hrithikpiyush/acl-arc huggingface dataset:
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 ) . VAGUE uses both of these devices .
In the work presented here , we restrict ourselves to transforming only one language of the two : the source , which has the less inflected morphology . For descriptions of SMT systems see for example ( Germann et al. , 2001 ; Och et al. , 1999 ; Tillmann and Ney , 2002 ; Vogel et al. , 2000 ; Wang and Waibel , 1997 ) .
( Graz and Meurers , 1997b ) describe a method for compiling implicational constraints into typed feature grammars and interleaving them with relational constraints .4 Because of space limitations we have to refrain from an example . The ConTroll grammar development system as described in ( Gotz and Meurers , 1997b ) implements the above mentioned techniques for compiling an HPSG theory into typed feature grammars .
Removing redundant sentences . After calculating the raw score of each sentence , we use a modified version of the Adaptive Greedy Algorithm by Filatova and Hatzivassiloglou ( 2004 ) to penalize redundant sentences in cohesive clusters . This is done by decrementing the score of a sentence that belongs to an SC for which there is a higher or equal scoring sentence ( if there are several highest-scoring sentences , we retain one sentence as a reference sentence -- i.e. , its score is not decremented ) .
in bitext space is another kind of collocation . If each word 's translation is treated as a sense tag ( Resnik and Yarowsky 1997 ) , then `` translational '' collocations have the unique property that the collocate and the word sense are one and the same ! Method B exploits this property under the hypothesis that `` one sense per collocation '' holds for translational collocations .
The input to Snob is a set of binary vectors , one vector per response document . The values of a vector correspond to the presence or absence of each ( lemmatized ) corpus word in the document in question ( after removing stop-words and words with very low frequency ) .4 The predictive model is a Decision Graph ( Oliver 1993 ) , which , like Snob , is based on the MML principle . The Decision Graph is trained on unigram and bigram lemmas in the request as input features ,5 and the identifier of the response cluster that contains the actual response for the request as the target feature .
Training a model which has such a huge number of classes is infeasible if the model does not factorize due to strong independence assumptions . Therefore , in order to be able to incorporate long-range dependencies in our models , we chose to adopt a re-ranking approach ( Collins 2000 ) , which selects from likely assignments generated by a model which makes stronger independence assumptions . We utilize the top n assignments of our local semantic role labeling model PSRL to generate likely assignments .
We investigate here only the `` grounding '' in logical theories . However , it is possible to think about constraining linguistic or logical predicates by simulating physical experiences ( cfXXX Woods 1987 ) . We assume here that a translation of the surface forms of sentences into a logical formalism is possible .
We currently have two application domains that can carry on a spoken dialog with a user . One , the VOYAGER domain ( Zue et al. 1990 ) , answers questions about places of interest in an urban area , in our case , the vicinity of MIT and Harvard University . The second one , ATIS ( Seneff et al. 1991 ) , is a system for accessing data in the Official
Although this study falls under the general topic of discourse modeling , our work differs from previous attempts to characterize text in terms of domainindependent rhetorical elements ( McKeown , 1985 ; Marcu and Echihabi , 2002 ) . Our task is closer to the work of Teufel and Moens ( 2000 ) , who looked at the problem of intellectual attribution in scientific texts .
In the seminal work by Rubenstein and Goodenough ( 1965 ) , similarity judgments were obtained from 51 test subjects on 65 noun pairs written on paper cards . Test subjects were instructed to order the cards according to the `` similarity of meaning '' and then assign a continuous similarity value ( 0.0 4.0 ) to each card .
Jackendoff 's ( 1983 ) formalism is richer and resembles more closely an English grammar . Jackendoff ( 1983 , p. 14 ) writes `` it would be perverse not to take as a working assumption that language is a relatively efficient and accurate encoding of the information it conveys . '' Therefore a formalism of the kind he advocates would probably be most suitable for an implementation of our semantics .
