hrithikpiyush/acl-arc huggingface dataset:
A key concern in corpus linguistics and related disciplines is verifiability and replicability of the results of studies . Word frequency counts in internet search engines are inconsistent and unreliable ( Veronis , 2005 ) . Tools based on static corpora do not suffer from this problem , e.g. BNCweb7 , developed at the University of Zurich , and View 8 ( Variation in English Words and Phrases , developed at Brigham Young University )
Most of early research work on person name ambiguity focuses on the CDC problem or uses methods found in the WSD literature . It is only recently that the web name ambiguity has been approached as a separate problem and defined as an NLP task Web People Search on its own ( Artiles et al. , 2005 ; Artiles et al. , 2007 ) . Therefore , it is useful to point out some crucial differences between WSD , CRC and WePS :
Further , compared to the retrieval , the local training is not the bottleneck . Actually , if we use LSH technique ( Andoni and Indyk , 2008 ) in retrieval process , the local method can be easily scaled to a larger training data .
However , as it stands , neither of these functions betrays anything about the syntactic nature of the constructs in question . Many lexicons , both automatically acquired and manually created , are more fine grained in their approaches to subcategorized clausal arguments , differentiating , for example , between a that-clause and a to + infinitive clause ( Ushioda et al. 1993 ) . With only a slight modification , our system , along with the details provided by the automatically generated f-structures , allows us to extract frames with an equivalent level of detail .
Some approaches apply semantic parsing , where words and sentences are mapped to logical structure meaning ( Kate and Mooney , 2007 ) . 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 ) .
A measure was derived for each annotated feature using the agreement analysis facility provided in ANVIL . Agreement between two annotation sets is calculated here in terms of Cohen 's kappa ( Cohen , 1960 ) 1 and corrected kappa ( Brennan and Prediger , 1981 ) 2 . Anvil divides the annotations in slices and compares each slice .
First , substantial understanding of the domain has already been codified in the Unified Medical Language System ( UMLS ) ( Lindberg , Humphreys , and McCray 1993 ) . Second , software for utilizing this ontology already exists : MetaMap ( Aronson 2001 ) identifies concepts in free text , and SemRep ( Rindflesch and Fiszman 2003 ) extracts relations between the concepts . Both systems utilize and propagate semantic information from UMLS knowledge sources : the Metathesaurus , the Semantic Network , and the SPECIALIST lexicon .
Numbers under `` ECMF '' are Entity-Constrained-Mention F-measure and numbers under `` ACE-Val '' are ACE-values . These types of features result in an improvement in both the mention detection and coreference resolution performance , as shown through experiments on the ACE 2004 Arabic data . The experiments are performed on a clearly specified partition of the data , so comparisons against the presented work can be correctly and accurately made in the future .
We do not use any other lexical 0-features that reference x , for fear that they would enable the learner to explain the rationales without changing 0 as desired ( see the end of section 5.3 ) . 14We parse each sentence with the Collins parser ( Collins , 1999 ) . Then the document has one big parse tree , whose root is DOC , with each sentence being a child of DOC .
Word reordering between German and English is a complex problem . Encouraged by the success of chunk-based verb reordering lattices on ArabicEnglish ( Bisazza and Federico , 2010 ) , we tried to adapt the same approach to the German-English language pair . It turned out that there is a larger variety of long reordering patterns in this case .
In order to validate such claim we perform a lexical decision experiment using 32 native Bangla speakers with 92 different verb sequences . We followed the same experimental procedure as discussed in ( Taft , 2004 ) for English polymorphemic words . However , rather than derived words , the subjects were shown a verb sequence and asked whether they recognize them as a valid combination .
In our experiments , we employed the well-known classifier SVM `` ght to obtain individual-document classification scores , treating Y as the positive class and using plain unigrams as features .5 Following standard practice in sentiment analysis ( Pang et al. , 2002 ) , the input to SVM `` ght consisted of normalized presence-of-feature ( rather than frequency-of-feature ) vectors . The ind value 5SVMlight is available at svmlight.joachims.org .
