hrithikpiyush/acl-arc huggingface dataset:
On the other hand , donate is coded [ T1 ( to ) ] , which tells us that it does not undergo dative movement but does require a prepositional phrase headed by `` to '' : ( Takes NP NP ToPP ) . There are many more distinctions which are conveyed by the conjunction of grammar codes and word qualifiers ( see Michiels , 1982 , for further details ) . However , exploiting this information to the full would be a non-trivial task , because it would require accessing the relevant knowledge about the words contained in the qualifier fields from their LDOCE entries .
Word co-occurrence can be defined in various ways . The most common way is to divide each half of the bitext into an equal number of segments and to align the segments so that each pair of segments Si and Ti are translations of each other ( Gale & Church , 1991 ; Melamed , 1996a ) . Then , two word tokens ( u , v ) are said to co-occur in the
In the longer term , therefore , the automatic construction of dictionaries for natural language processing systems may need to be based on techniques for the automatic analysis of large corpora ( eg . Leech et al. , 1983 ) . However , in the short term , the approach outlined in this paper will allow us to produce a relatively sophisticated and useful dictionary rapidly .
Thus phrasing , in their approach , is only indirectly related to syntax , since readjustment is done by special rules outside the grammar proper . Langendoen ( 1975 ) proposes readjustment rules similar to those of Chomsky and Halle , but he claims that the readjustment of structure is part of the grammar , not part of the performance model . He thus makes explicit what is often a tacit assumption in both the linguistic and psycholinguistic literature2 -- that there is a direct connection between syntactic constituency and prosodic phrasing , with apparent misalignments readjusted before syntax interfaces with prosodic phonology .
Compare an example of a Czech tectogrammatical tree after the lexical transfer step ( Figure 3 ) , with the original English sentence in Figure 2 . financial news , we created a probabilistic CzechEnglish dictionary by running GIZA + + training ( translation models 1-4 , see Och and Ney ( 2000 ) ) on the training part of the English-Czech WSJ parallel corpus extended by the parallel corpus of entry/translation pairs from the manual dictionary . As a result , the entry/translation pairs seen in the parallel corpus of WSJ become more probable .
Compared with retraining mode , incremental training can improve the training efficiency . In the field of machine learning research , incremental training has been employed in the work ( Cauwenberghs and Poggio , 2001 ; Shilton et al. , 2005 ) , but there is little work for tuning parameters of statistical machine translation . The biggest difficulty lies in that the feature vector of a given training example , i.e. translation example , is unavailable until actually decoding the example , since the derivation is a latent variable .
Maximum-posterior estimation tries to maximize P ( 0 ) · Hi fθ ( xi , yi ) where P ( 0 ) is a prior probability . In a log-linear parameterization , for example , a prior that penalizes feature strengths far from 1 can be used to do feature selection and avoid overfitting ( Chen and Rosenfeld , 1999 ) . The EM algorithm ( Dempster et al. , 1977 ) can maximize these functions .
As in SC induction , we use the ACE Phase 2 coreference corpus for evaluation purposes , acquiring the coreference classifiers on the 422 training texts and evaluating their output on the 97 test texts . We report performance in terms of two metrics : ( 1 ) the Fmeasure score as computed by the commonly-used MUC scorer ( Vilain et al. , 1995 ) , and ( 2 ) the accuracy on the anaphoric references , computed as the fraction of anaphoric references correctly resolved . Following Ponzetto and Strube ( 2006 ) , we consider an anaphoric reference , NPi , correctly resolved if NPi and its closest antecedent are in the same coreference chain in the resulting partition .
com are examples of sites which perform web people search , although with limited disambiguation capabilities . A study of the query log of the AllTheWeb and Altavista search sites gives an idea of the relevance of the people search task : 11-17 % of the queries were composed of a person name with additional terms and 4 % were identified as person names ( Spink et al. , 2004 ) . According to the data available from 1990 U.S. Census Bureau , only 90,000 different names are shared by 100 million people ( Artiles et al. , 2005 ) .