Whether or not a goal is sufficiently instantiated is determined on the basis of so-called delay patterns .13 These are specifications provided by the user that 12Coroutining appears under many different guises , like for example , suspension , residuation , ( goal ) freezing , and blocking . See also ( Colmerauer , 1982 ; Naish , 1986 ) . `` In the literature delay patterns are sometimes also referred to as wait declarations or block statements .
We parsed the 3 GB AQUAINT corpus ( Voorhees , 2002 ) using Minipar ( Lin , 1998b ) , and collected verb-object and verb-subject frequencies , building an empirical MI model from this data . Verbs and nouns were converted to their ( possibly multi-token ) root , and string case was preserved .
We use support vector machines3 to predict the next parser action from a feature vector representing the history . More specifically , we use LIBSVM ( Chang and Lin , 2001 ) with a quadratic kernel K ( xZ , xj ) = ( - yxT xj + r ) 2 and the built-in one-versus-all strategy for multi-class classification . Symbolic features are
Then ti is the total semiring weight w0n of paths in Ti from initial state 0 to final state n ( assumed WLOG to be unique and unweighted ) . It is wasteful to compute ti as suggested earlier , by minimizing ( cxxi ) of o ( yixE ) , since then the real work is done by an c-closure step ( Mohri , 2002 ) that implements the all-pairs version of algebraic path , whereas all we need is the single-source version . If n and m are the number of states and edges ,19 then both problems are O ( n3 ) in the worst case , but the single-source version can be solved in essentially O ( m ) time for acyclic graphs and other reducible flow graphs ( Tar an , 1981b ) .
Each instance is represented by 33 lexical , grammatical , semantic , and positional features that have been employed by highwe can see , the baseline achieves an F-measure of performing resolvers such as Ng and Cardie ( 2002 ) 57.0 and a resolution accuracy of 48.4 . and Yang et al. ( 2003 ) , as described below .
He was a grammarian who analysed Sanskrit ( Misra , 1966 ) . The idea resurfaced forcefully at several points in the more recent history of linguistic research ( Tesni`ere , 1959 ; Gruber , 1965 ; Fillmore , 1968 ) . Now it has the attention of many researchers in natural language processing , as shown by recent research in semantic parsing and semantic
A number of stand-alone tools have also been developed . For example , the suite of LT tools ( Mikheev et al. , 1999 ; Grover et al. , 2000 ) perform tokenization , tagging and chunking on XML marked-up text directly . These tools also store their configuration state , e.g. the transduction rules used in LT CHUNK , in XML configuration files .
The metric we consider here is derived from an example-based machine translation . To retrieve translation examples for a test sentence , ( Watanabe and Sumita , 2003 ) defined a metric based on the combination of edit distance and TF-IDF ( Manning and Sch ¨ utze , 1999 ) as follows : where 0 ( 0 < 0 < 1 ) is an interpolation weight , fi ( i = 1 , 2 ) is a word sequence and can be also considered as a document .
One advantage that Brown et al. 's Model 1 has over our word-to-word model is that their objective function has no local maxima . By using the EM algorithm ( Dempster et al. , 1977 ) , they can guarantee convergence towards the globally optimum parameter set . In contrast , the dynamic nature of the competitive linking algorithm changes the Pr ( datalmodel ) in a non-monotonic fashion .
Grounded semantics provides a bridge to connect symbolic labels or words with lower level visual features ( Harnad , 1990 ) . Previous work has developed various approaches for grounded semantics mainly for the reference resolution task , i.e. , identifying visual objects in the environment given language descriptions ( Dhande , 2003 ; Gorniak and Roy , 2004 ; Tenbrink and Moratz , 2003 ; Siebert and Schlangen , 2008 ; Liu et al. , 2012 ) . For the referring expression generation task here , we also need a lexicon with grounded semantics .
However , because templates are a static facility we will also provide dynamic versions ( using inheritance ) , which will be slower but accessible from scripting languages and user interfaces . To provide the required configurability in the static version of the code we will use policy templates ( Alexandrescu , 2001 ) , and for the dynamic version we will use configuration classes . A key aspect of increasing the efficiency of the system will be using a common text and annotation representation throughout the infrastructure .
