hrithikpiyush/acl-arc huggingface dataset:
Thus , we can construct a forest and the mentions in the same connected component ( i.e. , in the same tree ) are co-referred . For this mention-pair coreference model φ ( u , v ) , we use the same set of features used in Bengtson and Roth ( 2008 ) . fu , vyu , v ,
This is roughly an 11 % relative reduction in error rate over Charniak ( 2000 ) and Bods PCFG-reduction reported in Table 1 . Compared to the reranking technique in Collins ( 2000 ) , who obtained an LP of 89.9 % and an LR of 89.6 % , our results show a 9 % relative error rate reduction . While SL-DOP and LS-DOP have been compared before in Bod ( 2002 ) , especially in the context of musical parsing , this paper presents the The DOP approach is based on two distinctive features : ( 1 ) the use of corpus fragments rather than grammar rules , and ( 2 ) the use of arbitrarily large fragments rather than restricted ones .
In this paper , we present a novel and realistic method for speeding up the training time of a transformation-based learner without sacrificing performance . The paper compares and contrasts the training time needed and performance achieved by our modified learner with two other systems : a standard transformation-based learner , and the ICA system ( Hepple , 2000 ) . The results of these experiments show that our system is able to achieve a significant improvement in training time while still achieving the same performance as a standard transformation-based learner .
The light verb vDO licenses an atelic non-inchoative event , and is compatible with verbal roots expressing activity . It projects a functional head , voice ( Kratzer , 1994 ) , whose specifier is the external argument . ( 10 ) John ran .
This approach , known as translation memory , example-based or case-based translation , can work on the word level as well as on structured examples as they are generated during analysis and generation in more grammar-based translation paradigms ( Kitano , 1993 ; Brown , 1996 ) . Finite state transducers , which can be learned from bilingual corpora , have been proposed for automatic translation ( Amengual et al. , 2000 ) , as have been bilingual stochastic grammars ( Wu , 1996 ) . Statistical approaches ( Wang and Waibel , 1997 ; Och et al. , 1999 ) also fall into the category of corpus based approaches .
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 . This is noticeable for German ( Brants et al. , 2002 ) and Portuguese ( Afonso et al. , 2002 ) , which still have high overall accuracy thanks to very high attachment scores , but much more conspicuous for Czech ( B ¨ ohmov ´ a et al. , 2003 ) , Dutch ( van der Beek et al. , 2002 ) and Slovene ( Dˇzeroski et al. , 2006 ) , where root precision drops more drastically to about 69 % , 71 % and 41 % , respectively , and root recall is also affected negatively . On the other hand , all three languages behave like high-accuracy languages with respect to attachment score .
Suppose the target is c4 : 3 The degree of precision of the measurement ( James et al. 1996 , Section 1.5 ) determines which objects can be described by the GRE algorithm , since it determines which objects count as having the same size . 4 To turn this likelihood into a certainty , one can add a test at the end of the algorithm , which adds a type-related property if none is present yet ( cfXXX , Dale and Reiter 1995 ) .
This standardisation of remote procedures is very exciting from a software engineering viewpoint since it allows systems to be totally distributed . There have already been several attempts to develop distributed NLP systems for dialogue systems ( Bayer et al. , 2001 ) and speech recognition ( Hacioglu and Pellom , 2003 ) . Web services will allow components developed by different researchers in different locations to be composed to build larger systems .
However , the IBM models , which attempt to capture a broad range of translation phenomena , are computationally expensive to apply . Table look-up using an explicit translation lexicon is sufficient and preferable for many multilingual NLP applications , including `` crummy '' MT on the World Wide Web ( Church & Hovy , 1993 ) , certain machine-assisted translation tools ( e.g. ( Macklovitch , 1994 ; Melamed , 1996b ) ) , concordancing for bilingual lexicography ( Catizone et al. , 1993 ; Gale & Church , 1991 ) , computerassisted language learning , corpus linguistics ( Melby . 1981 ) , and cross-lingual information retrieval ( Oard & Dorr , 1996 ) .