We also used our DCA system to assign capitalized words , abbreviations , and sentence breaks , retaining only cases assigned by the strategies with an accuracy not less than 99.8 % . This was done because purely unsupervised techniques ( e.g. , Baum-Welch [ Baum and Petrie 1966 ] or Brill 's [ Brill 1995b ] ) enable regularities to be induced for word classes which contain many entries , exploiting the fact that individual words that belong to a POS class occur in different ambiguity patterns . Counting all possible POS combinations in these ambiguity patterns over multiple patterns usually produces the right combinations as the most frequent .
This problem is exacerbated by syntax-augmented MT with its thousands of nonterminals , and made even worse by its joint source-and-target extension . Future research should apply the work of Blunsom et al. ( 2008 ) and Blunsom and Osborne ( 2008 ) , who marginalize over derivations to find the most probable translation rather than the most probable derivation , to these multi-nonterminal grammars . All source code underlying this work is available under the GNU Lesser General Public License as part of the ` SAMT ' system at : www.cs.cmu.edu/˜zollmann/samt
For example , Gale and Church ( 1991 , 154 ) suggest that `` 02 , a x2-like statistic , seems to be a particularly good choice because it makes good use of the off-diagonal cells '' in the contingency table . In informal experiments described elsewhere ( Melamed 1995 ) , I found that the G2 statistic suggested by Dunning ( 1993 ) slightly outperforms 02 . Let the cells of the contingency table be named as follows :
Recall that there are eight ways of incorto select an antecedent for each NP in a test text . porating these two KSs into our resolver : they can Following Soon et al. ( 2001 ) , we select as the aneach be represented as a constraint or as a feature , tecedent of each NP , NPS , the closest preceding NP and they can be applied to the resolver in isolation that is classified as coreferent with NPS . If no such and in combination .
The output probabilities were modeled as four-dimensional Gaussians mixtures with diagonal covariance matrices . Using the section labels , the HMM was trained using the HTK toolkit ( Young et al. , 2002 ) , which efficiently performs the forward-backward algorithm and BaumWelch estimation . For testing , we performed a Viterbi ( maximum likelihood ) estimation of the label of each test sentence/vector ( also using the HTK toolkit ) .
On the other hand , ( Rogati and Yang , 2002 ) reports the X2 to produce best performance . In this paper , we use TF-IDF ( a kind of augmented DF ) as a feature selection criterion , in order to ensure results are comparable with those in ( Yahyaoui , 2001 ) . TF-IDF ( term frequency-inverse document frequency ) is one of the widely used feature selection techniques in information retrieval ( Yates and Neto , 1999 ) .
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 ) . However , we do not know of any such investigations for Indian languages , which
The best performance on the Brown corpus , a 0.2 % error rate , was reported by Riley ( 1989 ) , who trained a decision tree classifier on a 25-million-word corpus . In the disambiguation of capitalized words , the most widespread method is POS tagging , which achieves about a 3 % error rate on the Brown corpus and a 5 % error rate on the WSJ corpus , as reported in Mikheev ( 2000 ) . We are not aware of any studies devoted to the identification of abbreviations with comprehensive evaluation on either the Brown corpus or the WSJ corpus .
One response to this problem is to apply some kind of thresholding , and either ignore counts below the threshold , or only apply the test to tables that do not contain low counts . The problem with this approach is that any threshold is , to some extent , arbitrary , and there is evidence to suggest that , for some tasks , low counts are important ( Collins and Brooks , 1995 ) . Another approach would be to use Fisher 's exact test , which can be applied to tables regardless of the size of the counts .
Zollmann and Venugopal ( 2006 ) substituted the non-terminal X in hierarchical phrase-based model by extended syntactic categories . Zollmann and Vogel ( 2011 ) further labeled the SCFG rules with POS tags and unsupervised word classes . Our work differs from theirs in that we present a Bayesian model to learn effective STSG translation rules and U-tree structures for tree-based translation models , rather than designing a labeling strategy for translation rules .
