hrithikpiyush/acl-arc huggingface dataset:
In contrast , our dependency-based word subsequence kernel , which also computes similarity between two dependency trees , is very transparent with the implicit features being simply the dependency paths . Their kernel is also very time consuming and in their more general sparse setting it requires O ( mn3 ) time and O ( mn2 ) space , where m and n are the number of nodes of the two trees ( m > = n ) ( Zelenko et al. , 2003 ) . Bunescu and Mooney ( 2005a ) give a shortest path dependency kernel for relation extraction .
1 Most modern Arabic writing ( web , novels , articles ) are written without vowels . include decision tree learning and Bayesian learning , nearest neighbor learning , and artificial neural networks , early such works may be found in ( Lewis and Ringnette , 1994 ) , ( Creecy and Masand , 1992 ) and ( Wiene and Pedersen , 1995 ) , respectively . The bulk of the text categorization work has been devoted to cope with automatic categorization of English and Latin character documents .
AJAX is not a technology in itself but a term that refers to the use of a group of technologies together , in particular Javascript and XML . In other words AJAX is a web development technique for creating interactive web applications using a combination of XHTML and CSS , Document Object Model ( or DOM ) , the XMLHTTPRequest object ( Wikipedia , 2005 ) . AJAX paradigm has been recently defined , when someone has rediscovered a simple function originally developed by Microsoft as ActiveX control .
3We also ran preliminary experiments with memory-based learning but found that this gave consistently lower accuracy . converted to numerical features using the standard technique of binarization , and we split values of the FEATS field into its atomic components .4 For some languages , we divide the training data into smaller sets , based on some feature s ( normally the CPOS or POS of the next input token ) , which may reduce training times without a significant loss in accuracy ( Yamada and Matsumoto , 2003 ) . To avoid too small training sets , we pool together categories that have a frequency below a certain threshold t.
Linguistic preprocessing of text documents is carried out by re-using smes , an information extraction core system for real-world German text processing ( Neumann et al. , 1997 ) . The fundamental design criterion of smes is to provide a set of basic , powerful , robust , and efficient STP components and
For the English − > French language pair , Juola gives results of 61 % correct translation when the system is tested on the training corpus , and 36 % accuracy when it is evaluated with test data . For English − > Urdu , Juola ( 1997 , page 213 ) notes that `` the system learned the original training corpus ... perfectly and could reproduce it without errors '' ; that is , it scored 100 % accuracy when tested against the training corpus . On novel test sentences , he gives results of 72 % correct translation .
However , our implementation applies the construction separately for each nonterminal in a set N , such that recursive ( N ) = self , which leads to a separate subautomaton of the compact representation ( Section 3 ) . See Nederhof ( 1998 ) for a variant of this approximation that constructs finite transducers rather than finite automata . We have further implemented a parameterized version of the RTN approximation .
It is an extension of shallow parsing towards handling composite and multiple patterns , while maintaining the local nature of the task , and simplicity of training material . One approach to partial parsing was presented by Buchholz et al. ( 1999 ) , who extended a shallow-parsing technique to partial parsing . The output of NP and VP chunking was used as an input to grammatical relation inference .
Jijkoun and de Rijke ( 2005 ) compared different variants of retrieval techniques . Soricut and Brill ( 2006 ) compared a predictive approach ( statistical translation ) , a retrieval approach based on a language-model , and a hybrid approach which combines statistical chunking and traditional retrieval . Two significant differences between help-desk and FAQs are the following .
Even though this work is carried out for terminographical and lexicographical purposes , it can certainly be of use in other applications , namely information retrieval . Indeed , such rich semantic links can be used to extend indices or reformulate queries ( similar to the work by Voorhees ( 1994 ) with WoRDNET relations ) .
This is largely due to the fact that pipelined systems can not perform general search over a decision space that includes decisions made in more than one module . Despite these arguments , most applied NLG systems use a pipelined architecture ; indeed , a pipeline was used in every one of the systems surveyed by Reiter ( 1994 ) and Paiva ( 1998 ) . This may be because pipelines have many engineering advantages , and in practice the sort of problems pointed out by Danlos and other pipeline critics do not seem to be a major problem in current applied NLG systems ( Mittal et al. 1998 ) .
