hrithikpiyush/acl-arc huggingface dataset:
In other words , we train a SC model that assumes as input a post sequence and outputs a stance sequence , with one stance label for each post in the input post sequence . This choice is motivated by an observation we made previously ( Hasan and Ng , 2013a ) : since each post in a sequence is a reply to the preceding post , we could exploit their dependencies by determining their stance labels together .3 As our sequence learner , we employ a maximum entropy Markov model ( MEMM ) ( McCallum et al. , 2000 ) . Given an input post sequence PS = ( p1 , p2 , ... , pn ) , the MEMM finds the most probable stance sequence S = ( s1 , s2 , ... , sn ) by computing P ( S | PS ) , where :
Despite these arguments , most applied NLG systems use a pipelined architecture ; indeed , a pipeline was used in every one of the systems surveyed by Reiter ( 1994 ) and Paiva ( 1998 ) . This may be because pipelines have many engineering advantages , and in practice the sort of problems pointed out by Danlos and other pipeline critics do not seem to be a major problem in current applied NLG systems ( Mittal et al. 1998 ) .
Verbs are viewed as simple predicates over their arguments . This approach has its roots in Fillmore 's Case Grammar ( 1968 ) , and serves as the foundation for two current large-scale semantic annotation projects : FrameNet ( Baker et al. , 1998 ) and PropBank ( Kingsbury et al. , 2002 ) . Underlying the semantic roles approach is a lexicalist assumption , that is , each verb 's lexical entry completely encodes ( more formally , projects ) its syntactic and semantic structures .
Functions defined here decide what string should be spoken and send that string to the speech output module based on the current dialogue state . They can also shift the dialogue 2The notion of the initiative in this paper is different from that of the dialogue initiative of Chu-Carroll ( 2000 ) . phase and change the holder of the initiative as well as change the dialogue state .
The Google n-gram data was collected by Google Research for statistical language modelling , and has been used for many tasks such as lexical disambiguation ( Bergsma et al. , 2009 ) , and contains English n-grams and their observed frequency counts , for counts of at least 40 . The striking feature of
The most basic is a Bag of Words ( BoW ) representation of the document text . Withindocument coreference resolution has been applied to produce summaries of text surrounding occurrences of the name ( Bagga and Baldwin , 1998 ; Gooi and Allan , 2004 ) . Nevertheless , the full document text is present in most systems , sometimes as the only feature ( Sugiyama and Okumura , 2007 ) and sometimes in combination with others see for instance ( Chen and Martin , 2007 ; Popescu and Magnini , 2007 ) - .
For non predicative units , we use WordNet synset numbers or when working within a restricted domain with a well defined thesaurus , the descriptors of that thesaurus . To represent the semantics of predicative units , we use FrameNet inventory of frames and frame elements ( C.Johnson et al. , 2002 ) . FrameNet is an online lexical resource for English based on the principles of Frame Semantics .
The multimodal data we obtained by combining the linguistic annotations from DanPASS with the gesture annotation created in ANVIL , resulted into two different groups of data , one containing all Yes and No expressions , and the other the subset of those that are accompanied by a face expression or a head movement , as shown in Table 4 . These two sets of data were used for automatic dialogue act classification , which was run in the Weka system ( Witten and Frank , 2005 ) . We experimented with various Weka classifiers , comprising Hidden Naive Bayes , SMO , ID3 , LADTree and Decision Table .
where W 's are query words in Q. Miller et al. estimated probabilities as follows : • The transition probability a is 0.7 using the EM algorithm ( Rabiner , 1989 ) on the TREC4 ad-hoc query set . P ( v IGx ) = number of occurrences of W in Cx • length of Cx which is the general language probability for word Win language x. • P ( v I D ) = number of occurrences of W in D length of D
Various feature selection techniques have been used in automatic text categorization ; they include document frequency ( DF ) , information gain ( IG ) ( Tzeras and Hartman , 1993 ) , minimum description length principal ( Lang , 1995 ) , and the X2 statistic . ( Yang and Pedersen , 1997 ) has found strong correlations between DF , IG and the X2 statistic for a term . On the other hand , ( Rogati and Yang , 2002 ) reports the X2 to produce best performance .