Generally , the problem is not so much to state the correspondances between synonymic but syntactically different constructs as to do this in a general way while not overgeneralising . To address this problem , we are currently working on developing a metagrammar in the sense of ( Candito , 1999 ) . This metagrammar allows us to factorise both syntactic and semantic information .
For example , in considering the connection between syntax and phrasing , the linguistic literature most often refers to examples of embedded sentences . Sentences like 12 , from Chomsky ( 1965 ) , are frequently cited . ( Square brackets mark off the NP constituents that contain embedded sentences . )
This paper made the first attempt on Chinese SRL and produced promising results . After the PropBank ( Xue and Palmer 2003 ) was built , Xue and Palmer ( 2005 ) and Xue ( 2008 ) have produced more complete and systematic research on Chinese SRL . Moschitti et al. ( 2005 ) has made some preliminary attempt on the idea of hierarchical semantic
In this paper , we seek to learn from only raw data and an incomplete dictionary mapping some words to sets of potential supertags . In order to estimate the parameters of our model , we develop a blocked sampler based on that of Johnson et al. ( 2007 ) to sample parse trees for sentences in the raw training corpus according to their posterior probabilities . However , due to the very large sets of potential supertags used in a parse , computing inside charts is intractable , so we design a Metropolis-Hastings step that allows us to sample efficiently from the correct posterior .
Chen and Vijay-Shanker ( 2000 ) explore a number of related approaches to the extraction of a lexicalized TAG from the Penn-II Treebank with the aim of constructing a statistical model for parsing . The extraction procedure utilizes a head percolation table as introduced by Magerman ( 1995 ) in combination with a variation of Collins 's ( 1997 ) approach to the differentiation between complement and adjunct . This results in the construction of a set of lexically anchored elementary trees which make up the TAG in question .
This means that the microplanner will not be able to make optimal pronominalization decisions in cases where le or la are unambiguous , but l' is not , since it does not know word order and hence whether the pronoun will be abbreviated . Many other such cases are described in Danlos 's book ( Danlos 1987 ) . The common theme behind many of these examples is that pipelines have difficulties satisfying linguistic constraints ( such as unambiguous reference ) or performing linguistic optimizations ( such as using pronouns instead of longer referring expressions whenever possible ) in cases where the constraints or optimizations depend on decisions made in multiple modules .
For example , the pattern `` What is the population '' gets replaced by `` NUMBER$ population '' . • Before indexing the text , we process it with Textract ( Byrd and Ravin , 1998 ; Wacholder et al. , 1997 ) , which performs lemmatization , and discovers proper names and technical terms . We added a new module ( Resporator ) which annotates text segments with QA-Tokens using pattern matching .
Put differently , we are interested in the tradeoff between relevance and accuracy . Therefore , we repeated the experiments with POS tags predicted by the MADA toolkit ( Habash and Rambow 2005 ; Habash , Rambow , and Roth 2012 ) 15 ( see Table 2 , 14 Some parsers predict POS tags internally , instead of receiving them as input , but this is not the case in this article . 15 We use MADA v3 .1 in all of our experiments .
For semantic analysis , developing features that capture the right kind of information is crucial . Experiments on Chinese SRL ( Xue and Palmer 2005 , Xue 2008 ) reassured these findings . In this paper , we mainly focus on the semantic role classification ( SRC ) process .
However , it will be increasingly important as techniques become more complex and corpus sizes grow . An example of this is the estimation of maximum entropy models , from simple iterative estimation algorithms used by Ratnaparkhi ( 1998 ) that converge very slowly , to complex techniques from the optimisation literature that converge much more rapidly ( Malouf , 2002 ) . 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 knowledge sources used for computing relatedness can be as different as dictionaries , ontologies or large corpora . According to Budanitsky and Hirst ( 2006 ) , there are three prevalent approaches for evaluating SR measures : mathematical analysis , applicationspecific evaluation and comparison with human judgments . Mathematical analysis can assess a measure with respect to some formal properties , e.g. whether a measure is a metric ( Lin , 1998 ) .4 However , mathematical analysis can not tell us whether a measure closely resembles human judgments or whether it performs best when used in a certain application .
