hrithikpiyush/acl-arc huggingface dataset:
linguistic in nature , rather than dealing with superficial properties of the text , e.g. the amount of white space between words ( Por et al. , 2008 ) . Our proposed method is based on the automatically acquired paraphrase dictionary described in Callison-Burch ( 2008 ) , in which the application of paraphrases from the dictionary encodes secret bits . One advantage of the dictionary is that it has wide coverage , being automatically extracted ; however , a disadvantage is that it contains many paraphrases which are either inappropriate , or only appropriate in certain contexts .
More accurate models can be induced by taking into account various features of the linked tokens . For example , frequent words are translated less consistently than rare words ( Melamed , 1997 ) . To account for this difference , we can estimate separate values of A + and Afor different ranges of n ( „ , v ) .
The knowledge sources we use include parts-of-speech , local collocations , and surrounding words . These knowledge sources were effectively used to build a state-of-the-art WSD program in one of our prior work ( Lee and Ng , 2002 ) . In performing WSD with a naive Bayes classifier , the sense s assigned to an example with features f1 , ... , fn is chosen so as to maximize : In our domain adaptation study , we start with a WSD system built using training examples drawn from BC .
We employ the same procedure as our previous work for setting the terminal production prior dis - category relationships from the weak supervision : the tag dictionary and raw corpus ( Garrette and Baldridge , 2012 ; Garrette et al. , 2015 ) .4 This procedure attempts to automatically estimate the frequency of each word/tag combination by dividing the number of raw-corpus occurrences of each word in the dictionary evenly across all of its associated tags . These counts are then combined with estimates of the `` openness '' of each tag in order to assess its likelihood of appearing with new words .
As Kinyon and Prolo ( 2002 ) does not include an evaluation , currently it is impossible to say how effective their technique is . Sarkar and Zeman ( 2000 ) present an approach to learn previously unknown frames for Czech from the Prague Dependency Bank ( Hajic 1998 ) .
In this section we summarize these experiments and their results . A companion paper describes the evaluation process and results in further detail ( Chu-Carroll and Nickerson , 2000 ) . Each experiment involved eight users interacting with MIMIC and MIMIC-SI or MIMIC-MI to perform a set of tasks , each requiring the user to obtain specific movie information .
The problem one faces from an IR point of view is that besides fairly standardized nominal compounds , which already form a regular part of the sublanguage proper , a myriad of ad hoc compounds are formed on the fly which can not be anticipated when formulating a retrieval query though they appear in relevant documents . Hence , enumerating morphological variants in a semi-automatically generated lexicon , such as proposed for French ( Zweigenbaum et al. , 2001 ) , turns out to be infeasible , at least for German and related languages . Furthermore , medical terminology is characterized by a typical mix of Latin and Greek roots with the corresponding host language ( e.g. , German ) , often referred to as neo-classical compounding ( McCray et al. , 1988 ) .
We find that prosodic features improve the classification of dialogue acts and that head gestures , where they occur , contribute to the semantic interpretation of feedback expressions . The results , which partly confirm those obtained on a smaller dataset in Paggio and Navarretta ( 2010 ) , must be seen in light of the fact that our gesture annotation scheme comprises more fine-grained categories than most of the studies mentioned earlier for both head movements and face expressions . The classification results improve , however , if similar categories such as head nods and jerks are collapsed into a more general category .
Unlike the global method which performs tuning on the whole development set Dev + Di as in Algorithm 1 , Wi can be incrementally learned by optimizing on Di based on Wb . We employ the idea of ultraconservative update ( Crammer and Singer , 2003 ; Crammer et al. , 2006 ) to propose two incremental methods for local training in Algorithm 2 as follows . Ultraconservative update is an efficient way to consider the trade-off between the progress made on development set Dev and the progress made on Di .
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 ) . These multimodal LDA models ( hereafter , mLDA ) have been shown to be qualitatively sensible and highly predictive of several psycholinguistic tasks ( Andrews et al. , 2009 ) .
