) . On the other hand , morphemic model argues that morphologically complex words are decomposed and represented in terms of the smaller morphemic units .
Another type of semantic knowledge that has been employed by coreference resolvers is the semantic class ( SC ) of an NP , which can be used to disallow coreference between semantically incompatible NPs . However , learning-based resolvers have not been able to benefit from having an SC agreement feature , presumably because the method used to compute the SC of an NP is too simplistic : while the SC of a proper name is computed fairly accurately using a named entity ( NE ) recognizer , many resolvers simply assign to a common noun the first ( i.e. , most frequent ) WordNet sense as its SC ( e.g. , Soon et al. ( 2001 ) , Markert and Nissim ( 2005 ) ) . It is not easy to measure the accuracy of this heuristic , but the fact that the SC agreement feature is not used by Soon et al. 's decision tree coreference classifier seems to suggest that the SC values of the NPs are not computed accurately by this first-sense heuristic .
fields generally follow the pattern of `` introduction '' , `` methods '' , `` results '' , and `` conclusions '' ( SalangerMeyer , 1990 ; Swales , 1990 ; Or˘asan , 2001 ) . The ability to explicitly identify these sections in unstructured text could play an important role in applications such as document summarization ( Teufel and Moens , 2000 ) , information retrieval ( Tbahriti et al. , 2005 ) , information extraction ( Mizuta et al. , 2005 ) , and question answering . Although there is a trend towards analysis of full article texts , we believe that abstracts still provide a tremendous amount of information , and much value can still be extracted from them .
Many statistical parsers ( Ratnaparkhi , 1999 ; Collins , 1999 ; Charniak , 2001 ) are based on a history-based probability model ( Black et al. , 1993 ) , where the probability of each decision in a parse is conditioned on the previous decisions in the parse . A major challenge in this approach is choosing a representation of the parse history from which the probability for the next parser decision can be accurately estimated .
Because of the large , open domain corpora these systems deal with , coverage and robustness are key issues and much on the work on paraphrases in that domain is based on automatic learning techniques . 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 .
Then the predicted vector given θ is Ej , a dj , a · ( expected feature counts on a randomly chosen arc in Dj , a ) . Per-state joint normalization ( Eisner , 2001b , § 8.2 ) is similar but drops the dependence on a . The difficult case is global conditional normalization .
While word sense disambiguation has been a central topic in previous studies for cross-lingual IR , our study suggests that using multiple weighted translations and compensating for the incompleteness of the lexicon may be more valuable . Other studies on the value of disambiguation for cross-lingual IR include Hiemstra and de Jong , 1999 ; Hull , 1997 . Sanderson , 1994 studied the issue of disambiguation for mono-lingual M.
Following the Viterbi search , each partial theory is first extended by the parser to specify possible next words , which are then scored by the recognizer . We have not yet made use of TINA 'S probabilities in adjusting the recognizer scores on the fly , but we have been able to incorporate linguistic scores to resort N-best outputs , giving a significant improvement in performance ( Goodine et al. 1991 ) . Ultimately we want to incorporate TINA 's probabilities directly into the A * search , but it is as yet unclear how to provide an appropriate upper bound for the probability estimate of the unseen portion of the linguistic model .
We incorporate into the ME based model the following informative context-based features to train CBSM and CBTM . These features are carefully designed to reduce the data sparseness problem and some of them are inspired by previous work ( He et al. , 2008 ; Gimpel and Smith , 2008 ; Marton and Resnik , 2008 ; Chiang et al. , 2009 ; Setiawan et al. , 2009 ; Shen et al. , 2009 ; Xiong et al. , 2009 ) : 1 . Function word features , which indicate whether the hierarchical source-side/targetside rule strings and sub-phrases covered by non-terminals contain function words that are often important clues of predicting syntactic structures .
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 ) . In this article we investigate morphological features for dependency parsing of Modern Standard Arabic ( MSA ) .
By restricting the height of the stack of a pushdown automaton , one obstructs recognition of a set of strings in the context-free language , and therefore a subset approximation results . This idea was proposed by Krauwer and des Tombe ( 1981 ) , Langendoen and Langsam ( 1987 ) , and Pulman ( 1986 ) , and was rediscovered by Black ( 1989 ) and recently by Johnson ( 1998 ) . Since the latest publication in this area is more explicit in its presentation , we will base our treatment on this , instead of going to the historical roots of the method .
However , many other classifier architectures are applicable to the tasks discussed , in particular to DA classification . A nonprobabilistic approach for DA labeling proposed by Samuel , Carberry , and Vijay-Shanker ( 1998 ) is transformation-based learning ( Brill 1993 ) . Finally it should be noted that there are other tasks with a mathematical structure similar to that of DA tagging , such as shallow parsing for natural language processing ( Munk 1999 ) and DNA classification tasks ( Ohler , Harbeck , and Niemann 1999 ) , from which further techniques could be borrowed .