The other system components use the preprocessing tools only as part of candidate generation ( e.g. , to identify all nouns in the data for the noun classifier ) . The choice of learning algorithm for each classifier is motivated by earlier findings showing that discriminative classifiers outperform other machine-learning methods on error correction tasks ( Rozovskaya and Roth , 2011 ) . Thus , the classifiers trained on the learner data make use of a discriminative model .
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 ) . More recently , ( Sebastiani , 2002 ) has performed a good survey of document categorization ; recent works can also be found in ( Joachims , 2002 ) , ( Crammer and Singer , 2003 ) , and ( Lewis et al. , 2004 ) .
• In many cases of interest , Ti is an acyclic graph .20 Then Tar an 's method computes w0j for each j in topologically sorted order , thereby finding ti in a linear number of ⊕ and ⊗ operations . For HMMs ( footnote 11 ) , Ti is the familiar trellis , and we would like this computation of ti to reduce to the forwardbackward algorithm ( Baum , 1972 ) . But notice that it has no backward pass .
This approach resulted in too many compounds . We follow Stymne and Cancedda ( 2011 ) , for compound merging . We trained a CRF using ( nearly all ) of the features they used and found their approach to be effective ( when combined with inflection and portmanteau merging ) on one of our two test sets .
In this case , the verb has no information concerning its subject , and so it identifies it as an unbound pronoun . Semantic filters can also be used to prevent multiple versions of the same case frame ( Fillmore 1968 ) showing up as complements . For instance , the set of complements [ from-place ] , [ to-place ] , and [ at-time ] are freely ordered following a movement verb such as `` leave . ''
CompuTerm 2004 3rd International Workshop on Computational Terminology 43 To construct this test set , we have focused our attention on ten domain-specific terms : commande ( command ) , configuration , fichier ( file ) , Internet , logiciel ( software ) , option , ordinateur ( computer ) , serveur ( server ) , systeme ( system ) , utilisateur ( user ) . The terms have been identified as the most specific to our corpus by a program developed by Drouin ( 2003 ) and called TER1vloSTAT . The ten most specific nouns have been produced by comparing our corpus of computing to the French corpus Le Monde , composed of newspaper articles ( Lemay et al. , 2004 ) .
Unlabeled attachment accuracy score ( UAS ) and label accuracy ( dependency relation regardless of parent , LS ) are also given . For statistical significance , we use McNemar 's test on non-gold LAS , as implemented by Nilsson and Nivre ( 2008 ) . We denote p < 0.05 and p < 0.01 with + and + + , respectively .
An artificial example will appear in § 2 . The availability of toolkits for this weighted case ( Mohri et al. , 1998 ; van Noord and Gerdemann , 2001 ) promises to unify much of statistical NLP . Such tools make it easy to run most current approaches to statistical markup , chunking , normalization , segmentation , alignment , and noisy-channel decoding , ' including classic models for speech recognition ( Pereira and Riley , 1997 ) and machine translation ( Knight and Al-Onaizan , 1998 ) .
In the CLE-QLF approach , as ra - tionally reconstructed by Alshawi and Crouch ( 1992 ) and Crouch and Putman ( 1994 ) , the context-independent meaning of a sentence is given by one or more QLFs that are built directly from syntactic and semantic rules . Just as here , these QLFs represent the basic predicate argument structure of the sentence , and contain constructs which represent those aspects of the meaning of the sentence that are dependent on context .
Datasets The ACE-2004 dataset contains 443 documents . We use a standard split of 268 training documents , 68 development documents , and 106 testing documents ( Culotta et al. , 2007 ; Bengtson and Roth , 2008 ) . The OntoNotes-5 .0 dataset , which is released for the CoNLL-2012 Shared Task ( Pradhan et al. , 2012 ) , contains 3,145 annotated documents .