Finkelstein et al. ( 2002 ) did not report inter-subject correlation for their larger dataset . Gurevych ( 2006 ) reported a correlation of r = .69 . Test subjects were trained students of computational linguistics , and word pairs were selected analytically .
Arbitrary weights such as 2.7 may be assigned to arcs or sprinkled through a regexp ( to be compiled into E : E/2 .7 − ) arcs ) . A more subtle example is weighted FSAs that approximate PCFGs ( Nederhof , 2000 ; Mohri and Nederhof , 2001 ) , or to extend the idea , weighted FSTs that approximate joint or conditional synchronous PCFGs built for translation . These are parameterized by the PCFG 's parameters , but add or remove strings of the PCFG to leave an improper probability distribution .
for building spoken dialogue systems that integrate speech recognition , language understanding and generation , and speech output . WIT features an incremental understanding method ( Nakano et al. , 1999b ) that makes it possible to build a robust and real-time system . In addition , WIT compiles domain-dependent system specifications into internal knowledge sources so that building systems is easier .
To capture syntagmatic patterns , we choose in this work standard n-gram language models as the basis for a concrete model implementing our scheme . This choice is inspired by recent work on learning syntactic categories ( Yatbaz et al. , 2012 ) , which successfully utilized such language models to represent word window contexts of target words . However , we note that other richer types of language models , such as class-based ( Brown et al. , 1992 ) or hybrid ( Tan et al. , 2012 ) , can be seamlessly integrated into our scheme .
Other studies which view lR as a query generation process include Maron and Kuhns , 1960 ; Hiemstra and Kraaij , 1999 ; Ponte and Croft , 1998 ; Miller et al , 1999 . Our work has focused on cross-lingual retrieval .
Quality is a subjective measure , which is best judged by the users of the system ( i.e. , the help-desk customers or operators ) . In Section 5 , we discuss the difficulties associated with such user studies , and describe a human-based evaluation we conducted for a small subset of the responses generated by our system ( Marom and Zukerman 2007b ) . However , our more comprehensive evaluation is an automatic one that treats the responses generated by the help-desk operators as model responses , and performs text-based comparisons between the model responses and the automatically generated ones .
A plethora of works has been done to provide linguistic explanations on the formation of such word , yet none so far has led to any consensus . Hook ( 1981 ) considers the second verb V2 as an aspectual complex comparable to the auxiliaries . Butt ( 1993 ) argues CV formations in Hindi and Urdu are either morphological or syntactical and their formation take place at the argument structure .
This is why we must make do with approximations to the EM algorithm . In this situation , Brown et al. ( 1993b , 293 ) recommend `` evaluating the expectations using only a single , probable alignment . '' The single most probable assignment Amax is the maximum a posteriori ( MAP ) assignment :
While dependencybased features capture the syntactic dependencies , frame-semantic features encode the semantic representation of the concepts in a sentence . Following our previous work on stance classification ( Hasan and Ng , 2013c ) , we employ three types of features computed based on the frame-semantic parse of each sentence in a post obtained from SEMAFOR ( Das et al. , 2010 ) . Frame-word interaction features encode whether two words appear in different elements of the same frame .
Given the ubiquitous nature of natural languages and electronic text , text is an obvious medium to consider . However , the literature on Linguistic Steganography , in which linguistic properties of a text are modified to hide information , is small compared with other media ( Bergmair , 2007 ) . The likely reason is that it is easier to make changes to images and other nonlinguistic media which are undetectable by an observer .
In order to improve the grammaticality checking , we use a parser as an addition to the basic Google ngram method . We use the Clark and Curran ( 2007 ) CCG parser to analyse the sentence before and after paraphrasing . Combinatory Categorial Grammar ( CCG ) is a lexicalised grammar formalism , in which CCG lexical categories -- typically expressing subcategorisation information -- are assigned to each word in a sentence .
