hrithikpiyush/acl-arc huggingface dataset:
This DAG-like property was responsible for the over-generalisation , and so we removed the link between / per son ) and causal_agent ) . This appeared to solve the problem , and the results presented later for the average degree of generalisation do not show an over-generalisation compared with those given in Li and Abe ( 1998 ) .
Often , however , these functional elements responsible for licensing event interpretations are not phonologically realized . These observations and this line of reasoning has not escaped the attention of theoretical linguists : Hale and Keyser ( 1993 ) propose that argument structure is , in fact , encoded syntactically . They describe a cascading verb phrase analysis with multiple phonetically empty verbal projections corresponding to concepts such as inchoativity and agentivity .
Previous sentiment-analysis work in different domains has considered inter-document similarity ( Agarwal and Bhattacharyya , 2005 ; Pang and Lee , 2005 ; Goldberg and Zhu , 2006 ) or explicit 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 ) .
Firstly , we plan to explore our estimator on other language pairs in order to obtain more evidence on its behavior . Secondly , as ( Blunsom et al. , 2008 ) show , marginalizing out the different segmentations during decoding leads to improved performance . We plan to build our own decoder ( based on ITG ) where different ideas can be tested including tractable ways for achieving a marginalization effect .
Recently , Green and Manning ( 2010 ) analyzed the PATB for annotation consistency , and introduced an enhanced split-state constituency grammar , including labels for short idafa constructions and verbal or equational clauses . Nivre ( 2008 ) reports experiments on Arabic parsing using his MaltParser ( Nivre et al. 2007 ) , trained on the PADT . His results are not directly comparable to ours because of the different treebank representations , even though all the experiments reported here were performed using the MaltParser .
The grammar conversion from LTAG to HPSG ( Yoshinaga and Miyao , 2001 ) is the core portion of the RenTAL system . The conversion algorithm consists of :
Each individual PICO extractor takes as input the abstract text of a MEDLINE citation and identifies the relevant elements : Outcomes are complete sentences , while population , problems , and interventions are short noun phrases . Our knowledge extractors rely extensively on MetaMap ( Aronson 2001 ) , a system for identifying segments of text that correspond to concepts in the UMLS Metathesaurus . Many of our algorithms operate at the level of coarser-grained semantic types called Semantic Groups ( McCray , Burgun , and Bodenreider 2001 ) , which capture higher-level generalizations about entities ( e.g. , CHEMICALS & DRUGS ) .
Finally , we evaluate DSP on a common application of selectional preferences : choosing the correct antecedent for pronouns in text ( Dagan and Itai , 1990 ; Kehler et al. , 2004 ) . We study the cases where a 9Recall that even the Keller and Lapata ( 2003 ) system , built on the world 's largest corpus , achieves only 34 % recall ( Table 1 ) ( with only 48 % of positives and 27 % of all pairs previously observed , but see Footnote 5 ) . pronoun is the direct object of a verb predicate , v .
generating the source string , which contains several source words and variables . Inspired by ( Blunsom et al. , 2009 ) and ( Cohn and Blunsom , 2009 ) , we define P ( str | frag ) as follows : where csw is the number of words in the source string . ™ s means the source vocabulary set .
Other attempts to address efficiency include the fast Transformation Based Learning ( TBL ) Toolkit ( Ngai and Florian , 2001 ) which dramatically speeds up training TBL systems , and the translation of TBL rules into finite state machines for very fast tagging ( Roche and Schabes , 1997 ) . The TNT POS tagger ( Brants , 2000 ) has also been designed to train and run very quickly , tagging between 30,000 and 60,000 words per second . The Weka package ( Witten and Frank , 1999 ) provides a common framework for several existing machine learning methods including decision trees and support vector machines .
This may be due to CASE tagging having a lower error rate in Czech ( 5.0 % ) ( Hajiˇc and Vidová-Hladká 1998 ) compared with Arabic ( ≈ 14.0 % , see Table 3 ) . Similarly , Cowan and Collins ( 2005 ) report that the use of a subset of Spanish morphological features ( number for adjectives , determiners , nouns , pronouns , and verbs ; and mode for verbs ) outperforms other combinations . Our approach is
We work with a semi-technical text on meteorological phenomena ( Larrick , 1961 ) , meant for primary school students . The text gradually introduces concepts related to precipitation , and explains them .