This indicates that help-desk customers may have also become more tolerant of inaccurate or incomplete automatically generated replies , provided these replies are still relevant to their problem , and so long as the customers can follow up with a request for human-generated responses if necessary . Despite this , to date , there has been little work on corpus-based approaches to help-desk response automation ( notable exceptions are Carmel , Shtalhaim , and Soffer 2000 ; Lapalme and Kosseim 2003 ; Bickel and Scheffer 2004 ; Malik , Subramaniam , and Kaushik 2007 ) . A major factor limiting this work is the dearth of corpora -- help-desk e-mails tend to be proprietary and are subject to privacy issues .
The first ordered pairs are more frequent , as are the individual adjectives involved . To quantify the relative strengths of these transitive inferences , Shaw and Hatzivassiloglou ( 1999 ) propose to assign a weight to each link . Say the order ( a , b ) occurs m times and the pair -LCB- a , b -RCB- occurs n times in total .
In addition , in order to link the machine readable version of LDOCE to our development environment , and eventually to our natural language processing systems , we need to provide fast access from Lisp to data held in secondary storage . Lisp is not particularly well suited for interfacing to complex , structured objects , and it was not our intention to embark on a major effort involving the development of a formal model of a dictionary ( of the style described in , eg. , Tompa 1986 ) ; on the other hand a method of access was clearly required , which was flexible enough to support a range of applications intending to make use of the LDOCE tape . The requirement for having the dictionary entries in a form convenient for symbolic manipulation from within Lisp was furthermore augmented by the constraint that all the information present in the typesetting tape should be carried over to the on-line version of LDOCE , since it is impossible to say in advance which records and fields of an entry would , or would not , be of potential use to a natural language processing program .
We run GIZA + + ( Och and Ney , 2000 ) on the training corpus in both directions ( Koehn et al. , 2003 ) to obtain the word alignment for each sentence pair . We train a 4-gram language model on the Xinhua portion of the English Gigaword corpus using the SRILM Toolkits ( Stolcke , 2002 ) with modified Kneser-Ney smoothing ( Chen and Goodman , 1998 ) . In our experiments the translation performances are measured by case-insensitive BLEU4 metric ( Papineni et al. , 2002 ) and we use mtevalv13a.pl as the evaluation tool .
Examples of the heuristics operation on the MUC data are presented presented in Table 2 . Details of the top performing heuristics of COCKTAIL were reported in ( Harabagiu and Maiorano , 1999 ) .
None of these verbs take sentential complements and therefore they appear to be counterexamples to our Object Raising rule . In addition , Moulin et al. ( 1985 ) note that our Object Raising rule would assign mean to this category incorrectly . Mean is assigned both a V3 and a T5 category in the code field associated with sense 2 ( i.e. `` intend '' ) , however , when it is used in this sense it must be treated as an Object Equi verb .
Our system uses a standard phrase-based architecture . The parallel corpus is word-aligned using GIZA + + ( Och and Ney , 2003 ) . Phrase translations of up to 10 words are extracted in the Moses phrase table .
7See http://www.cs.ualberta.ca/∼lindek/downloads.htm 8In our implementation of Soon 's method , we label an instance as OTHERS if no NE or WN CLASS feature is generated ; otherwise its label is the value of the NE feature or the ACE SC that has the WN CLASS features as its keywords ( see Table 1 ) . tions for the remaining 20 % of the instances ; and ( 3 ) train an SVM classifier ( using the LIBSVM package ( Chang and Lin , 2001 ) ) on these 20 % of the instances , where each instance , i , is represented by a set of 31 binary features . More specifically , let Li = 141 , 42 , 43 , 44 , 451 be the set of predictions that we obtained for i in step ( 2 ) .
would be labeled as `` ( ( Several fund managers ) ( expect ( ( a rough market ) ( this morning ) ) ( before ( prices stabilize ) ) ) . ) '' Our algorithm is similar to the approach taken by Pereira and Schabes ( 1992 ) for inducing PCFG parsers . Because the EM algorithm itself is an iterative procedure , performing sample selection on top of an EM-based learner is an extremely computational-intensive process .
Miller and Charles ( 1991 ) replicated the experiment with 38 test subjects judging on a subset of 30 pairs taken from the original 65 pairs . This experiment was again replicated by Resnik ( 1995 ) with 10 subjects . Table 1 summarizes previous experiments .