For both languages , we resolved coreference by using SWIZZLE , our implementation of a bilingual coreference resolver . SWIZZLE is a multilingual enhancement of COCKTAIL ( Harabagiu and Maiorano , 1999 ) , a coreference resolution system that operates on a mixture of heuristics that combine semantic and textual cohesive information ' . When COCKTAIL was applied separately on the English and the Romanian texts , coreferring links were identified for each English and Romanian document respectively .
Another common approach is term translation , e.g. , via a bilingual lexicon . ( Davis and Ogden , 1997 ; Ballesteros and Croft , 1997 ; Hull and ( 3refenstette , 1996 ) . 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 .
nphil.uni-tuebingen . de URL : http://www.sfs.nphil.uni-tuebingen.de/sfb / b4home.html 1 This is , for example , the case for all proposals working with verbal lexical entries that raise the arguments of a verbal complement ( Hinrichs and Nakazawa 1989 ) that also use lexical rules such as the Complement Extraction Lexical Rule ( Pollard and Sag 1994 ) or the Complement Cliticization Lexical Rule ( Miller and Sag 1993 ) to operate on those raised elements . Also an analysis treating adjunct extraction via lexical rules ( van Noord and Bouma 1994 ) results in an infinite lexicon .
There is a wealth of literature on trying to establish subsentential translations from a bilingual corpus .3 Kay and R ¨ oscheisen ( 1993 ) attempt to extract a bilingual dictionary using a hybrid method of sentence and word alignment on the assumption that the ( source , target ) words have a similar distribution . Fung and McKeown ( 1997 ) attempt to translate technical terms using word relation matrices , although the resource from which such relations are derived is a pair of nonparallel corpora . Somers ( 1998 ) replicates the work of Fung and McKeown with different language pairs using the simpler metric of Levenshtein distance .
For example , the misspelling identifier will specialize in identifying misspellings , the abbreviation component will specialize in identifying abbreviations , etc. . Each component will return a confidence measure of the reliability of its prediction , c.f. ( Elworthy , 1998 ) . The results from each component are evaluated to determine the final category of the word .
In both cases , the mention heads are sufficient to support the decisions : '' they '' refers to '' companies '' , and '' They '' refers to '' manufacturers '' . In fact , most of the features3 implemented in existing coreference resolution systems rely solely on mention heads ( Bengtson and Roth , 2008 ) . Furthermore , consider the possible mention candidate `` league '' ( italic in the text ) .
A detailed description of the kinds of expectation mechanisms appearing in these systems appears in Fink ( 1983 ) . The problem of handling ill-formed input has been studied by Carbonell and Hayes ( 1983 ) , Granger ( 1983 ) , Jensen et al. ( 1983 ) , Kwasny and Sondheimer ( 1981 ) , Riesbeck and Schank ( 1976 ) , Thompson ( 1980 ) , Weischedel and Black ( 1980 ) , and Weischedel and Sondheimer ( 1983 ) . A wide variety of techniques have been developed for addressing problems at the word , phrase , sentence , and in some cases , dialogue level .
Since lexical rules are expressed in the theory just like any other part of the theory , they are represented in the same way , as unary immediate dominance schemata . ' This conception of lexical rules thus can be understood as underlying the computational approach that treats lexical rules as unary phrase structure rules as , for example , adopted in the LKB system ( Copestake 1992 ) . Both the input and output of a lexical rule , i.e. , the mother and the daughter of a phrase structure rule , are available during a generation or parsing process .
Conventionally , these rules are specified by an expert linguist , conversant with the sound and spelling systems of the language of concern . Typical letter-to-sound rule sets are those described by Ainsworth ( 1973 ) , McIlroy ( 1973 ) , Elovitz et al. ( 1976 ) , Hurmicutt ( 1976 ) , and Divay and Vitale ( 1997 ) . Because of the complexities of English spelling-to-sound correspondence detailed in the previous section , more than one rule generally applies at each stage of transcription .
In an attempt to further boost performance , we employed Linear Discriminant Analysis ( LDA ) to find a linear projection of the four-dimensional vectors that maximizes the separation of the Gaussians ( corresponding to the HMM states ) . Venables and Ripley ( 1994 ) describe an efficient algorithm ( of linear complexity in the number of training sentences ) for computing the LDA transform matrix , which entails computing the withinand between-covariance matrices of the classes , and using Singular Value Decomposition ( SVD ) to compute the eigenvectors of the new space . Each sentence/vector is then multiplied by this matrix , and new HMM models are re-computed from the projected data .