Steganography is concerned with hiding information in some cover medium , by manipulating properties of the medium in such a way that the hidden information is not easily detectable by an observer ( Fridrich , 2009 ) . The covert communication is such that the very act of communication is to be kept secret from outside observers .
Each of these probabilities in turn affects multiple arcs in the composed FST of Fig. 1a . We offer a theorem that highlights the broad applicability of these modeling techniques .4 If f ( input , output ) is a weighted regular relation , then the following statements are equivalent : ( 1 ) f is a joint probabilistic relation ; ( 2 ) f can be computed by a Markovian FST that halts with probability 1 ; ( 3 ) f can be expressed as a probabilistic regexp , i.e. , a regexp built up from atomic expressions a : b ( for a E E U -LCB- E -RCB- , b E A U -LCB- E -RCB- ) using concatenation , probabilistic union + p , and probabilistic closure * p. For defining conditional relations , a good regexp language is unknown to us , but they can be defined in several other ways : ( 1 ) via FSTs as in Fig. 1c , ( 2 ) by compilation of weighted rewrite rules ( Mohri and Sproat , 1996 ) , ( 3 ) by compilation of decision trees ( Sproat and Riley , 1996 ) , ( 4 ) as a relation that performs contextual left-to-right replacement of input substrings by a smaller conditional relation ( Gerdemann and van Noord , 1999 ) ,5 ( 5 ) by conditionalization of a joint relation as discussed below . A central technique is to define a joint relation as a noisy-channel model , by composing a joint relation with a cascade of one or more conditional relations as in Fig. 1 ( Pereira and Riley , 1997 ; Knight and Graehl , 1998 ) .
were noun-noun pairs , 15 % noun-verb pairs and so on . Word pairs containing polysemous words are expanded to concept pairs using GermaNet ( Kunze , 2004 ) , the German equivalent to WordNet , as a sense inventory for each word . It is the most complete resource of this type for German .
The created tree nodes are labeled by the non-terminals described in section 3 . Using the initial target U-trees , source sentences and word alignment , we extract minimal GHKM translation rules7 in terms of frontier nodes ( Galley et al. , 2004 ) . Frontier nodes are the tree nodes that can map onto contiguous substrings on the source side via word alignment .
Many learning tasks for natural language processing require supervised training ; that is , the system successfully learns a concept only if it has been given annotated training data . 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 ) .
Tjong Kim Sang ( 1999 ) presented result for composite NP , obtained by repeated cascading , similar to our results with seperate base and composite NPs and no internal structure . Our results are lower than those of full parsers , e.g. , Collins ( 1997 ) as might be expected since much less structural data , and no lexical data are being used .
This is likely a result of MSA having a rich agreement system , covering both verb -- subject and noun -- adjective relations . The result holds for both the MaltParser ( Nivre 2008 ) and the Easy-First Parser ( Goldberg and Elhadad 2010 ) . Additionally , almost all work to date in MSA morphological analysis and part-ofspeech ( POS ) tagging has concentrated on the morphemic form of the words .
can be captured within plug-ins that can then integrate with the environment and utilise its functionality . This system has been successfully tested with the development of plug-ins supporting instant messaging , distributed video encoding ( Hughes and Walkerdine , 2005 ) , distributed virtual worlds ( Hughes et al. , 2005 ) and digital library management ( Walkerdine and Rayson , 2004 ) . It is our intention to implement our distributed corpus annotation framework as a plugin .
3 ) , we train on a set of candidates with precision larger than 50 % . We then use Illinois Chunker ( Punyakanok and Roth , 2001 ) 6 to extract more noun phrases from the text and employ Collins head rules ( Collins , 1999 ) to identify their heads . When these extracted heads do not overlap with gold mention heads , we treat them as negative examples .
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 . Bashir ( 1993 ) tried to construct a semantic analysis based on `` prepared '' and `` unprepared mind '' .