This can be expressed as a measure of the coverage of the induced lexicon on new data . Following Hockenmaier , Bierner , and Baldridge ( 2002 ) , Xia ( 1999 ) , and Miyao , Ninomiya , and Tsujii ( 2004 ) , we extract a reference lexicon from Sections 02 -- 21 of the WSJ . We then compare this to a test lexicon from Section 23 .
The shape-based metric . The only disambiguation metric that we used in our previous work ( Marcu , 1997b ) was the shape-based metric , according to which the `` best '' trees are those that are skewed to the right . The explanation for this metric is that text processing is , essentially , a left-to-right process .
Very briefly , concept maps show the relationships between concepts labelling both nodes and arcs . Every arc always has a definite direction , i.e. arcs are arrows ( Novak , 1998 ) . In contrast , mind maps spread from a centre , with branches radiating out .
4.2.1 An Expectation-Maximization-Based Learner . In the first experiment , we use an induction algorithm ( Hwa 2001a ) based on the expectation-maximization ( EM ) principle that induces parsers for PLTIGs . The algorithm performs heuristic search through an iterative reestimation procedure to find local optima : sets of values for the grammar parameters that maximizes the grammar 's likelihood of generating the training data .
The statistical significance test is performed by the re-sampling approach ( Koehn , 2004 ) . To create the baseline system , we use the opensource Joshua 4.0 system ( Ganitkevitch et al. , 2012 ) to build a hierarchical phrase-based ( HPB ) system , and a syntax-augmented MT ( SAMT ) 11 system ( Zollmann and Venugopal , 2006 ) respectively . The translation system used for testing the effectiveness of our U-trees is our in-house stringto-tree system ( abbreviated as s2t ) .
During the 6 For Sent-Pred we also experimented with grammatical and sentence-based syntactic features , such as number of syntactic phrases , grammatical mood , and grammatical person ( Marom and Zukerman 2006 ) , but the simple binary bag-of-lemmas representation yielded similar results . 7 We employed the LIBSVM package ( Chang and Lin 2001 ) .
The diagnoser , based on Dzikovska et al. ( 2008b ) , outputs a diagnosis which consists of lists of correct , contradictory and non-mentioned objects and relations from the student 's answer . At present , the system uses a heuristic matching algorithm to classify relations into the appropriate category , though in the future we may consider a classifier similar to Nielsen et al. ( 2008 ) .
It is interesting to compare this analysis with that described in Dalrymple , Shieber , and Pereira ( 1991 ) and Pereira ( 1990 , 1991 ) . Recall that in their treatment , quantified noun phrases are treated in two stages : firstly , what they call a `` free variable '' of type e is introduced in the NP position , with an associated `` quantifier assumption , '' which is added as a kind of premise .
While the models of Collins ( 1996 ) and Eisner ( 1996 ) restricted the fragments to the locality of head-words , later models showed the importance of including context from higher nodes in the tree ( Charniak 1997 ; Johnson 1998a ) . The importance of including nonheadwords has become uncontroversial ( e.g. Collins 1999 ; Charniak 2000 ; Goodman 1998 ) . And Collins ( 2000 ) argues for `` keeping track of counts of arbitrary fragments within parse trees '' , which has indeed been carried out in Collins and Duffy ( 2002 ) who use exactly the same set of ( all ) tree fragments as proposed in Bod ( 1992 ) .
The phrase `` which hospital '' is finally absorbed by the preposition 's object . The example used to illustrate the power of ATNs ( Woods 1986 ) , `` John was believed to have been shot , '' also parses correctly , because the [ object ] node following the verb `` believed '' acts as both an absorber and a ( re ) generator . Cases of crossed traces are automatically blocked because the second CURRENT-FOCUS gets moved into the FLOAT-OBJECT position at the time of the second activator , overriding the preexisting FLOAT-OBJECT set up by the earlier activator .
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 ) . We applied our system to the XTAG English grammar ( The XTAG Research Group , 2001 ) 3 , which is a large-scale FB-LTAG grammar for English .
We seeked to exploit this ability to generalize to improve the dictionary based model . As in ( Lee et al. , 2003 ) , we used unsupervised training data which is automatically segmented to discover previously unseen stems . In our case , the character n-gram model is used to segment a portion of the Arabic Gigaword corpus .