Fraser ( 2009 ) tried to solve the inflection prediction problem by simply building an SMT system for translating from stems to inflected forms . Bojar and Kos ( 2010 ) improved on this by marking prepositions with the case they mark ( one of the most important markups in our system ) . Both efforts were ineffective on large data sets .
Just like OT grammars , HMM Viterbi decoders are functions that pick the optimal output from E * , based on criteria of well-formedness ( transition probabilities ) and faithfulness to the input ( emission probabilities ) . But typical OT grammars offer much richer finite-state models of left context ( Eisner 1997a ) than provided by the traditional HMM finite-state topologies . Now , among methods that use a Gibbs distribution to choose among linguistic forms , OT generation is special in that the distribution ranks the features strictly , rather than weighting them in a gentler way that allows tradeoffs .
This gives a greater flexibility but the tradeoff is that these tools can run very slowly . Other tools have been designed around particular techniques , such as finite state machines ( Karttunen et al. , 1997 ; Mohri et al. , 1998 ) . However , the source code for these tools is not freely available , so they can not be extended .
As can be seen , with an average score of 51.8 the improvement over the English only variant ( 50.6 ) is minimal . This contrasts with the findings described in Rapp & Zock ( 2010 ) where significant improvements could be achieved by increasing the number of source languages . So this casts some doubt on these .
FindBestValue selects the ` best value ' from among the Values of a given Attribute , assuming that these are linearly ordered in terms of specificity . The function selects the Value that removes most distractors , but in case of a tie , the least specific contestant is chosen , as long as it is not less specific than the basic-level Value ( i.e. , the most commonly occurring and psychologically most fundamental level , Rosch 1978 ) . IAPlur can refer to individuals as well as sets , since reference to a target individual r can be modeled as reference to the singleton set -LCB- r -RCB- .
However , even with the present setup , automatic extraction of concept pairs performs remarkably well and can be used to quickly create balanced test datasets . Budanitsky and Hirst ( 2006 ) pointed out that distribution plots of judgments for the word pairs used by Rubenstein and Goodenough display an empty horizontal band that could be used to separate related and unrelated pairs . This empty band is not observed here .
This approach is the one taken ( implicitly or explicitly ) in the majority of generators . In fact , Reiter has even argued in favor of this approach , claiming that the interactions are sufficiently minor to be ignored ( or at least handled on an ad hoc basis ) ( Reiter 1994 ) . While this certainly has appeal as a design methodology , it seems reckless to assume that problems will never appear .
The parse with the best reordering score is then fixed and added back to the training set and a new parser is trained on resulting data . The method is called targeted self-training as it is similar in vein to self-training ( McClosky et al. , 2006 ) , with the exception that the new parse data is targeted to produce accurate word reorderings . Our method differs as it does not statically fix a new parse , but dynamically updates the parameters and parse selection by incorporating the additional loss in the inner loop of online learning .
Here we address a different angle regarding conversation scripts , namely conversation entailment . In our previous work ( Zhang and Chai , 2009 ) , we started an initial investigation on conversation entailment . We have collected a dataset of 875 instances .
Butt ( 1993 ) argues CV formations in Hindi and Urdu are either morphological or syntactical and their formation take place at the argument structure . 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 .
One way to increase the precision of the mapping process is to impose some linguistic constraints on the sequences such as simple noun-phrase contraints ( Gaussier , 1995 ; Kupiec , 1993 ; hua Chen and Chen , 94 ; Fung , 1995 ; Evans and Zhai , 1996 ) . It is also possible to focus on non-compositional compounds , a key point in bilingual applications ( Su et al. , 1994 ; Melamed , 1997 ; Lin , 99 ) . Another interesting approach is to restrict sequences to those that do not cross constituent boundary patterns ( Wu , 1995 ; Furuse and Iida , 96 ) .