The inter-coder agreement figures obtained for the three types of annotation are given in Table 2 . feature Cohen 's k corrected k agreement 73.59 98.74 dial act 84.53 98.87 turn 73.52 99.16 Table 2 : Inter-coder agreement on feedback expression annotation Although researchers do not totally agree on how to measure agreement in various types of annotated data and on how to interpret the resulting figures , see Artstein and Poesio ( 2008 ) , it is usually assumed that Cohen 's kappa figures over 60 are good while those over 75 are excellent ( Fleiss , 1971 ) . Looking at the cases of disagreement we could see that many of these are due to the fact that the annotators had forgotten to remove some of the features automatically proposed by ANVIL from the latest annotated element .
Table 1 gives the results of these experiments and compares them with some other statistical parsers ( resp . Collins 1996 , Charniak 1997 , Collins 1999 and Charniak 2000 ) . While the PCFG reduction of Bod ( 2001 ) obtains state-of-the-art results on the WSJ , comparable to Charniak ( 2000 ) , Bonnema et al. 's estimator performs worse and is comparable to Collins ( 1996 ) .
While the setup of King provides a clear formal basis for basic HPSG grammars , nothing is said about how special linguistic mechanisms like lexical rules fit into this formal setup . Two formalizations of lexical rules as used by HPSG linguists have been proposed , the meta-level lexical rules ( MLRs ; Calcagno 1995 ; Calcagno and Pollard 1995 ) and the . description-level lexical rules ( DLRs ; Meurers 1995 ) .5 2.2.1 Meta-Level Lexical Rules .
In order to build English-Spanish phrase tables for our experiments , we used the freely available Europarl V. 4 , News Commentary and United Nations Spanish-English parallel corpora released for the WMT101 . We run TreeTagger ( Schmid , 1994 ) for tokenization , and used the Giza + + ( Och and Ney , 2003 ) to align the tokenized corpora at the word level . Subsequently , we extracted the bilingual phrase table from the aligned corpora using the Moses toolkit ( Koehn et al. , 2007 ) .
The aim is merely to show that we can , to a first approximation , provide a reasonably fully worked out description of these phenomena in a truly bidirectional way . We then go on to compare the current approach with that of some other theories with similar aims : the `` standard '' version of quasi-logical form implemented in the Core Language Engine , as rationally reconstructed by Alshawi and Crouch ( 1992 ) and Crouch and Pulman ( 1994 ) ; underspecified Discourse Representation Theory ( Reyle 1993 ) ; and the `` glue language '' approach of Dalrymple et al. ( 1996 ) . Finally , we discuss some of the semantic and logical issues raised by the approach described here , in particular the extent to which the theory meets the desiderata for accounts of underspecification outlined by van Eijck and Jaspars ( 1996 ) , and the extent to which the theory supplies a methodologically satisfactory account of truth and interpretation for sentences involving contextually dependent constructs .
In WordNet , if an adjective ascribes a value to a noun concept , e.g. round gives a value of shape , WordNet will contain a pointer between the adjective and the noun by which the appropriate attribute is lexicalised , e.g. between round and shape . Using WordNet , annotating the sem feature of an adjective involves first choosing the correct sense for the adjective 2Some descriptions of int modifiers can be found in ( Cheng and Mellish , 2000b ) . and then mapping the word sense to a noun concept in the WordNet ontology .
Many approaches to cross-lingual IR have been published . One common approach is using Machine Translation ( MT ) to translate the queries to the language of the documents or translate documents to the language of the queries ( Gey et al , 1999 ; Oard , 1998 ) . For most languages , there are no MT systems at all .
But syntactic relations do not suffice to resolve anaphora : Hobbs ' ( 1976 ) algorithm for resolving the reference of pronouns , depending only on the surface syntax of sentences in the text , when applied to `` it '' in the example paragraph , fails in both cases to identify the most likely referent NP . Adding selectional restrictions ( semantic feature information , Hobbs 1977 ) does not solve the problem , because isolated features offer only part of the background knowledge necessary for reference disambiguation . Later , Hobbs ( 1979 , 1982 ) proposed a knowledge base in which information about language and the world would be encoded , and he emphasized the need for using `` salience '' in choosing facts from this knowledge base .