For better comparison with work of others , we adopt the suggestion made by Green and Manning ( 2010 ) to evaluate the parsing quality on sentences up to 70 tokens long . We report these filtered results in Table 14 .
applications . Some examples include text categorization ( Lewis and Catlett 1994 ) , base noun phrase chunking ( Ngai and Yarowsky 2000 ) , part-of-speech tagging ( Engelson Dagan 1996 ) , spelling confusion set disambiguation ( Banko and Brill 2001 ) , and word sense disambiguation ( Fujii et al. 1998 ) . More challenging are learning problems whose objective is not classification , but generation of complex structures .
In addition , it should be noted that the word alignment is fixed8 , and we only explore the entire space of tree structures in our sampler . Differently , Cohn and Blunsom ( 2009 ) designed a sampler to infer an STSG by fixing the tree structure and exploring the space of alignment . We believe that it is possible to investigate the space of both tree structure and alignment simultaneously .
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 . The topic model using the bimodal vocabulary outperforms a purely textual based model in word association and word similarity prediction .
In either formalization we get BUT_Cl as a consequence : Since `` laws '' can not be deleted , BUT ca n't be applied , and hence its use in those kinds of sentences would be incorrect . W. Labov ( 1973 ) discussed sentences of the form * This is a chair but you can sit on it . The sentence is incorrect , since the function `` one can sit on it '' belongs to the core of the concept `` chair '' ; so -- contrary to the role of `` but '' -- the sentence does not contain any surprising new elements .
Baroni and Bernardini ( 2004 ) built a corpus by iteratively searching Google for a small set of seed terms . Prototypes of Internet search engines for linguists , corpus linguists and lexicographers have been proposed : WebCorp ( Kehoe and Renouf , 2002 ) , KWiCFinder ( Fletcher , 2004a ) and the Linguist 's Search Engine ( Kilgarriff , 2003 ; Resnik and Elkiss , 2003 ) . A key concern in corpus linguistics and related disciplines is verifiability and replicability of the results of studies .
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 . More recently , Silberer et al. ( 2013 ) show that visual attribute classifiers , which have been immensely successful in object recognition ( Farhadi et al. , 2009 ) , act as excellent substitutes for feature norms .
Some methods of semantic relation analysis rely on predefined templates filled with information from processed texts ( Baker et al. , 1998 ) . In other methods , lexical resources are specifically tailored to meet the requirements of the domain ( Rosario and Hearst , 2001 ) or the system ( Gomez , 1998 ) . Such systems extract information from some types of syntactic units ( clauses in ( Fillmore and Atkins , 1998 ; Gildea and Jurafsky , 2002 ; Hull and Gomez , 1996 ) ; noun phrases in ( Hull and Gomez , 1996 ; Rosario et al. , 2002 ) ) .
The second version ( RM ) concerns the Resource Management task ( Pallett 1989 ) that has been popular within the DARPA community in recent years . The third version ( VOYAGER ) serves as an interface both with a recognizer and with a functioning database back-end ( Zue et al. 1990 ) . The VOYAGER system can answer a number of different types of questions concerning navigation within a city , as well as provide certain information about hotels , restaurants , libraries , etc. , within the region .
based parsing algorithms with an arc-factored parameterization ( McDonald et al. , 2005 ) . We use the non-projective k-best MST algorithm to generate k-best lists ( Hall , 2007 ) , where k = 8 for the experiments in this paper . The graphbased parser features used in the experiments in this paper are defined over a word , wi at position i ; the head of this word wρ ( i ) where ρ ( i ) provides the index of the head word ; and partof-speech tags of these words ti .
The literature also contains work on sentence-level classification of MEDLINE abstracts for non-clinical purposes . For example , McKnight and Srinivasan ( 2003 ) describe a machine learning approach to automatically label sentences as belonging to introduction , methods , results , or conclusion using structured abstracts as training data ( see also Lin et al. 2006 ) . Tbahriti et al. ( 2006 ) have demonstrated that differential weighting of automatically labeled sections can lead to improved retrieval performance .