Lee et al. ( 2003 ) demonstrates a technique for segmenting Arabic text and uses it as a morphological processing step in machine translation . A trigram language model was used to score and select among hypothesized segmentations determined by a set of prefix and suffix expansion rules .
A recent study by Katz-Brown et al. ( 2011 ) also investigates the task of training parsers to improve MT reordering . In that work , a parser is used to first parse a set of manually reordered sentences to produce k-best lists .
14 We use rather abstract lexical rules in the examples to be able to focus on the relevant aspects . 15 Hinrichs and Nakazawa ( 1996 ) show that the question of whether the application criterion of lexical rules should be a subsumption or a unification test is an important question deserving of more attention . We here assume unification as the application criterion , which formally corresponds to the conjunction of descriptions and their conversion to normal form ( Glitz 1994 ) .
Latent Dirichlet Allocation ( Blei et al. , 2003 ) , or LDA , is an unsupervised Bayesian probabilistic model of text documents . It assumes that all documents are probabilistically generated from a shared set of K common topics , where each topic is a multinomial distribution over the vocabulary ( notated as Q ) , and documents are modeled as mixtures of these shared topics ( notated as 0 ) .
The Semantic Network provides a consistent categorization of all concepts represented in the UMLS Metathesaurus . 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 .
There has been other work on solving inflection . Koehn and Hoang ( 2007 ) introduced factored SMT . We use more complex context features .
Both tasks are performed with a statistical framework : the mention detection system is similar to the one presented in ( Florian et al. , 2004 ) and the coreference resolution system is similar to the one described in ( Luo et al. , 2004 ) . Both systems are built around from the maximum-entropy technique ( Berger et al. , 1996 ) . We formulate the mention detection task as a sequence classification problem .
We chose nine adjectives according to a set of minimal criteria and paired each adjective with 10 nouns randomly selected from the BNC . We chose the adjectives as follows : we first compiled a list of all the polysemous adjectives mentioned in the lexical semantics literature ( Vendler , 1968 ; Pustejovsky , 1995 ) . From these we randomly sampled nine adjectives ( difficult , easy , fast , good , hard , right , safe , slow , wrong ) .
A key aspect of our case study research will be to investigate extending corpus collection to new document types . Most web-derived corpora have exploited raw text or HTML pages , so efforts have focussed on boilerplate removal and cleanup of these formats with tools like Hyppia-BTE , Tidy and Parcels3 ( Baroni and Sharoff , 2005 ) . Other document formats such as Adobe PDF and MS-Word have been neglected due to the extra conversion and clean-up problems they entail .
Unfortunately , the result may differ for equivalent FSTs that express the same weighted relation . Undesirable consequences of this fact have been termed `` label bias '' ( Lafferty et al. , 2001 ) . Also , in the conditional case such per-state normalization is only correct if all states accept all input suffixes ( since `` dead ends '' leak probability mass ) .8 • A better-founded approach is global normal -
So far , we have only evaluated models trained on gold POS tag set and morphological feature values . Some researchers , however , including Goldberg and Elhadad ( 2010 ) , train on predicted feature values instead . It makes sense that training on predicted features yields better scores for evaluation on predicted features , since the training better resembles the test .
Further , these linguistically important and interesting questions are also highly significant for computational linguistics ( CL ) and natural language processing ( NLP ) applications . Their computational significance arises from the issue of their storage in lexical resources like WordNet ( Fellbaum , 1998 ) and raises the questions like , how to store morphologically complex words , in a lexical resource like WordNet keeping in mind the storage and access efficiency . There is a rich literature on organization and lexical access of morphologically complex words where experiments have been conducted mainly for derivational suffixed words of English , Hebrew , Italian , French , Dutch , and few other languages ( Marslen-Wilson et al. , 2008 ; Frost et al. , 1997 ; Grainger , et al. , 1991 ; Drews and Zwitserlood , 1995 ) .
