hrithikpiyush/acl-arc huggingface dataset:
For example , consider a relational description ( cfXXX , Dale and Haddock 1991 ) involving a gradable adjective , as in the dog in the large shed . CD for this type of descriptions along the lines of Section 4 is not difficult once relational descriptions are integrated with a standard GRE algorithm ( Krahmer and Theune 2002 , Section 8.6.2 ) : Suppose an initial description is generated describing the set of all those dogs that are in sheds over a given size ( say , size 5 ) ; if this description happens to distinguish an individual dog then this legitimizes the use of the noun phrase the dog in the large shed . Note that this is felicitous even if the shed is not the largest one in the domain , as is true for d2 in the following situation ( contains-a = b means that a is contained by b ) :
it will function as the logical subject of the predicate complement . Michiels proposed rules for doing this for infinitive complement codes ; however there seems to be no principled reason not to extend this approach to computing the underlying relations in other types of VP as well as in cases of NP , AP and PP predication ( see Williams ( 1980 ) , for further discussion ) . The five rules which are applied to the grammar codes associated with a verb sense are ordered in a way which reflects the filtering of the verb sense through a series of syntactic tests .
The main source of Novelle are wikis and blogs . While wikis have spread from a detailed design ( Cunningham and Leuf , 2001 ) , unfortunately blogs have not been designed under a model . So we have tested and compared the most used tools available for blogging : Bloggers , WordPress , MovableType and LiveJournal .
To our knowledge , this study is the first to include concept pairs and to automatically generate the test dataset . In our experiment , we annotated a high number of pairs similar in size to the test sets by Finkelstein ( 2002 ) and Gurevych ( 2006 ) . We used the revised experimental setup ( Gurevych , 2005 ) , based on discrete relatedness scores and presentation of word pairs in isolation , that is scalable to the higher number of pairs .
The logical notation of Montague ( 1970 ) is more sophisticated , and may be considered another possibility . Jackendoff 's ( 1983 ) formalism is richer and resembles more closely an English grammar .
Atallah et al. ( 2002 ) used semantic transformations and embed information in textmeaning representation ( TMR ) trees of the text by either pruning , grafting or substituting the tree structure with information available from ontological semantic resources . Vybornova and Macq ( 2007 ) aimed to embed information by exploiting the linguistic phenomenon of presupposition , with the idea that some presuppositional information can be removed without changing the meaning of a sentence .
As emerges from the ablation tests reported in ( Bentivogli et al. , 2010 ) , even the most common resources proved to have a positive impact on some systems and a negative impact on others . Some previous works ( Bannard and Callison-Burch , 2005 ; Zhao et al. , 2009 ; Kouylekov et al. , 2009 ) indicate , as main limitations of the mentioned resources , their limited coverage , their low precision , and the fact that they are mostly suitable to capture relations mainly between single words . Addressing CLTE we have to face additional and more problematic issues related to : i ) the stronger need of lexical knowledge , and ii ) the limited availability of multilingual lexical resources .
Recently , there is a growing consensus that an evaluation methodology based on the notion of `` information nuggets '' may provide an appropriate framework for assessing the quality of answers to complex questions . Nugget F-score has been employed as a metric in the TREC question-answering track since 2003 , to evaluate so-called definition and `` other '' questions ( Voorhees 2003 ) . A number of studies ( e.g. , Hildebrandt , Katz , and Lin 2004 ) have pointed out shortcomings of the original nugget scoring model , although a number of these issues have been recently addressed ( Lin and Demner-Fushman 2005a , 2006b ) .
Our novel composite kernels , which account for the two syntactic structures , are experimented with the appropriate convolution kernels and show significant improvement with respect to the state-ofthe-art in RE . Regarding future work , there are many research line that may be followed : i ) Capturing more features by employing external knowledge such as ontological , lexical resource or WordNet-based features ( Basili et al. , 2005a ; Basili et al. , 2005b ; Bloehdorn et al. , 2006 ; Bloehdorn and Moschitti , 2007 ) or shallow semantic trees , ( Giuglea and Moschitti , 2004 ; Giuglea and Moschitti , 2006 ; Moschitti and Bejan , 2004 ; Moschitti et al. , 2007 ; Moschitti , 2008 ; Moschitti et al. , 2008 ) . ii ) Design a new tree-based structures , which combines the information of both constituent and dependency parses .