A search of the British National Corpus ( BNC ) , for example , shows at least seven of the ten most frequent adjectives ( last , other , new , good , old , great , high , small , different , large ) to be gradable . Children use vague adjectives among their first dozens of words ( Peccei 1994 ) and understand some of their intricacies as early as their 24th month ( Ebeling and Gelman 1994 ) . These
For compound splitting , we follow Fritzinger and Fraser ( 2010 ) , using linguistic knowledge encoded in a rule-based morphological analyser and then selecting the best analysis based on the geometric mean of word part frequencies . Other approaches use less deep linguistic resources ( e.g. , POS-tags Stymne ( 2008 ) ) or are ( almost ) knowledge-free ( e.g. , Koehn and Knight ( 2003 ) ) . Compound merging is less well studied .
Again , this represents our attempt to coarsely model subcategorization . ( 4 ) NE : We use BBN 's IdentiFinder ( Bikel et al. , 1999 ) , a MUC-style NE recognizer to determine the NE type of NPZ . If NPZ is determined to be a PERSON or ORGANIZATION , we create an NE feature whose
Although we make discriminations of arguments and adjuncts , the analysis is still coarse-grained . Yi et al. ( 2007 ) has made the first attempt working on the single semantic role level to make further improvement . However , the impact of this idea is limited due to that the amount of the research target , ARG2 , is few in PropBank .
Task properties Determining whether or not a speaker supports a proposal falls within the realm of sentiment analysis , an extremely active research area devoted to the computational treatment of subjective or opinion-oriented language ( early work includes Wiebe and Rapaport ( 1988 ) , Hearst ( 1992 ) , Sack ( 1994 ) , and Wiebe ( 1994 ) ; see Esuli ( 2006 ) for an active bibliography ) . In particular , since we treat each individual speech within a debate as a single `` document '' , we are considering a version of document-level sentiment-polarity classification , namely , automatically distinguishing between positive and negative documents ( Das and Chen , 2001 ; Pang et al. , 2002 ; Turney , 2002 ; Dave et al. , 2003 ) . Most sentiment-polarity classifiers proposed in the recent literature categorize each document independently .
Length of words ( L ) : Instead of using the raw length value as a feature , we follow our previous work ( Rubino et al. , 2013 ; Wagner et al. , 2014 ) and create multiple features for length using a decision tree ( J48 ) . We use length as the only feature to train a decision tree for each fold and use the nodes obtained from the tree to create boolean features .
Both efforts were ineffective on large data sets . Williams and Koehn ( 2011 ) used unification in an SMT system to model some of the agreement phenomena that we model .
Following Ruch et al. ( 2003 ) and Barzilay and Lee ( 2004 ) , we employed Hidden Markov Models to model the discourse structure of MEDLINE abstracts . The four states in our HMMs correspond to the information that characterizes each section ( `` introduction '' , `` methods '' , `` results '' , and `` conclusions '' ) and state transitions capture the discourse flow from section to section .
3.2.3 Strategy Selection Previous work has argued that initiative affects the degree of control an agent has in the dialogue interaction ( Whittaker and Stenton , 1988 ; Walker and Whittaker , 1990 ; Chu-Carroll and Brown , 1998 ) . Thus , a cooperative system may adopt different strategies to achieve the same goal depending on the initiative distribution .
Considering the original definition of S-TAGs , one can see that it does not restrict the structures that can be produced in the source and target languages . It allows the construction of a non-TAL ( Shieber , 1994 ) , ( Harbusch & Poller , 2000 ) . As a result , Shieber ( 1994 ) propose a restricted definition for S-TAG , namely , the IS-TAG for isomorphic S-TAG .
This topic generated intense interest at workshops held at the University of Heidelberg ( October 2004 ) , University of Bologna ( January 2005 ) , University of Birmingham ( July 2005 ) and now in Trento in April 2006 . In addition , the advantages of using linguistically annotated data over raw data are well documented ( Mair , 2005 ; Granger and Rayson , 1998 ) . As the size of a corpus increases , a near linear increase in computing power is required to annotate the text .
This is motivated by the aim to enable research in information retrieval incorporating SR measures . In particular , the `` Semantic Information Retrieval '' project ( SIR Project , 2006 ) systematically investigates the use of lexical-semantic relations between words or concepts for improving the performance of information retrieval systems . The BERUFEnet ( BN ) corpus7 consists of descriptions of 5,800 professions in Germany and therefore contains many terms specific to professional training .