In Arabic , however , the use of stems will not yield satisfactory categorization . This is mainly due to the fact that Arabic is a non-concatenative language ( Al-Shalabi and Evens , 1998 ) , and that the stem/infix obtained by suppression of infix and prefix add-ons is not the same for words derived from the same origin called the root . The infix form ( or stem ) needs further to be processed in order to obtain the root .
4 http://mallet.cs.umass.edu/ ear regression adapted for classification ( Ting and Witten 1999 ) , which can be described by the following equation : Pk is the probability that a sentence specifies an outcome , as determined by classifier k ( for classifiers that do not return actual probabilities , we normalized the scores and treated them as such ) .
Not only do these methods require additional programming outside the toolkit , but they are limited to particular kinds of models and training regimens . For example , the forward-backward algorithm ( Baum , 1972 ) trains only Hidden Markov Models , while ( Ristad and Yianilos , 1996 ) trains only stochastic edit distance . In short , current finite-state toolkits include no training algorithms , because none exist for the large space of statistical models that the toolkits can in principle describe and run .
Using the tree-cut technique described above , our previous work ( Tomuro , 2000 ) extracted systematic polysemy from WordNet . In this section , we give a summary of this method , and describe the cluster pairs obtained by the method .
Several works have proposed discriminative techniques to train log-linear model for SMT . ( Och and Ney , 2002 ; Blunsom et al. , 2008 ) used maximum likelihood estimation to learn weights for MT. ( Och , 2003 ; Moore and Quirk , 2008 ; Zhao and Chen , 2009 ; Galley and Quirk , 2011 ) employed an evaluation metric as a loss function and directly optimized it . ( Watanabe et al. , 2007 ; Chiang et al. , 2008 ; Hopkins and May , 2011 ) proposed other optimization objectives by introducing a margin-based and ranking-based indirect loss functions .
Nonetheless , in the future , it may be worth investigating a TF.IDF-based representation . 5 Significant bigrams are obtained using the n-gram statistics package NSP ( Banerjee and Pedersen 2003 ) , which offers statistical tests to decide whether to accept or reject the null hypothesis regarding a bigram ( that it is not a collocation ) . As discussed in Section 2 , there are situations that can not be addressed by a document-level approach , because requests only predict or match portions of responses .
In other words , instead of performing lexical substitution directly to the text , the secret message is embedded into syntactic parse trees of the sentences . Liu et al. ( 2005 ) , Meral et al. ( 2007 ) , Murphy ( 2001 ) , Murphy and Vogel ( 2007 ) and Topkara et al. ( 2006a ) all belong to the syntactic transformation category . After embedding the secret message , modified deep structure forms are converted into the surface structure format via language generation tools .
We generated word n-grams of length 2 to 5 , 2The WePS-1 corpus includes data from the Web03 testbed ( Mann , 2006 ) which follows similar annotation guidelines , although the number of document per ambiguous name is more variable . 3Both corpora are available from the WePS website http://nlp.uned.es/weps 4A very sparse feature might never occur in a sentence with the person name .
2 Local Training and Testing The local training method ( Bottou and Vapnik , 1992 ) is widely employed in computer vision ( Zhang et al. , 2006 ; Cheng et al. , 2010 ) . Compared with the global training method which tries to fit a single weight on the training data , the local one learns weights based on the local neighborhood information for each test example .
However , the greatest increase is in the amount of raw text available to be processed , e.g. the English Gigaword Corpus ( Linguistic Data Consortium , 2003 ) . Recent work ( Banko and Brill , 2001 ; Curran and Moens , 2002 ) has suggested that some tasks will benefit from using significantly more data . Also , many potential applications of NLP will involve processing very large text databases .
The work of Hearst ( 1996 ) demonstrates that faceted queries can be converted into simple filtering constraints to boost precision . The feasibility of automatically identifying outcome statements in secondary sources has been demonstrated by Niu and Hirst ( 2004 ) . Their study also illustrates the importance of semantic classes and relations .