Most of the feature templates are standard , so only the new ones will be explained . The candidate feature templates include : Voice from Sun and Jurafsky ( 2004 ) . Head word POS , Head Word of Prepositional Phrases , Constituent tree distance , from Pradhan etc. ( 2004 ) .
Third , the paradigm of evidence-based medicine ( Sackett et al. 2000 ) provides a task-based model of the clinical information-seeking process . The PICO framework ( Richardson et al. 1995 ) for capturing well-formulated clinical queries ( described in Section 2 ) can serve as the basis of a knowledge representation that bridges the needs of clinicians and analytical capabilities of a system . The confluence of these many factors makes clinical question answering a very exciting area of research .
The Chinese PropBank has labeled the predicateargument structures of sentences from the Chinese TreeBank ( Xue et al. 2005 ) . It is constituted of two parts .
This is an important property if semantic relatedness is used in information retrieval where users tend to use specific search terms ( Porsche ) rather than general ones ( car ) . Furthermore , manually selected word pairs are often biased towards highly related pairs ( Gurevych , 2006 ) , because human annotators tend to select only highly related pairs connected by relations they are aware of . Automatic corpus-based selection of word pairs is more objective , leading to a balanced dataset with pairs connected by all kinds of lexical-semantic relations .
The domain-dependent knowledge used in this module consists of a unification-based lexicon and phrase structure rules . Disjunctive feature descriptions are also possible ; WIT incorporates an efficient method for handling disjunctions ( Nakano , 1991 ) . When a phrase boundary is detected , the feature structure for a phrase is computed using some built-in rules from the feature structure rules for the words in the phrase .
Since there is no way to analytically compute these modes , we resort to Gibbs sampling to find an approximate solution . Our strategy is based on the approach presented by Johnson et al. ( 2007 ) . At a high level , we alternate between resampling model parameters ( θROOT , θBIN , θUN , θTERM , λ , θLCTX , θRCTX ) given the current set of parse trees and resampling those trees given the
The main benefits of this acquisition technique lie in the inferred patterns . Indeed , contrary to the more classical statistical methods ( Mutual Information , Loglike ... , see below ) used for collocation acquisition ( see ( Pearce , 2002 ) for a review ) , these patterns allow : 1 .
( The green chair , said in the presence of two greenish chairs , would refer to the one that is closest to prototypical green . ) A further complication is that different speakers can regard very different values as prototypical , making it difficult to assess which of two objects is greener even on one dimension ( Berlin and Kay 1969 , pages 10 -- 12 ) . ( Ideally , GRE should also take into account that the meaning of color words can differ across different types of referent .
• If arc probabilities ( or even A , v , µ , ρ ) have loglinear parameterization , then the E step must compute c = Ei ecf ( xi , yi ) , where ec ( x , y ) denotes the expected vector of total feature counts along a random path in fθ whose ( input , output ) matches ( x , y ) . The M step then treats c as fixed , observed data and adjusts 0 until the predicted vector of total feature counts equals c , using Improved Iterative Scaling ( Della Pietra et al. , 1997 ; Chen and Rosenfeld , 1999 ) .12 For globally normalized , joint models , the predicted vector is ecf ( E * , A * ) .
For this study , we added semantic labels -- including dialogue acts -- and gesture annotation . Both kinds of annotation were carried out using ANVIL ( Kipp , 2004 ) . To distinguish among the various functions that feedback expressions have in the dialogues , we selected a subset of the categories defined in the emerging ISO 24617-2 standard for semantic annotation of language resources .
Because the Google corpus does not contain complete sentences but only n-gram counts of length up to five , training a discriminative model is not desirable , and we thus use NB ( details in Rozovskaya and Roth ( 2011 ) ) . The article classifier is a discriminative model that draws on the state-of-the-art approach described in Rozovskaya et al. ( 2012 ) . The model makes use of the Averaged Perceptron ( AP ) algorithm ( Freund and Schapire , 1996 ) and is trained on the training data of the shared task with rich features .