Some generalizations of our method are fairly straightforward . 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 .
Selkirk ( 1984 ) and Nespor and Vogel ( 1986 ) take a similar approach , but within a different theoretical framework . Previous versions of our work , as described in Bachenko et al. ( 1986 ) also assume that phrasing is dependent on predicate-argument structure . The problem here is that the phrasing in observed data often ignores the argument status of constituents .
Several recent works suggest studying coreference jointly with other tasks . Lee et al. ( 2012 ) model entity coreference and event coreference jointly ; Durrett and Klein ( 2014 ) consider joint coreference and entity-linking . The work closest to ours is that of Lassalle and Denis ( 2015 ) , which studies a joint anaphoricity detection and coreference resolution framework .
The second machine is a dictionary that accepts characters and produces identifiers corresponding to dictionary entries . The final machine is a trigram language model , specifically a Kneser-Ney ( Chen and Goodman , 1998 ) based backoff language model . Differing from ( Lee et al. , 2003 ) , we have also introduced an explicit model for un ` As an example , we do not chain mentions with different gender , number , etc. known words based upon a character unigram model , although this model is dominated by an empirically chosen unknown word penalty .
In IGEN , the plans can involve any goals or actions that could be achieved via communication . Hovy has described another text planner that builds similar plans ( Hovy 1988b ) . This system , however , starts with a list of information to be expressed and merely arranges it into a coherent pattern ; it is thus not a planner in the sense used here ( as Hovy makes clear ) .
A kind of word order correlation bias can be effected by conditioning the auxiliary parameters on the relative positions of linked word tokens in their respective texts . Just as easily , we can model link types that coincide with entries in an on-line bilingual dictionary separately from those that do not ( cfXXX Brown et al. 1993 ) . When the auxiliary parameters are conditioned on different link classes , their optimization is carried out separately for each class : B ( links ( u , v ) Icooc ( u , v ) , A-zF ) ( 37 ) scorec ( u , viz = class ( u , v ) ) = log B ( links ( u , v ) lcooc ( u , v ) , ) Section 6.1.1 describes the link classes used in the experiments below .
However , we do not have access to the computational resources necessary to apply knowledge extractors to the 15 million plus citations in the MEDLINE database and directly index their results . As an alternative , we rely on PubMed to retrieve an initial set of hits that we then postprocess in greater detail -- this is the standard pipeline architecture commonly employed in other question-answering systems ( Voorhees and Tice 1999 ; Hirschman and Gaizauskas 2001 ) . The architecture of our system is shown in Figure 1 .
Section 8 compares translations generated from automatically built and manually annotated tectogrammatical representations . We also compare the results with the output generated by the statistical translation system GIZA + + / ISI ReWrite Decoder ( AlOnaizan et al. , 1999 ; Och and Ney , 2000 ; Germann et al. , 2001 ) , trained on the same parallel corpus .
Like CCM , this model is deficient since the same supertags are generated multiple times , and parses with conflicting supertags are not valid . Since we are not generating from the model , this does not introduce difficulties ( Klein and Manning , 2002 ) . One additional complication that must be addressed is that left-frontier non-terminal categories -- those whose subtree span includes the first word of the sentence -- do not have a left-side supertag to use as context .
GermaNet contains only a few conceptual glosses . As they are required to enable test subjects to distinguish between senses , we use artificial glosses composed from synonyms and hypernyms as a surrogate , e.g. for brother : `` brother , male sibling '' vs. `` brother , comrade , friend '' ( Gurevych , 2005 ) . We removed words which had more than three senses .
As a further extension , the extraction procedure reads off the syntactic category of the head of each of the subcategorized syntactic functions : impose ( v , [ subj ( n ) , obj ( n ) , obl : on ] ) .3 In this way , our methodology is able to produce surface syntactic as well as abstract functional subcategorization details . Dalrymple ( 2001 ) argues that there are cases , albeit exceptional ones , in which constraints on syntactic category are an issue in subcategorization . In contrast to much of the work reviewed in Section 3 , which limits itself to the extraction of surface syntactic subcategorization details , our system can provide this information as well as details of grammatical function .