To avoid overfitting we encode only a small set of linguistically motivated dependencies in features over sets of the random variables . Our re-ranking approach , like the approach to parse re-ranking of Collins ( 2000 ) , employs a simpler model -- a local semantic role labeling algorithm -- as a first pass to generate a set of n likely complete assignments of labels to all parse tree nodes . The joint model is restricted to these n assignments and does not have to search the exponentially large space of all possible joint labelings .
To put together a few rules is fast and easy , but to develop a rule-based system with good performance is quite a labor-consuming enterprise . For instance , the Alembic workbench ( Aberdeen et al. 1995 ) contains a sentence-splitting module that employs over 100 regular-expression rules written in Flex . Another well-acknowledged shortcoming of rule-based systems is that such systems are usually closely tailored to a particular corpus or sublanguage and are not easily portable across domains .
This is because we can not combine scores from different instances . In contrast , a single statistical model allows one to maintain a single table ( Langkilde , 2000 ) . Second , the cosine score of an edge can increase as well as decrease when it is combined with other edges , depending on whether or not the newly added terms match the instance terms .
Our experiments are based on the multimodal extension of Latent Dirichlet Allocation developed by Andrews et al. ( 2009 ) . Previously LDA has been successfully used to infer unsupervised joint topic distributions over words and feature norms together ( Andrews et al. , 2009 ; Silberer and Lapata , 2012 ) .
Research on shallow parsing was inspired by psycholinguistics arguments ( Gee and Grosjean , 1983 ) that suggest that in many scenarios ( e.g. , conversational ) full parsing is not a realistic strategy for sentence processing and analysis , and was further motivated by several arguments from a natural language engineering viewpoint . First , it has been noted that in many natural language applications it is sufficient to use shallow parsing information ; information such as noun phrases ( NPs ) and other syntactic sequences have been found useful in many large-scale language processing applications including information extraction and text summarization ( Grishman , 1995 ; Appelt et al. , 1993 ) . Second , while training a full parser requires a collection of fully parsed sentences as training corpus , it is possible to train a shallow parser incrementally .
They utilized the bilingual Tree-bank to train a joint model for both parsing and word alignment . Cohn and Blunsom ( 2009 ) adopted a Bayesian method to infer an STSG by exploring the space of alignments based on parse trees . Liu et al. ( 2012 ) re-trained the linguistic parsers bilingually based on word alignment .
Two exceptions to this generalisation are the Linguistic String Project ( Sager , 1981 ) and the IBM CRITIQUE ( formerly EPISTLE ) Project ( Heidorn et al. , 1982 ; Byrd , 1983 ) ; the former employs a dictionary of approximately 10,000 words , most of which are specialist medical terms , the latter has well over 100,000 entries , gathered from machine readable sources . In addition , there are a number of projects under way to develop substantial lexicons from machine readable sources ( see Boguraev , 1986 for details ) . However , as yet few results have been published concerning the utility of electronic versions of published dictionaries as sources for such lexicons .
The analytical parsing of Czech runs in two steps : the statistical dependency parser , which creates the structure of a dependency tree , and a classifier assigning analytical functors . We carried out two parallel experiments with two parsers available for Czech , parser I ( Hajie et al. , 1998 ) and parser II ( Charniak , 1999 ) . In the second step , we used a module for automatic analytical functor assignment ( 2abokrtskyT et al. , 2002 ) .
However , knowledge of sentence boundaries is required by many NLP technologies . Part of speech taggers typically require input in the format of a single sentence per line ( for example Brill 's tagger ( Brill , 1992 ) ) and parsers generally aim to produce a tree spanning each sentence . Only the most trivial linguistic analysis can be carried out on text which is not split into sentences .
We thereby eliminate the redundant nondeterminism resulting from multiply defined frame predicates . The elimination of redundant nondeterminism is based on Unfold/Fold transformation techniques ( Tamaki and Sato 1984 ) .29 The unfolding transformation is also referred to as partial execution , for example , by Pereira and Shieber ( 1987 ) . Intuitively understood , unfolding comprises the evaluation of a particular literal in the body of a clause at compile-time .