Although many problems share algorithms and data structures there is a tendency to reinvent the wheel . Software engineering research on Generative Programming ( Czarnecki and Eisenecker , 2000 ) attempts to solve these problems by focusing on the development of configurable elementary components and knowledge to combine these components into complete systems . Our infrastructure for NLP will provide high performance ) components inspired by Generative Programming principles .
The results of two different evaluations of the automatically generated f-structures are presented in Table 2 . Both use the evaluation software and triple encoding presented in Crouch et al. ( 2002 ) . The first of these is against the DCU 105 , a gold-standard set of 105 hand-coded f-structures
As mentioned earlier , there are some non-standard phenomena exist between different languages , that cause challenges for synchronized formalisms . In this Section , we will describe some example cases , which are drawn from the problem of using synchronous formalisms to define translations between languages ( e.g. Shieber ( 1994 ) cases ) . Due to lack of space we will only brief on some of these non-standard cases without going into the details .
Mention detection is rarely studied as a stand-alone research problem ( Recasens et al. ( 2013 ) is one key exception ) . Most coreference resolution work simply mentions it in passing as a module in the pipelined system ( Chang et al. , 2013 ; Durrett and Klein , 2013 ; Lee et al. , 2011 ; Bj ¨ orkelund and Kuhn , 2014 ) . However , the lack of emphasis is not due to this being a minor issue , but rather , we think , its difficulty .
In addition , we tried to ensure that the sets of cases shown to the judges were of similar quality , so that the judges ' assessments would be comparable . Because the judges do not evaluate the same cases , we could not employ standard inter-annotator agreement measures ( Carletta 1996 ) . However , it is still necessary to
For example , Jokinen and Ragni ( 2007 ) and Jokinen et al. ( 2008 ) find that machine learning algorithms can be trained to recognise some of the functions of head movements , while Reidsma et al. ( 2009 ) show that there is a dependence between focus of attention and assignment of dialogue act labels . Related are also the studies by Rieks op den Akker and Schulz ( 2008 ) and Murray and Renals ( 2008 ) : both achieve promising results in the automatic segmentation of dialogue acts using the annotations in a large multimodal corpus . Work has also been done on prosody and gestures in the specific domain of map-task dialogues , also targeted in this paper .
The understanding of natural language text includes not only analysis of syntactic structure , but also of semantic content . Due to advances in statistical syntactic parsing techniques ( Collins , 1997 ; Charniak , 2001 ) , attention has recently shifted towards the harder question of analyzing the meaning of natural language sentences . A common lexical semantic representation in the computational linguistics literature is a frame-based model where syntactic arguments are associated with various semantic roles ( essentially frame slots ) .
Since it is the `` highest '' path , flit is the most plausible ( relative to R ) interpretation of the words that appear in the sentence . Because it is also consistent , it will be chosen as a best interpretation of S , ( cfXXX Zadrozny 1987a , 1987b ) . Another theory , consisting of f ' = -LCB- el , sh2 , pl , b2 , dl -RCB- and S , saying that A space vehicle came into the harbor and caused a disease/illness is less plausible according to that ordering .
For our experiments we used the standard division of the WSJ ( Marcus et al. 1993 ) , with sections 2 through 21 for training ( approx . 40,000 sentences ) and section 23 for testing ( 2416 sentences 100 words ) ; section 22 was used as development set .
experiments to probe the brain signatures of verb-verb combinations and propose cognitive as well as computational models regarding the possible organization and processing of Bangla CVs in the mental lexicon ( ML ) . With respect to this , we apply the different priming and other lexical decision experiments , described in literature ( Marslen-Wilson et al. , 1994 ; Bentin , S. and Feldman , 1990 ) specifically for derivationally suffixed polymorphemic words and compound verbs of Bangla . Our cross-modal and masked priming experiment on Bangla derivationally suffixed words shows that morphological relatedness between lexical items triggers a significant priming effect , even when the forms are phonologically/orthographically unrelated .