The disambiguation of capitalized words in ambiguous positions leads to the identification of proper names ( or their derivatives ) , and in this article we will use these two terms and the term case normalization interchangeably . Church ( 1995 , p. 294 ) studied , among other simple text normalization techniques , the effect of case normalization for different words and showed that `` sometimes case variants refer to the same thing ( hurricane and Hurricane ) , sometimes they refer to different things ( continental and Continental ) and sometimes they do n't refer to much of anything ( e.g. , anytime and Anytime ) . '' Obviously these differences arise because some capitalized words stand for proper names ( such as Continental , the name of an airline ) and some do not .
The process is similar to that for computing the probability of the entire sentence from the probabilities of substrings ( called Inside Probabilities ) . We follow the notation convention of Lari and Young ( 1990 ) . The inside probability of a nonterminal X generating the substring wi ... wj is denoted as e ( X , i , j ) ; it is the sum of the probabilities of all possible subtrees that have X as the root and wi ... wj as the leaf nodes .
Resnik ( 1995 ) reported a correlation of r = .9026.10 The results are not directly comparable , because he only used noun-noun pairs , words instead of concepts , a much smaller dataset , and measured semantic similarity instead of semantic relatedness . Finkelstein et al. ( 2002 ) did not report inter-subject correlation for their larger dataset . Gurevych ( 2006 ) reported a correlation of r = .69 .
Comparing the best performer to the OOV selection mode system shows that translating low frequency in-vocabulary dialectal words and phrases to their MSA paraphrases can improve the English translation . This is a similar conclusion to our previous work in Salloum and Habash ( 2011 ) . 6.1.3 Results on the Blind Test Sets
paper . The priorities are used for disambiguating interpretation in the incremental understanding method ( Nakano et al. , 1999b ) . When the command on the right-hand side of the arrow is a frame operation command , phrases to which this rule can be applied can be considered a sentence , and the sentence 's semantic representation is the command for updating the dialogue state .
The denotational semantics of RQLF structures involving instantiated scope and referent metavariables is given in terms of simple interpretation rules that have the effect of interpreting the quantifiers as having the scopes indicated by the lists of indices , and the pronouns as having the interpretation of the instantiation of the metavariable ( in these cases at least ) . Alshawi and Crouch ( 1992 ) present an illustrative first-order fragment along these lines and are able to supply a coherent formal semantics for the CLF-QLFs themselves , using a technique essentially equivalent to supervaluations : a QLF is true iff all its possible RQLFs are , false iff they are all false , and undefined otherwise . There are many good things about this approach .
We use the open-source Moses toolkit ( Koehn et al. , 2007 ) to build a phrase-based SMT system trained on mostly MSA data ( 64M words on the Arabic side ) obtained from several LDC corpora including some limited DA data . Our system uses a standard phrase-based architecture .
Our spread activation based semantic measure ( R98 ( „ , sa ) ) improved accuracy . This confirms that although Kozima 's approach ( Kozima , 1993 ) is computationally expensive , it does produce more precise segmentation . The most significant improvement was due to our ranking scheme which linearises the cosine coefficient , .
that this does not presuppose the existence of a corpus in which the current document is similar to other documents . Park and Byrd ( 2001 ) recently described a hybrid method for finding abbreviations and their definitions . This method first applies an `` abbreviation recognizer '' that generates a set of `` candidate abbreviations '' for a document .
Accordingly , they generate lexical correspondences by means of co-occurrence measures and string similarity metrics . More specifically , the notion of the phrasal lexicon ( used first by Becker 1975 ) has been used successfully in a number of areas : • Learnability ( Zernik and Dyer 1987 ) • Text generation ( Hovy 1988 ; Milosavljevic , Tulloch , and Dale 1996 ) • Speech generation ( Rayner and Carter 1997 ) • Localization ( Sch ¨ aler 1996 )
Brent used hypothesis testing on binomial frequency data to statistically filter the induced frames . Ushioda et al. ( 1993 ) run a finite-state NP parser on a POS-tagged corpus to calculate the relative frequency of the same six subcategorization verb classes . The experiment is limited by the fact that all prepositional phrases are treated as adjuncts .