• Support vector machines for mapping histories to parser actions ( Kudo and Matsumoto , 2002 ) . • Graph transformations for recovering nonprojective structures ( Nivre and Nilsson , 2005 ) . All experiments have been performed using MaltParser ( Nivre et al. , 2006 ) , version 0.4 , which is made available together with the suite of programs used for preand post-processing .1
Klein and Manning ( 2002 ) 's CCM is an unlabeled bracketing model that generates the span of part-of-speech tags that make up each constituent and the pair of tags surrounding each constituent span ( as well as the spans and contexts of each non-constituent ) . They found that modeling constituent context aids in parser learning because it is able to capture the observation that the same contexts tend to appear repeatedly in a corpus , even with different constituents .
The Brown clustering algorithm works by starting with an initial assignment of word types to classes ( which is usually either one unique class per type or a small number of seed classes corresponding to the most frequent types in the corpus ) , and then iteratively selecting the pair of classes to merge that would lead to the highest post-merge log-likelihood , doing so until all classes have been merged . This process produces a hierarchical clustering of the word types in the corpus , and these clusterings have been found useful in many applications ( Ratinov and Roth , 2009 ; Koo et al. , 2008 ; Miller et al. , 2004 ) . There are other similar models of distributional clustering of English words which can be similarly effective ( Pereira et al. , 1993 ) .
Given these facts , research on automating acquisition of dictionaries for lexically based NLP systems is a particularly important issue . Aside from the extraction of theory-neutral subcategorization lexicons , there has also been work in the automatic construction of lexical resources which comply with the principles of particular linguistic theories such as LTAG , CCG , and HPSG ( Chen and Vijay-Shanker 2000 ; Xia 1999 ; Hockenmaier , Bierner , and Baldridge 2004 ; Nakanishi , Miyao , and Tsujii 2004 ) . In this article we present an approach to automating the process of lexical acquisition for LFG ( i.e. , grammatical-function-based systems ) .
This means that new e-transitions connect q to the start state of A , and the final states of A , to q ' . 1 The representation in Mohri and Pereira ( 1998 ) is even more compact than ours for grammars that are not self-embedding . However , in this paper we use our representation as an intermediate result in approximating an unrestricted context-free grammar , with the final objective of obtaining a single minimal deterministic automaton .
As a result , the literal can be removed from the body of 29 This improvement of the covariation encoding can also be viewed as an instance of the program transformation technique referred to as deletion of clauses with a finitely failed body ( Pettorossi and Proietti 1994 ) . the clause .
Due to the Zipfian nature of word frequencies , around half the word types in a corpus occur only once , so tremendous increases in corpus size are required both to ensure inclusion of essential word and phrase types and to increase the chances of multiple occurrences of a given type . In corpus linguistics building such megacorpora is beyond the scope of individual researchers , and they are not easily accessible ( Kennedy , 1998 : 56 ) unless the web is used as a corpus ( Kilgarriff and Grefenstette , 2003 ) . Increasingly , corpus researchers are tapping the Web to overcome the sparse data problem ( Keller et al. , 2002 ) .
We optimize the dual objective using the gradient based methods shown in Algorithm 1 . Here 11 is an optimization precision , oc is a step size chosen with the strong Wolfe 's rule ( Nocedal and Wright 1999 ) . Here , PV ( A ) represents an ascent direction chosen as follows : For inequality constraints , it is the projected gradient ( Bertsekas 1999 ) ; for equality constraints with slack , we use conjugate gradient ( Nocedal and Wright 1999 ) , noting that when A = 0 , the objective is not differentiable .
The number and nature of the thematic roles depend on the generic predicates and subpredicates , and not on some general criteria regardless of each predicate ( Fillmore , 1968 ) . See ( Gomez , 1998 ) for a discussion .