A disadvantage of this model is that an extremely large number of subtrees ( and derivations ) must be taken into account . Fortunately , there exists a compact PCFG-reduction of DOP1 that generates the same trees with the same probabilities , as shown by Goodman ( 1996 , 2002 ) . Here we will only sketch this PCFG-reduction , which is heavily based on Goodman ( 2002 ) .
Both predicateargument relations and discourse factors have been examined for their possible input to prosodic phrasing . Crystal ( 1969 ) claims that prosodic phrase boundaries will co-occur with grammatical functions such as subject , predicate , modifier , and adjunct . Selkirk ( 1984 ) and Nespor and Vogel ( 1986 ) take a similar approach , but within a different theoretical framework .
We rephrase the method of Grimley-Evans ( 1997 ) as follows : First , we construct the approximating finite automaton according to the unparameterized RTN method above . Then an additional mechanism is introduced that ensures for each rule A -- Xi .
In Turkish , very essential syntactic information is contained in the rich morphological structure , where 6The analysis is reminiscent of the treatment of coordination in the Collins parser ( Collins , 1999 ) . concatenated suffixes carry information that in other languages may be expressed by separate words .
In this paper , the method is applied to a French corpus on computing to find noun-verb combinations in which verbs convey a meaning of realization . The work is carried out in order to assist terminographers in the enrichment of a dictionary on computing that includes collocational information ( L'Homme , 2004 ) . Even though this work is carried out for terminographical and lexicographical purposes , it can certainly be of use in other applications , namely information retrieval .
A number of other systems have addressed part of the task . CornmandTalk ( Moore et al. , 1997 ) , Circuit Fix-It Shop ( Smith , 1997 ) and TRAINS-96 ( Traum and Allen , 1994 ; Traum and Andersen , 1999 ) are spoken language systems but they interface to simulation or help facilities rather than semi-autonomous agents . Jack 's MOOse Lodge ( Badler et al. , 1999 ) takes text rather than speech as natural language input and the avatars being controlled are not semi-autonomous .
The algorithm uses categorial , configurational , local head , and Penn-II functional and trace information . The annotation procedure is dependent on locating the head daughter , for which an amended version of Magerman ( 1994 ) is used . The head is annotated with the LFG equation r = J. .
However , this possibility is not usually given much credence . For instance , Divay and Vitale ( 1997 ) recently wrote : `` To our knowledge , learning algorithms , although promising , have not ( yet ) reached the level of rule sets developed by humans '' ( p. 520 ) . Dutoit ( 1997 ) takes this further , stating `` such training-based strategies are often assumed to exhibit much more intelligence than they do in practice , as revealed by their poor transcription scores '' ( p. 115 , note 14 ) .
So it is questionable as to whether morpheme segmentation techniques are sufficient to solve the inflectional problem we are addressing . Much previous work looks at the impact of using source side information ( i.e. , feature functions on the aligned English ) , such as those of Avramidis and Koehn ( 2008 ) , Yeniterzi and Oflazer ( 2010 ) and others . Toutanova et .
The first feature represents the part of speech of the word . We use an in-house statistical tagger ( based on ( Church , 1988 ) ) to tag the text in which the unknown word occurs . The tag set used is a simplified version of the tags used in the machinereadable version of the Oxford Advanced Learners Dictionary ( OALD ) .
We chose to investigate an alternative research direction by aiming to develop a `` paraphrastic grammar '' that is , a grammar which captures the paraphrastic relations between linguistic structuress . Based on a computational grammar that associates natural language expressions with both a syntactic and a semantic representation , a paraphrastic gram ` As we shall briefly discuss in section 4 , the grammar is developed with the help of a meta-grammar ( Candito , 1999 ) thus ensuring an additional level of abstraction . The metagrammar is an abstract specification of the linguistic properties ( phrase structure , valency , realisation of grammatical functions etc. ) encoded in the grammar basic units .
The process of composing a planned response for a new request is informed by probabilistic and lexical properties of the requests and responses in the corpus . There are very few reported attempts at corpus-based automation of help-desk responses ( Carmel , Shtalhaim , and Soffer 2000 ; Lapalme and Kosseim 2003 ; Bickel and Scheffer 2004 ; Malik , Subramaniam , and Kaushik 2007 ) . eResponder , the system developed by Carmel , Shtalhaim , and Soffer ( 2000 ) , retrieves a list of request -- response pairs and presents a ranked list of responses to the user .