The automatic extraction of PICO elements from MEDLINE citations represents a key capability integral to clinical question answering . This section , which elaborates on preliminary results reported in Demner-Fushman and Lin ( 2005 ) , describes extraction algorithms for population , problems , interventions , outcomes , and the strength of evidence . For an example of a completely annotated abstract , see Figure 2 .
This immediate information may be insufficient to decide the truth of certain predicates . It would seem therefore that the iteration of the PT operation to form a closure is needed ( cfXXX Zadrozny 1987b ) . However , there are at least three arguments against iterating PT .
Sedivy et al. ( 1999 ) asked subjects to identify the target of a vague description in a visual scene . Consider the tall cup .
Since the system accepts unrestricted input , interpretation errors are unavoidable . Our recovery policy is modeled on the TargetedHelp ( Hockey et al. , 2003 ) policy used in task-oriented dialogue . If the system can not find an interpretation for an utterance , it attempts to produce a message that describes the problem but without giving away the answer , for example , `` I 'm sorry , I 'm having a problem understanding .
It has been argued that , in an incremental approach , gradable properties should be given a low preference ranking because they are difficult to process ( Krahmer and Theune 2002 ) . We have seen in Section 4.3 that generation and interpretation of vague descriptions does have a slightly higher computational complexity than that of nonvague descriptions .
Typed feature structures as normal form TFC terms are merely syntactic objects . ` See ( King , 1994 ) for a discussion of the appropriateness of TIG for HPSG and a comparison with other feature logic approaches designed for HPSG . which are used to restrict lexical entries .
The above equation holds regardless of how we represent concepts . There are many plausible representations , such as pairs of trees from synchronous tree adjoining grammars ( Abeille et al. 1990 ; Shieber 1994 ; Candito 1998 ) , lexical conceptual structures ( Dorr 1992 ) and WordNet synsets ( Fellbaum 1998 ; Vossen 1998 ) . Of course , for a representation to be used , a method must exist for estimating its distribution in data .
In the above example , this would result in two lexical rules : one for words with t1 as their c value and one for those with t2 as their c value . In the latter case , we can also take care of transferring the value of z. However , as discussed by Meurers ( 1994 ) , creating several instances of lexical rules can be avoided . Instead , the disjunctive possibilities introduced by the frame specification are attached as a constraint to a lexical rule .
The combination of likelihood and prior modeling , HMMs , and Viterbi decoding is fundamentally the same as the standard probabilistic approaches to speech recognition ( Bahl , Jelinek , and Mercer 1983 ) and tagging ( Church 1988 ) . It maximizes the probability of getting the entire DA sequence correct , but it does not necessarily find the DA sequence that has the most DA labels correct ( Dermatas and Kokkinakis 1995 ) . To minimize the total number of utterance labeling errors , we need to maximize the probability of getting each DA label correct individually , i.e. , we need to maximize P ( U11E ) for each i = 1 , ... , n .
Finally , we introduced the idea of evaluating induction systems based on their ability to produce useful cluster prototypes . We found that the oldest system ( Brown et al. , 1992 ) yielded the best prototypes , and that using these prototypes gave state-of-the-art performance on WSJ , as well as improvements on nearly all of the non-English corpora . These promising results suggest a new direction for future research : improving POS induction by developing methods targeted towards extracting better prototypes , rather than focusing on improving clustering of the entire data set .
In this section , we explain the predicate communicate so that the algorithm may be followed by the reader . Other definitions of predicates may be found in ( Gomez , 1998 ) . ferences between teach and its superpredicate , communicate are as follows .
Using the same approach discussed in Section 4 , we compare the results achieved with English paraphrase tables with those obtained with other widely used monolingual knowledge resources over two RTE datasets . For the sake of completeness , we report in this section also the results obtained adopting the `` basic solution '' proposed by ( Mehdad et al. , 2010 ) . Although it was presented as an approach to CLTE , the proposed method brings the problem back to the monolingual case by translating H into the language of T .