These are split further for each leaf class , as explained in Section 2.3 . The algorithm we implemented is inspired by the work of Yarowsky ( 1995 ) on word sense disambiguation . He classified the senses of a word on the basis of other words that the given word co-occurs with .
Because of the latent alignment variables z , the log-likelihood function for the HMM model is not concave , and the model is fit using the Expectation Maximization ( EM ) algorithm ( Dempster , Laird , and Rubin 1977 ) . EM maximizes G ( 0 ) via block-coordinate ascent on a lower bound F ( q , 0 ) using an auxiliary distribution over the latent variables q ( z | x , y ) ( Neal and Hinton 1998 ) : step uses qt +1 to fill the values of alignments z and estimate parameters This step is particularly easy for HMMs , where simply involves normalizing ( expected ) counts .
There is no in-depth discussion of the problem of generating non-referring modifiers in general . It has been argued that generating such modifiers is not a trivial decision because it interferes with the planning of both local and global coherence ( in the sense of ( Grosz and Sidner , 1986 ) ) ( Cheng and Mellish , 2000a ) . NP subordination , as an alternative to sententialisation , is a major way to achieve conciseness as it reduces the number of sentences in generated texts .
were categorized into four types : ( 1 ) fact : profile and social relations of conversation participants ( accounted for 47 % of the development data and 49 % of the testing data ) ; ( 2 ) belief : participants ' beliefs and opinions ( 34 % and 35 % ) ; ( 3 ) desire : participants ' desire of certain actions or outcomes ( 11 % and 4 % ) ; ( 4 ) intent : communicative intent that captures some perlocutionary force from one participant to the other ( e.g. . A stops B from doing something ; A disagreees with B on something , 8 % and 12 % ) Note that in our original work ( Zhang and Chai , 2009 ) , only development data were used to show some initial observations . Here we trained our models on the development data and results shown are from the testing data .
In our evaluation we compared our supertagcontext approach to ( our reimplementation of ) the best-performing model of our previous work ( Garrette et al. , 2015 ) , which SCM extends . We evaluated on the English CCGBank ( Hockenmaier and Steedman , 2007 ) , which is a transformation of the Penn Treebank ( Marcus et al. , 1993 ) ; the CTBCCG ( Tse and Curran , 2010 ) transformation of the Penn Chinese Treebank ( Xue et al. , 2005 ) ; and the CCG-TUT corpus ( Bos et al. , 2009 ) , built from the TUT corpus of Italian text ( Bosco et al. , 2000 ) . Each corpus was divided into four distinct data sets : a set from which we extract the tag dictionaries , a set of raw ( unannotated ) sentences , a development set , and a test set .
In a log-linear parameterization , for example , a prior that penalizes feature strengths far from 1 can be used to do feature selection and avoid overfitting ( Chen and Rosenfeld , 1999 ) . The EM algorithm ( Dempster et al. , 1977 ) can maximize these functions . Roughly , the E step guesses hidden information : if ( xi , yi ) was generated from the current fθ , which FST paths stand a chance of having been the path used ?
Multi word expressions however remain a problem as they are often not or only partially included in such dictionnaries . For these or for a specific domain , basic synonymic dictionaries can be complemented using learning methods based on distributional similarity ( Pereira et al. , 1993 ; Lin , 1998 ) . techniques .
`` Mary '' and `` student '' are in the same entity since both refer to the same person . The coreference system system is similar to the Bell tree algorithm as described by ( Luo et al. , 2004 ) . In our implementation , the link model between a candidate entity e and the current mention m is computed as
start from the acquisition and use of lexical knowledge , which represents the basic building block of any TE system . Using the basic solution proposed by ( Mehdad et al. , 2010 ) as a term of comparison , we experiment with different sources of multilingual lexical knowledge to address the following questions : ( 1 ) What is the potential of the existing multilingual lexical resources to approach CLTE ? To answer this question we experiment with lexical knowledge extracted from bilingual dictionaries , and from a multilingual lexical database .
As the recognition engine , either VoiceRex , developed by NTT ( Noda et al. , 1998 ) , or HTK from Entropic Research can be used . Acoustic models for HTK is trained with the continuous speech database of the Acoustical Society of Japan ( Kobayashi et al. , 1992 ) . This recognizer incrementally outputs word hypotheses as soon as they are found in the best-scored path in the forward search ( Hirasawa et al. , 1998 ) using the ISTAR ( Incremental Structure Transmitter And Receiver ) protocol , which conveys word graph information as well as word hypotheses .