In this section we will discuss ( i ) the state of the art in Web People Search in general , focusing on which features are used to solve the problem ; and ( ii ) lessons learnt from the WePS evaluation campaign where most approaches to the problem have been tested and compared . The disambiguation of person names in Web results is usually compared to two other Natural Language Processing tasks : Word Sense Disambiguation ( WSD ) ( Agirre and Edmonds , 2006 ) and Cross-document Coreference ( CDC ) ( Bagga and Baldwin , 1998 ) . Most of early research work on person name ambiguity focuses on the CDC problem or uses methods found in the WSD literature .
Quirk et al. ( 2007 ) extract fragments using a generative model of noisy translations . Our own work ( Wang and Callison-Burch , 2011 ) extends the first idea to paraphrase fragment extraction on monolingual parallel and comparable corpora . Our current approach also uses word-word alignment , however , we use syntactic dependency trees to compute grammatical fragments .
For example , while it is difficult to induce a grammar with raw text alone , the task is tractable when the syntactic analysis for each sentence is provided as a part of the training data ( Pereira and Schabes 1992 ) . Current state-of-the-art statistical parsers ( Collins 1999 ; Charniak 2000 ) are all trained on large annotated corpora such as the Penn Treebank ( Marcus , Santorini , and Marcinkiewicz 1993 ) . However , supervised training data are difficult to obtain ; existing corpora might not contain the relevant type of annotation , and the data might not be in the domain of interest .
• LEFT-ARC ( r ) : Add an arc labeled r from next to top ; pop the stack . Although the parser only derives projective graphs , the fact that graphs are labeled allows non-projective dependencies to be captured using the pseudoprojective approach of Nivre and Nilsson ( 2005 ) . Another limitation of the parsing algorithm is that it does not assign dependency labels to roots , i.e. , to tokens having HEAD = 0 .
Furthermore , an abbreviation itself can be the last token in a sentence in which case its period acts at the same time as part of this abbreviation and as the end-of-sentence indicator ( fullstop ) . A detailed introduction to the SBD problem can be found in Palmer and Hearst ( 1997 ) . The disambiguation of capitalized words and sentence boundaries presents a chicken-and-egg problem .
It also becomes more difficult to experiment with different tutorial policies within the system due to the inherent completixites in applying tutoring strategies consistently across a large number of individual hand-authored remediations . The BEETLE II system architecture is designed to overcome these limitations ( Callaway et al. , 2007 ) . It uses a deep parser and generator , together with a domain reasoner and a diagnoser , to produce detailed analyses of student utterances and generate feedback automatically .
tion 7 . For the evaluation of the results we use the BLEU score ( Papineni et al. , 2001 ) . Section 8 compares translations generated from automatically built and manually annotated tectogrammatical representations .
This enables us to translate the verb alone even if it occurs in the training corpus only as a spliced entry . For an introduction to maximum entropy modeling and training procedures , the reader is referred to the corresponding literature , for instance ( Berger et al. , 1996 ) or ( Ratnaparkhi , 1997 ) .
Our system begins operation with a minimum of manually encoded knowledge , and accumulates information as it processes the text . This design idea was adopted from TANKA ( Barker et al. , 1997b ) . The only manually encoded knowledge is a dictionary of markers ( subordinators , coordinators , prepositions ) .
highlighting of the corpus-specific structures conveying the target element . In addition to its explanatory capacity , this symbolic acquisition technique has obtained good results for other acquisition tasks when compared to existing statistical techniques ( Bouillon et al. , 2002 ) .
We use features such as the shallow parsing information associated with the tokens in a window of 3 tokens , POS , etc. . The context of a current token ti is clearly one of the most important features in predicting whether ti is a mention or not ( Florian et al. , 2004 ) . We denote these features as backward token tri-grams and forward token tri-grams for the previous and next context of ti respectively .