For an overview of systems designed to answer open-domain factoid questions , the TREC QA track overview papers are a good place to start ( Voorhees and Tice 1999 ) . In addition , there has been much work on the application of linguistic and semantic knowledge to information retrieval ; see Lin and Demner-Fushman ( 2006a ) for a brief overview . The idea that clinical information systems should be sensitive to the practice of evidence-based medicine is not new .
The usual statistic encountered in text books is the Pearson chisquared statistic , denoted X2 . However , Dunning ( 1993 ) claims that the log-likelihood chisquared statistic ( G2 ) is more appropriate for corpus-based NLP . In Section 6 , we compare the two statistics in a task-based evaluation .
After identification of word and sentence boundaries the text is processed into a bi-text by an alignment program . This alignment is done on the basis of both length ( Gale and Church [ 7 ] ) and a notion of cognateness ( Simard [ 16 ] ) . 2 .
These tag sets are hybrids in the sense that they are neither simply the core POS , nor the complete morphologically enriched tag set , but instead they selectively enrich the core POS tag set with only certain morphological features . A more detailed discussion of the various available Arabic tag sets can be found in Habash ( 2010 ) . The following are the various tag sets we use in this article : ( a ) the core POS tag sets CORE44 and the newly introduced CORE12 ; ( b ) CATiB Treebank tag set ( CATIB6 ) ( Habash and Roth 2009 ) and its newly introduced extension of CATIBEX created using simple regular expressions on word form , indicating particular morphemes such as the prefix JI Al + or the suffix v ' + wn ; this tag set is the best-performing tag set for Arabic on predicted values as reported in Section 4 ; ( c ) the PATB full tag set with complete morphological tag ( BW ) ( Buckwalter 2004 ) ; and two extensions of the PATB reduced tag set ( PENN POS , a.k.a. RTS , size 24 [ Diab , Hacioglu , and Jurafsky 2004 ] ) , both outperforming it : ( d ) Kulick , Gabbard , and Marcus ( 2006 ) 's tag set ( KULICK ) , size 43 , one of whose most important extensions is the marking of the definite article clitic , and ( e ) Diab and Benajiba 's ( in preparation ) EXTENDED RTS tag set ( ERTS ) , which marks gender , number , and definiteness , size 134 .
We do not optimize these hyperparameters or vary them over time . The high Dirichlet priors are chosen to prevent sparsity in topic distributions , while the other parameters are selected as the best from Hoffman et al. ( 2010 ) . In order to optimize the number of topics K , we run five trials of each modality for 2000 iterations for K = 150 , 100 , 150 , 200 , 2501 ( a total of 25 runs per setup ) .
The expectation system provided the intermediate processing between the errorful output of the speech recognizer and the deep semantics of NLC . The resulting speech understanding system is called the Voice Natural Language Computer with Expectation ( VNLCE , Fink 1983 ) . [ The current system should be distinguished from an earlier voice system ( VNLC , Biermann et al. 1985 ) , which had no expectation and which handled discrete speech where a 300 millisecond pause must follow each word . ]
Second , although we use the term chart , the structure for accepted hypotheses is not a dynamic programming chart in the same way as for the parsing problem . In our previous papers ( Zhang and Clark 2011 ; Zhang , Blackwood , and Clark 2012 ) , we applied a set of beams to this structure , which makes it similar to the data structure used for phrase-based MT decoding ( Koehn 2010 ) . However , we will show later that this structure is unnecessary when the model has more discriminative power , and a conceptually simpler single beam can be used .
Aligning English-Chinese parallel texts is already very difficult because of the great differences in the syntactic structures and writing systems of the two languages . A number of alignment techniques have been proposed , varying from statistical methods ( Brown et al. , 1991 ; Gale and Church , 1991 ) to lexical methods ( Kay and Roscheisen , 1993 ; Chen , 1993 ) . The method we adopted is that of Simard et al. ( 1992 ) .