An abridged list of the features that are used in the training data is listed in Table 2 and discussed below . Corpus frequency : ( Vosse , 1992 ) differentiates between misspellings and neologisms ( new words ) in terms of their frequency . His algorithm classifies unknown words that appear infrequently as misspellings , and those that appear more frequently as neologisms .
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 . To obtain satisfactory baseline performance , we tune InHiero system for 5 times using MERT , and then se -
Its strategy is top-down . The types of sentences accepted are essentially those accepted by the original NLC grammar , imperative sentences with nested noun groups and conjunctions ( Ballard 1979 ) . An attempt has been made to build as deep a parse as possible so that sentences with the same meaning result in identical parses .
They proved to be useful in a number of NLP applications such as natural language generation ( Iordanskaja et al. , 1991 ) , multidocument summarization ( McKeown et al. , 2002 ) , automatic evaluation of MT ( Denkowski and Lavie , 2010 ) , and TE ( Dinu and Wang , 2009 ) . One of the proposed methods to extract paraphrases relies on a pivot-based approach using phrase alignments in a bilingual parallel corpus ( Bannard and Callison-Burch , 2005 ) . With this method , all the different phrases in one language that are aligned with the same phrase in the other language are extracted as paraphrases .
Studies have used several different methods to mine web data . 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 .
There are many reasons why a simple word-to-word ( 1-to-1 ) correspondence is not possible for every sentence pair : for instance , auxiliary verbs used in one language but not the other ( e.g. , English He walked and French Il est all ´ e ) , articles required in one language but optional in the other ( e.g. , English Cars use gas and Portuguese Os carros usam gasolina ) , cases where the content is expressed using multiple words in one language and a single word in the other language ( e.g. , agglutination such as English weapons of mass destruction and German Massenvernichtungswaffen ) , and expressions translated indirectly . Due to this inherent ambiguity , manual annotations usually distinguish between sure correspondences for unambiguous translations , and possible , for ambiguous translations ( Och and Ney 2003 ) . The top row of Figure 1 shows two word alignments between an English -- French sentence pair .
• in classic examples like : ( 21 ) Every representative in a company saw most samples . only the available five relative scopings of the quantifiers are produced ( Hobbs and Shieber 1987 , 47 ) , but without the need for a free variable constraint -- the HOU algorithm will not produce any solutions in which a previously bound variable becomes free ; • the equivalences are reversible , and thus the above sentences cart be generated from scoped logical forms ; • partial scopings are permitted ( see Reyle [ 19961 ) • scoping can be freely interleaved with other types of reference resolution ; • unscoped or partially scoped forms are available for inference or for generation at every stage .
( Formally , the function is partial in that it is not defined on an input when there are no derivations or when there are multiple outputs with the same minimal cost . ) In the transducers produced by the training method described in this paper , the source and target positions are in the set -LCB- -1 , 0,1 -RCB- , though we have also used handcoded transducers ( Alshawi and Xia 1997 ) and automatically trained transducers ( Alshawi and Douglas 2000 ) with a larger range of positions . 2.2 Relationship to Standard FSTs
Pierce and Cardie ( 2001 ) have shown , in the context of base noun identification , that combining sample selection and cotraining can be an effective learning framework for large-scale training . Similar approaches are being explored for parsing ( Steedman , Hwa , et al. 2003 ; Hwa et al. 2003 ) .
For example , the sentence `` He reckons the current account deficit will narrow to only $ 1.8 billion in September . '' would be chunked as follows ( Tjong Kim Sang and Buchholz , 2000 ) : [ NP He ] [ VP reckons ] [ NP the current account deficit ] [ VP will narrow ] [ PP ✁ This research is supported by NSF grants IIS-9801638 , ITR-IIS-0085836 and an ONR MURI Award .