Since the SURF and GIST image features tend to capture object-likeness and scene-likeness respectively , it is possible that words which share associates are likely related through common settings and objects that appear with them . This seems to provide additional evidence of Bruni et al. ( 2012b ) 's suggestion that something like a distributional hypothesis of images is plausible . Once again , the clusters of images using SURF causes a dramatic drop in performance .
Many provide graphical user interfaces ( GUI ) for manual annotation ( e.g. General Architecture for Text Engineering ( GATE ) ( Cunningham et al. , 1997 ) and the Alembic Workbench ( Day et al. , 1997 ) ) as well as NLP tools and resources that can be manipulated from the GUI . For instance , GATE currently provides a POS tagger , named entity recogniser and gazetteer and ontology editors ( Cunningham et al. , 2002 ) . GATE goes beyond earlier systems by using a component-based infrastructure ( Cunningham , 2000 ) which the GUI is built on top of .
We would like to use features that look at wide context on the input side , which is inexpensive ( Jiampojamarn et al. , 2007 ) . Latent variables we wish to consider are an increased number of word classes ; more flexible regions -- see Petrov et al. ( 2007 ) on learning a state transition diagram for acoustic regions in phone recognition -- and phonological features and syllable boundaries . Indeed , our local log-linear features over several aligned latent strings closely resemble the soft constraints used by phonologists ( Eisner , 1997 ) .
The outcomes of these classifiers are then combined in a way that satisfies some constraints -- non-overlapping constraints in this case -- using an efficient constraint satisfaction mechanism that makes use of the confidence in the classifier 's outcomes . Since earlier versions of the SNoW based CSCL were used only to identify single phrases ( Punyakanok and Roth , 2001 ; Munoz et al. , 1999 ) and never to identify a collection of several phrases at the same time , as we do here , we also trained and tested it under the exact conditions of CoNLL-2000 ( Tjong Kim Sang and Buchholz , 2000 ) to compare it to other shallow parsers . Table 1 shows that it ranks among the top shallow parsers evaluated there 1 .
In Figure 2 ( a ) , we find the distance between x11 and x9 is 3 . Using the implicit modeling of argument consistency , we follow the same approach as in our previous work ( Zhang and Chai , 2009 ) and trained a logistic regression model to predict verb alignment based on the features in Table 1 . 6.1.2 Explicit Modeling of AC
This paper describes the extraction of this , and other , information from LDOCE and discusses the utility of the coding system for automated natural language processing . Recent developments in linguistics , and especially on grammatical theory -- for example , Generalised Phrase Structure Grammar ( GPSG ) ( Gazdar et al. , 1985 ) , Lexical Functional Grammar ( LFG ) ( Kaplan and Bresnan , 1982 ) -- and on natural language parsing frameworks -- for example , Functional Unification Grammar ( FUG ) ( Kay , 1984a ) , PATR-II ( Shieber , 1984 ) -- make it feasible to consider the implementation of efficient systems for the syntactic analysis of substantial fragments of natural language . These developments also emphasise that if natural language processing systems are to be able to handle the grammatical and semantic idiosyncracies of individual lexical items elegantly and efficiently , then the lexicon must be a central component of the parsing system .
This example also highlights a deficiency in the LDOCE coding system since prefer occurs much more naturally with a sentential complement if it collocates with a modal such as `` would '' . This deficiency is rectified in the verb classification system employed by Jackendoff and Grimshaw ( 1985 ) in the Brandeis verb catalogue . The main source of error comes from the misclassification of Object Raising into Object Equi verbs .
The vector does not find points of interest in the image , but rather attempts to provide a representation for the overall `` gist '' of the whole image . It is frequently used in tasks like scene identification , and Deselaers and Ferrari ( 2011 ) shows that distance in GIST space correlates well with semantic distance in WordNet . After computing the GIST vectors , each textual word is represented as the centroid GIST vector of all its images , forming the GIST modality .
The base distribution P0 ( r | N ) is designed to assign prior probabilities to the STSG production rules . Because each rule r consists of a target tree fragment frag and a source string str in the model , we follow Cohn and Blunsom ( 2009 ) and decompose the prior probability P0 ( r | N ) into two factors as follows : where P ( frag | N ) is the probability of producing the target tree fragment frag .