Most prominent among these are the rich grammatical subcategorisations of the 60,000 entries , the large amount of information concerning phrasal verbs , noun compounds and idioms , the individual subject , collocational and semantic codes for the entries and the consistent use of a controlled ` core ' vocabulary in defining the words throughout the dictionary . ( Michiels ( 1982 ) contains further description and discussion of LDOCE . ) In this paper we focus on the exploitation of the LDOCE grammar coding system ; Alshawi et al. ( 1985 ) and Alshawi ( 1987 ) describe further research in Cambridge utilising different types of information available in LDOCE .
That is , using the marker hypothesis method of segmentation , smaller aligned segments can be extracted from the phrasal lexicon without recourse to any detailed parsing techniques or complex co-ocurrence measures . Juola ( 1994 , 1997 ) assumes that words ending in - ed are verbs . However , given that verbs are not a closed class , in our approach we do not mark chunks beginning with a verb with any marker category .
We want to model views as dynamic objects the creation of context will be still arbitrary , but changes are very easily . We would also provide a user facility for choosing the right licence for every lexia , following the model of Creative Commons licences ( Lessig , 2004 ) .
That is , we want to allocate a larger portion of the probability space to the phrasal and marker lexicons than to the generalized or wordlevel lexicons . We have yet to import such a constraint into our model , but we plan to do so in the near future using the weighted majority algorithm ( Littlestone and Warmuth 1992 ) .
So we have tested and compared the most used tools available for blogging : Bloggers , WordPress , MovableType and LiveJournal . Generally speaking , we find that the personal public diary metaphor behind blogs ( McNeill , 2005 ) may bring to an unsatisfactory representation of the context . The only way to retrieve information is through a search engine or a calendar , i.e. the date of the ` post ' -- a lexia in the jargon of bloggers .
Most probabilistic translation model reestimation algorithms published to date are variations on the theme proposed by Brown et al. ( 1993b ) . These models involve conditional probabilities , but they can be compared to symmetric models if the latter are normalized by the appropriate marginal distribution .
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 . The significance testing is performed by paired bootstrap re-sampling ( Koehn , 2004 ) . We use an in-house developed hierarchical phrase-based translation ( Chiang , 2005 ) as our baseline system , and we denote it as In-Hiero .
The ranks are determined by the similarity to the closest instances and the highest ranked sentence is chosen as the final generation output . IGEN uses standard chart generation techniques ( Kay , 1996 ) in its base generator to efficiently produce generation candidates . The set of candidates forms a subset of the chart edges in that they comprise all edges of syntactic category S .
Head word POS , Head Word of Prepositional Phrases , Constituent tree distance , from Pradhan etc. ( 2004 ) . Position , subcat frame , phrase type , first word , last word , subcat frame + , predicate , path , head word and its POS , predicate + head word , predicate + phrase type , path to BA and BEI , verb class 3 , verb class + head word , verb class + phrase type , from Xue ( 2008 ) . 3 It is a bit different from Xue ( 2008 ) , since we did n't use the syntactic alternation information .
The psycholinguistic studies of Martin ( 1970 ) , Allen ( 1975 ) , Hillinger et al. ( 1976 ) , Grosjean et al. ( 1979 ) , Dommergues and Grosjean ( 1983 ) , and Gee and Grosjean ( 1983 ) , responding to the idea of readjusted syntax as the source of prosodic phrasing , show that grammatical structure , even if readjusted , is not in itself a reliable predictor of prosodic phrasing : mismatches between syntax and prosody occur often and systematically , and can be related to specific nonsyntactic factors such as length and word frequency . For example , although prosodic boundaries between subject and verb do occur , there also exist prosodic patterns in which the boundary comes between the verb and object , i.e. , the data reveal both X ( VY ) and ( XV ) Y groupings .
Nugget F-score has been employed as a metric in the TREC question-answering track since 2003 , to evaluate so-called definition and `` other '' questions ( Voorhees 2003 ) . A number of studies ( e.g. , Hildebrandt , Katz , and Lin 2004 ) have pointed out shortcomings of the original nugget scoring model , although a number of these issues have been recently addressed ( Lin and Demner-Fushman 2005a , 2006b ) . However , adaptation of the nugget evaluation methodology to a domain as specific as clinical medicine is an endeavor that has yet to be undertaken .