As such , one of the primary objectives of automatic text categorization has been the enhancement and the support of information retrieval tasks to tackle problems , such as information filtering and routing , clustering of related documents , and the classification of documents into pre-specified subject themes . Automatic text categorization has been used in search engines , digital library systems , and document management systems ( Yang , 1999 ) . Such applications have included electronic email filtering , newsgroups classification , and survey data grouping .
For our Text modality , we use deWaC , a large German web corpus created by the WaCKy group ( Baroni et al. , 2009 ) containing approximately 1.7 B word tokens . We filtered the corpus by : removing words with unprintable characters or encoding troubles ; removing all stopwords ; removing word types with a total frequency of less than 500 ; and removing documents with a length shorter than 100 .
It is comparable to WS at low recall levels ( ) , but at high ones its precision decreases almost dramatically . Unless very high rates of misspellings are to be expected ( this explains the favorable results for trigram indexing in ( Franz et al. , 2000 ) ) one can not really recommend this method . The subword approach ( SU ) clearly outperforms the previously discussed approaches .
Another known method consists in developing and using a test suite that is , a set of negative and positive items against which the grammar can be systematically tested . For english , there is for instance the 15 year old HewlettPackard test suite , a simple text file listing test sentences and grouping them according to linguistics phenomena ( Flickinger et al. , 1987 ) ; and more recently , the much more sophisticated TSNLP ( Test Suite for Natural Language Processing ) which includes some 9500 test items for English , French and German , each of them being annotated with syntactic and application related information ( Oepen and Flickinger , 1998 ) . Yet because they do not take into account the semantic dimension , none of these tools are adequate for evaluating the paraphrastic power of a grammar .
Compared to the research on English , the research on Chinese SRL is still in its infancy stage . Previous work on Chinese SRL mainly focused on how to transplant the machine learning methods which has been successful with English , such as Sun and Jurafsky ( 2004 ) , Xue and Palmer ( 2005 ) and Xue ( 2008 ) . Sun and Jurafsky ( 2004 ) did the preliminary work on Chinese SRL without any large semantically annotated corpus of Chinese .
This approach is taken , for example , in LKB ( Copestake 1992 ) where lexical rules are introduced on a par with phrase structure rules and the parser makes no distinction between lexical and nonlexical rules ( Copestake 1993 , 31 ) . A similar method is included in PATR-II ( Shieber et al. 1983 ) and can be used to encode lexical rules as binary relations in the CUF system ( Dorre and Eisele 1991 ; Done and Dorna 1993b ) or the TFS system ( Emele and Zajac 1990 ; Emele 1994 ) . The covariation approach described in this paper can be viewed as a domain-specific refinement of such a treatment of lexical rules .
With the use of computers in storing the explosive amount of biological information , natural language processing ( NLP ) approaches have been explored to make the task of managing information recorded in free text more feasible [ 1 , 2 ] . One requirement for NLP is the ability to accurately recognize terms that represent biological entities in free text .
The set of all preferred models of T is denoted by PM ( T ) . A formula q5 of L ( =-RRB- , the language with equality , is weakly R + M-abductible from an object theory T , denoted by T I-R + m 0 , iff there exists a partial theory T e PT ( T ) and a preferred model M E PM ( T ) such that M = 0 , i.e. 0 is true in at least one preferred model of the partial theory T. Note : The notions of strong provability and strong R + M-abduction can be introduced by replacing `` there exists '' by `` all '' in the above definitions ( cfXXX Zadrozny 1987b ) . We will have , however , no need for `` strong '' notions in this paper .
To date , four distinct domain-specific versions of TINA have been implemented . The first version ( TIMIT ) was developed for the 450 phonetically rich sentences of the TIMIT database ( Lamel et al. 1986 ) . The second version ( RM ) concerns the Resource Management task ( Pallett 1989 ) that has been popular within the DARPA community in recent years .
The resolution mechanism is not intended to be reversible , although by redefining resolution rules , reversibility is achievable to some extent within the limitations just discussed ( Hurst 1994 ) . A third problem arises with the approach to the semantics of QLFs that this notion of the relationship between QLF and RQLF encourages one to adopt : it is that taken by Alshawi and Crouch ( 1992 ) . This describes the semantics of QLFs via a supervaluation over the semantics of the RQLFs that they subsume .
Given this framework , deadjectival verbs such as flatten can be directly derived in the syntax : ( 15 ) The tire flattened . In ( Lin , 2004 ) , I present evidence from Mandarin Chinese that this analysis is on the right track . The rest of this paper , however , will be concerned with the computational implementation of my theoretical framework .
Interaction between components is coordinated by the dialogue manager which uses the informationstate approach ( Larsson and Traum , 2000 ) . The dialogue state is represented by a cumulative answer analysis which tracks , over multiple turns , the correct , incorrect , and not-yet-mentioned parts 1Other factors such as student confidence could be considered as well ( Callaway et al. , 2007 ) .