It is possible that this could be improved with a better strategy for representing dependency structure for tree kernels , or if the tree kernels could be incorporated into the structural learning framework . The flexible architecture we have presented enables interesting future research : ( i ) a straightforward improvement is the use of lexical similarity to reduce data sparseness , e.g. ( Basili et al. , 2005 ; Basili et al. , 2006 ; Bloehdorn et al. , 2006 ) . However , the similarity between subjective words , which have multiple senses against other words may negatively impact the system accuracy .
Since task initiative models contribution to domain/problemsolving goals , while dialogue initiative affects the cur - 5An alternative strategy to step ( 4 ) is to perform a database lookup based on the ambiguous query and summarize the results ( Litman et al. , 1998 ) , which we leave for future work . rent discourse goal , we developed alternative strategies for achieving the goals in Figure 4 based on initiative distribution , as shown in Table 1 .
This allows to treat data and metadata on different level of abstraction . The Ruby on Rails ( 2006 ) framework permits us to quickly develop web applications without rewriting common functions and classes . We used the Asyncronous Javascript And XML ( or AJAX ) paradigm to create the graphical user interface .
We follow Lewis and Steedman ( 2014 ) in allowing a small set of generic , linguistically-plausible unary and binary grammar rules . We further add rules for combining with punctuation to the left and right and allow for the merge rule X → X X of Clark and Curran ( 2007 ) .
After the PropBank ( Xue and Palmer 2003 ) was built , Xue and Palmer ( 2005 ) and Xue ( 2008 ) have produced more complete and systematic research on Chinese SRL . Moschitti et al. ( 2005 ) has made some preliminary attempt on the idea of hierarchical semantic role labeling .
Automatic corpus-based selection of word pairs is more objective , leading to a balanced dataset with pairs connected by all kinds of lexical-semantic relations . Morris and Hirst ( 2004 ) pointed out that many relations between words in a text are non-classical ( i.e. other than typical taxonomic relations like synonymy or hypernymy ) and therefore not covered by semantic similarity . Previous studies only considered semantic relatedness ( or similarity ) of words rather than concepts .
The referential structures we are going to use are collections of logical theories , but the concept of reference is more general . Some of the intuitions we associate with this notion have been very well expressed by Turner ( 1987 , pp. 7-8 ) : ... Semantics is constrained by our models of ourselves and our worlds . We have models of up and down that are based by the way our bodies actually function .
Other features used by the systems include noun phrases ( Chen and Martin , 2007 ) , word n-grams ( Popescu and Magnini , 2007 ) , emails and URLs ( del ValleAgudo et al. , 2007 ) , etc. . In 2009 , the second WePS campaign showed similar trends regarding the use of NE features ( Artiles et al. , 2009 ) . Due to the complexity of systems , the results of the WePS evaluation do not provide a direct answer regarding the advantages of using NEs over other computationally lighter features such as BoW or word n-grams .
For example , ( wrote a book in three days , attach-verb ) would be annotated as ( wrote , book , in , days , verb ) . The head words can be automatically extracted using a heuristic table lookup in the manner described by Magerman ( 1994 ) . For this learning problem , the supervision is the one-bit information of whether p should attach to v or to n .
McDonald has even argued for extending the model to a large number of components ( McDonald 1988 ) , and several systems have indeed added an additional component between the planner and the linguistic component ( Meteer 1994 ; Panaget 1994 ; Wanner 1994 ) . Reiter describes a pipelined modular approach as a consensus architecture underlying most recent work in generation ( Reiter 1994 ) . As this large body of work makes clear , the modular approach has been very useful , simplifying the design of generators and making them more flexible .
The RenTAL system is implemented in LiLFeS ( Makino et al. , 1998 ) 2 . LiLFeS is one of the fastest inference engines for processing feature structure logic , and efficient HPSG parsers have already been built on this system ( Nishida et al. , 1999 ; Torisawa et al. , 2000 ) .