The model was also used to induce a translation lexicon from a 6200-word corpus of French/English weather reports . Nasr ( 1997 ) reported that the translation lexicon that our model induced from this tiny bitext accounted for 30 % of the word types with precision between 84 % and 90 % . Recall drops when there is less training data , because the model refuses to make predictions that it can not make with confidence .
Approaches using treebank-based data as a source for subcategorization information , such as ours , do not predefine the frames to be extracted but rather learn them from the data . Kinyon and Prolo ( 2002 ) describe a simple tool which uses fine-grained rules to identify the arguments of verb occurrences in the Penn-II Treebank . This is made possible by manual examination of more than 150 different sequences of syntactic and functional tags in the treebank .
The parser provides a domain-independent semantic representation including high-level word senses and semantic role labels . The contextual interpreter then uses a reference resolution approach similar to Byron ( 2002 ) , and an ontology mapping mechanism ( Dzikovska et al. , 2008a ) to produce a domain-specific semantic representation of the student 's output . Utterance content is represented as a set of extracted objects and relations between them .
Notable early papers on graph-based semisupervised learning include Blum and Chawla ( 2001 ) , Bansal et al. ( 2002 ) , Kondor and Lafferty ( 2002 ) , and Joachims ( 2003 ) . Zhu ( 2005 ) maintains a survey of this area . Recently , several alternative , often quite sophisticated approaches to collective classification have been proposed ( Neville and Jensen , 2000 ; Lafferty et al. , 2001 ; Getoor et al. , 2002 ; Taskar et al. , 2002 ; Taskar et al. , 2003 ; Taskar et al. , 2004 ; McCallum and Wellner , 2004 ) .
We use a maximum entropy Markov model ( MEMM ) classifier . The principle of maximum entropy states that when one searches among probability distributions that model the observed data ( evidence ) , the preferred one is the one that maximizes the entropy ( a measure of the uncertainty of the model ) ( Berger et al. , 1996 ) . One big advantage of this approach is that it can combine arbitrary and diverse types of information in making a classification decision .
The idea of introducing constraints over a model to better guide the learning process has appeared before . In the context of word alignment , Deng and Byrne ( 2005 ) use a state-duration HMM in order to model word-to-phrase translations . The fertility of each source word is implicitly encoded in the durations of the HMM states .
These related models treat their lexical items as latent variables to be estimated from training data , while our model uses a fixed set of lexical items that correspond to the class of function words . With respect to the focus on function words , our reordering model is closely related to the UALIGN system ( Hermjakob , 2009 ) . However , UALIGN uses deep syntactic analysis and hand-crafted heuristics in its model .
For projective parsing , it is significantly faster than exact dynamic programming , at the cost of small amounts of search error , We are interested in extending these ideas to phrase-structure and lattice parsing , and in trying other higher-order features , such as those used in parse reranking ( Charniak and Johnson , 2005 ; Huang , 2008 ) and history-based parsing ( Nivre and McDonald , 2008 ) . We could also introduce new variables , e.g. , nonterminal refinements ( Matsuzaki et al. , 2005 ) , or secondary links Mid ( not constrained by TREE/PTREE ) that augment the parse with representations of control , binding , etc. ( Sleator and Temperley , 1993 ; Buch-Kromann , 2006 ) . Other parsing-like problems that could be attacked with BP appear in syntax-based machine translation .
Similarity-smoothed models are simple to compute , potentially adaptable to new domains , and require no manually-compiled resources such as WordNet . Selectional Preferences have also been a recent focus of researchers investigating the learning of paraphrases and inference rules ( Pantel et al. , 2007 ; Roberto et al. , 2007 ) . Inferences such as `` [ X wins Y ] ⇒ [ X plays Y ] '' are only valid for certain arguments X and Y .
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 ) . It has also been shown to be useful in joint inference of text with visual attributes obtained using visual classifiers ( Silberer et al. , 2013 ) .