MIMIC utilizes a non-recursive frame-based semantic representation commonly used in spoken dialogue systems ( e.g. ( Seneff et al. , 1991 ; Lamel , 1998 ) ) , which represents an utterance as a set of attribute-value pairs . Figure 2 ( a ) shows the frame-based semantic representation for the utterance `` What time is Analyze This playing 2 See ( Nakatani and Chu-Carroll , 2000 ) for how MIMIC 's dialoguelevel knowledge is used to override default prosodic assignments for concept-to-speech generation . in Montclair ? ''
Results for HOTCoref are slightly different from the results reported in Bj ¨ orkelund and Kuhn ( 2014 ) . For Berkeley system , we use the reported results from Durrett and Klein ( 2014 ) . shows significant improvement on all metrics for both datasets .
Usually , in computational linguistics literature , a named entity is an instance of a location , a person , or an organization , and the NER task consists of identifying each of these occurrences . Instead , we will adopt the nomenclature of the Automatic Content Extraction program ( NIST , 2004 ) : we will call the instances of textual references to objects/abstractions mentions , which can be either named ( e.g. John Mayor ) , nominal ( the president ) or pronominal ( she , it ) . An entity is the aggregate of all the mentions ( of any level ) which refer to one conceptual entity .
Thus , our exploratory experiments in applying content models trained with structured RCTs on unstructured RCTs is a closer approximation of an extrinsically-valid measure of performance . Such a component would serve as the first stage of a clinical question answering system ( Demner-Fushman and Lin , 2005 ) or summarization system ( McKeown et al. , 2003 ) . We chose to focus on randomized controlled trials because they represent the standard benchmark by which all other clinical studies are measured .
In particular , representativeness and replicability are key research concerns to enhance the reliability of web data for corpora . In the areas of Natural Language Processing ( NLP ) and computational linguistics , proposals have been made for using the computational Grid for data-intensive NLP and text-mining for eScience ( Carroll et al. , 2005 ; Hughes et al , 2004 ) . While such an approach promises much in terms of emerging infrastructure , we wish to exploit existing computing infrastructure that is more accessible to linguists via a P2P approach .
Michiels ( 1982 ) and Akkerman et al. ( 1985 ) provide a more detailed analysis of the information encoded by the LDOCE grammar codes and discuss their efficacy as a system of linguistic description . Ingria ( 1984 ) comprehensively compares different approaches to complementation within grammatical theory providing a touchstone against which the LDOCE scheme can be evaluated . Most automated parsing systems employ grammars which carefully distinguish syntactic and semantic information , therefore , if the information provided by the Longman grammar code system is to be of use , we need to be able to separate out this information and map it into a representation scheme compatible with the type of lexicon used by such parsing systems .
So the ARGX sub task is improved . To prove that our method is effective , we also make a comparison between the performances of our system and Xue and Palmer ( 2005 ) , Xue ( 2008 ) . Xue ( 2008 ) is the best SRL system until now and it has the same data setting with ours .
Blunsom et al. ( 2008 , 2009 , 2010 ) utilized Bayesian methods to learn synchronous context free grammars ( SCFG ) from a parallel corpus . The obtained SCFG is further used in a phrase-based and hierarchical phrase-based system ( Chiang , 2007 ) . Levenberg et al. ( 2012 ) employed a Bayesian method to learn discontinuous SCFG rules .
The information common to all solutions to the interaction call is lifted up into the lexical entry and becomes available upon lexical lookup . 32 In certain cases an extension of the constraint language with named disjunctions or contexted constraints ( Maxwell and Kaplan 1989 ; Eisele and Dorre 1990 ; Griffith 1996 ) can be used to circumvent constraint propagation . Encoding the disjunctive possibilities for lexical rule application in this way , instead of with definite clause attachments , makes all relevant lexical information available at lexical lookup .
This work differs from the above work in that we design a novel Bayesian model to induce unsupervised U-trees , and prior knowledge can be encoded into the model more freely and effectively . Blunsom et al. ( 2008 , 2009 , 2010 ) utilized Bayesian methods to learn synchronous context free grammars ( SCFG ) from a parallel corpus . The obtained SCFG is further used in a phrase-based and hierarchical phrase-based system ( Chiang , 2007 ) .
In addition to the model based upon a dictionary of stems and words , we also experimented with models based upon character n-grams , similar to those used for Chinese segmentation ( Sproat et al. , 1996 ) . For these models , both arabic characters and spaces , and the inserted prefix and suffix markers appear on the arcs of the finite state machine .