3The sp-tree is inspired by ( Lavoie and Rambow , 1998 ) . The representations used by Danlos ( 2000 ) , Gardent and Webber ( 1998 ) , or Stone and Doran ( 1997 ) are similar , but do not ( always ) explicitly represent the clause combining operations as labeled nodes . tem5 in Dialog D1 .
It is also used to add a gloss for each word sense that enables test subjects to distinguish between senses . If differences in meaning between senses are very fine-grained , distinguishing between them is hard even for humans ( Mihalcea and Moldovan , 2001 ) .6 Pairs containing such words are not suitable for evaluation . To limit their impact on the experiment , a threshold for the maximal number of senses can be defined .
Although not the first to employ a generative approach to directly model content , the seminal work of Barzilay and Lee ( 2004 ) is a noteworthy point of reference and comparison . However , our study differs in several important respects .
We release this list for further use by the research community .2 Future Work : Functionality is one of the several properties a relation can possess . Others include selectional preferences , transitivity ( Schoenmackers et al. , 2008 ) , mutual exclusion , symmetry , etc. . These properties are very useful in increasing our understanding about these Open IE relation strings .
The bulk of the text categorization work has been devoted to cope with automatic categorization of English and Latin character documents . For example , ( Fang et al. , 2001 ) discusses the evaluation of two different text categorization strategies with several variations of their feature spaces . A good study comparing document categorization algorithms can be found in ( Yang and Liu , 1999 ) .
We want to investigate the usefulness of stem ngram features in the mention detection system . As stated before , the experiments are run in the ACE '04 framework ( NIST , 2004 ) where the system will identify mentions and will label them ( cfXXX Section 4 ) with a type ( person , organization , etc ) , a sub-type ( OrgCommercial , OrgGovernmental , etc ) , a mention level ( named , nominal , etc ) , and a class ( specific , generic , etc ) . Detecting the mention boundaries ( set of consecutive tokens ) and their main type is one of the important steps of our mention detection system .
As computer vision techniques have improved over the past decade , other research has begun directly using visual information in place of feature norms . The first work to do this with topic models is Feng and Lapata ( 2010b ) . They use a Bag of Visual Words ( BoVW ) model ( Lowe , 2004 ) to create a bimodal vocabulary describing documents .
figures in brackets give the average number of generalised levels . The X2 statistic is performing at least as well as G2 , throwing doubt on the claim by Dunning ( 1993 ) that the G2 statistic is better suited for use in corpus-based NLP . The results show clearly that the average level of generalisation is slightly higher for G2 than X2 .
All the word alignments ( excluding stop-words ) with positive scores are selected as candidate fragment elements . Provided with the candidate fragment elements , we previously ( Wang and Callison-Burch , 2011 ) used a chunker3 to finalize the output fragments , in order to follow the linguistic definition of a ( para - ) phrase . We extend this step in the current system by applying a dependency parser to constrain the boundary of the fragments ( Sec .
from those that do not . This method of incorporating dictionary information seems simpler than the method proposed by Brown et al. for their models ( Brown et al. , 1993b ) . When the hidden parameters are conditioned on different link classes , the estimation method does not change ; it is just repeated for each link class .
In general , this means constructing inductive biases that take advantage of unannotated data to train probabilistic models . One important example is the constituentcontext model ( CCM ) of Klein and Manning ( 2002 ) , which was specifically designed to capture the linguistic observation made by Radford ( 1988 ) that there are regularities to the contexts in which constituents appear . This phenomenon , known as substitutability , says that phrases of the same type appear in similar contexts .
Typical examples are Bulgarian ( Simov et al. , 2005 ; Simov and Osenova , 2003 ) , Chinese ( Chen et al. , 2003 ) , Danish ( Kromann , 2003 ) , and Swedish ( Nilsson et al. , 2005 ) . Japanese ( Kawata and Bartels , 2000 ) , despite a very high accuracy , is different in that attachment score drops from 98 % to 85 % , as we go from length 1 to 2 , which may have something to do with the data consisting of transcribed speech with very short utterances . A second observation is that a high proportion of non-projective structures leads to fragmentation in the parser output , reflected in lower precision for roots .