The planner could supply whatever information is needed to drive the network . ' Something like this approach is in fact used in some systems ( e.g. , Elhadad and Robin 1992 ; PenMan 1989 ; Hovy 1988a ) . The problem with the discrimination network approach is that it ca n't handle the range of examples shown here without violating the modularity of the generator .
We believe that this name is clearly understable to every people educated in a European-based culture , and this is why we have chosen it . The emphasis on narrativity takes into account the use of blogs as public diaries on the web , that is still the main current interpretation of this literary genre , or metagenre ( McNeill , 2005 ) . Furthermore we noticed that blogs and wikis are currently subjected to osmosis , because they have in common the underlying core technology .
Beside HTML markups , other criteria may also be incorporated . For example , it would be helpful to consider strong correspondence between certain English and Chinese words , as in ( Wu , 1994 ) . We hope to implement such correspondences in our future research .
However , they use MSA at the sentence level rather than at the discourse level . We take some core ideas from our previous work on mining script information ( Regneri et al. , 2010 ) . In this earlier work , we focused on event structures and their possible realizations in natural language .
In other words , when prosodic features that reflect facts of the discourse are removed , is there a residual , neutral phrasing ? Our work on the prosodic phrase status of clause final prepositional phrases , which we discuss below , suggests the existence of a discourse-neutral phrasing that depends on syntactic constituency mediated by string adjacency and length of a potential prosodic phrase .3 Such phrasing provides us with a typical phrasing pattern analogous to the typical phrasal stress patterns examined in Liberman and Prince ( 1977 ) , which `` are often overwhelmed by the chiaroscuro of highlight and background in discourse , but retain the status of null-hypothesis patterns that emerge when Computational Linguistics Volume 16 , Number 3 , September 1990 157 J. Bachenko and E. Fitzpatrick Discourse-Neutral Prosodic Phrasing in English there is no good reason to take some other option '' ( p. 251 ) . This approach to prosodic phrase boundary determination brings us closer to a framework in which phonological , syntactic , and discourse features all contribute to prosodic phrasing .
9.3.1 Combinations of Adjectives . When objects are compared in terms of several dimensions , these dimensions can be weighed in different ways ( e.g. , Rasmusen 1989 ) . Let us focus on references to an individual referent r , starting with a description that contains more than one gradable adjective .
This paper describes an approach for sharing resources in various grammar formalisms such as Feature-Based Lexicalized Tree Adjoining Grammar ( FB-LTAG1 ) ( Vijay-Shanker , 1987 ; Vijay-Shanker and Joshi , 1988 ) and Head-Driven Phrase Structure Grammar ( HPSG ) ( Pollard and Sag , 1994 ) by a method of grammar conversion . The RenTAL system automatically converts an FB-LTAG grammar into a strongly equivalent HPSG-style grammar ( Yoshinaga and Miyao , 2001 ) . Strong equivalence means that both grammars generate exactly equivalent parse results , and that we can share the LTAG grammars and lexicons in HPSG applications .
Note that for lexicalized tree , features do not include the information of sub-trees since there is no nonterminals . The features can be easily obtained by modifying the TAT extraction algorithm described in ( Liu et al. , 2006 ) . When a TAT is extracted from a word-aligned , source-parsed parallel sentence , we just record the contextual features and the features of the sub-trees .
The system learns 137 subcategorization frames from 19,126 sentences for 914 verbs ( those which occurred five times or more ) . Marinov and Hemming ( 2004 ) present preliminary work on the automatic extraction of subcategorization frames for Bulgarian from the BulTreeBank ( Simov , Popova , and Osenova 2002 ) . In a similar way to that of Sarkar and Zeman ( 2000 ) , Marinov and Hemming 's system collects both arguments and adjuncts .
We simply give a short account of its basic principles herein . ASARES is based on a Machine Learning technique , Inductive Logic Programming ( ILP ) ( Muggleton and De-Raedt , 1994 ) , which infers general morpho-syntactic patterns from a set of examples ( this set is noted E + hereafter ) and counter-examples ( E − ) of the elements one 2CORDIAL is a commercial product of SynapseDeveloppement .
Turney ( 2001 ) extracts word co-occurrence probabilities from unlabelled text collected from a web crawler . 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 ) .