( 2 ) B is eighty-three . To address this limitation , our previous work ( Zhang and Chai , 2009 ) has initiated an investigation on the problem of conversation entailment . The problem was formulated as follows : given a conversation discourse D and a hypothesis H concerning its participant , the goal was to identify whether D entails H. For instance , as in Example 1 , the first hypothesis can be entailed from the
It is common to define further useful operations ( as macros ) , which modify existing relations not by editing their source code but simply by operating on them `` from outside . '' ∗ A brief version of this work , with some additional material , first appeared as ( Eisner , 2001a ) . A leisurely journal-length version with more details has been prepared and is available .
Lekakos and Giaglis ( 2007 ) implemented a supervised version of this approach for a recommender system , as opposed to our unsupervised version . They also proposed two major categories of meta-learning approaches for recommender systems , merging and ensemble , each subdivided into the more specific subclasses suggested by Burke ( 2002 ) as follows . The merging category corresponds to techniques where the individual methods affect each other in different ways ( this category encompasses Burke 's feature combination , cascade , feature augmentation , and meta-level sub-categories ) .
Following the non-lexicalist tradition , these primitives are argued to occupy functional projections in the syntactic structure , as so-called light verbs . Here , I adopt the model proposed by Marantz ( 1997 ) and decompose lexical verbs into verbalizing heads and verbal roots . Verbalizing heads introduce relevant eventive interpretations in the syntax , and correspond to ( assumed ) universal primitives of the human cognitive system .
We include a point in the final alignment if the average of the posteriors under the two models for that point is above a threshold . This heuristic is called soft union ( DeNero and Klein 2007 ) . Figure 8 shows the Precision/Recall curves after symmetrization for the En-Fr corpus .
We adopt the three-level semantics as a formal tool for the analysis of paragraphs . This semantics was constructed ( Zadrozny 1987a , 1987b ) as a formal framework for default and commonsense reasoning . It should not come as a surprise that we can now use this apparatus for text/discourse analysis ; after all , many natural language inferences are based on defaults , and quite often they can be reduced to choosing most plausible interpretations of predicates .
R98 ( , „ , d0t ) is the modularised version of R98 for experimenting with different similarity measures . R98 ( , , , , „ ) uses a variant of Kozima 's semantic similarity measure ( Kozima , 1993 ) to compute block similarity . Word similarity is a function of word cooccurrence statistics in the given document .
5In these experiments the tags are included in the input to the system , but , for compatibility with other parsers , we did not use the hand-corrected tags which come with the corpus . We used a publicly available tagger ( Ratnaparkhi , 1996 ) to tag the words and then used these in the input to the system . 6We found that 80 hidden units produced better performance than 60 or 100 .
However , with the only exceptions represented by WordNet and Wikipedia , most of the aforementioned resources are available only for English . Multilingual lexical databases aligned with the English WordNet ( e.g. MultiWordNet ( Pianta et al. , 2002 ) ) have been created for several languages , with different degrees of coverage . As an example , the 57,424 synsets of the Spanish section of MultiWordNet aligned to English cover just around 50 % of the WordNet 's synsets , thus making the coverage issue even more problematic than for TE .
The features we employ scale well across domains , given that the opinion target vocabularies are substantially different . For future work , we might investigate how machine learning algorithms , which are specifically designed for the problem of domain adaptation ( Blitzer et al. , 2007 ; Jiang and Zhai , 2007 ) , perform in comparison to our approach . Since three of the features we employed in
Performance of the hypothesis : Testing the candidates on the current working hypothesis shows the type of input data on which the hypothesis may perform weakly . That is , if the current hypothesis is unable to label a candidate or is uncertain about it , then the candidate might be a good training example ( Lewis and Catlett 1994 ) . The underlying assumption is that an uncertain output is likely to be wrong .
Intermediate to these two paradigms is the partial decomposition model that argues that different types of morphological forms are processed separately . For instance , the derived morphological forms are believed to be represented as a whole , whereas the representation of the inflected forms follows the morphemic model ( Caramazza et al. , 1988 ) . Traditionally , priming experiments have been used to study the effects of morphology in language processing .
We can say that the grammar conversion enables us to share HPSG parsing techniques in LTAG parsing . Another paper ( Yoshinaga et al. , 2001 ) describes the detailed analysis on the factor of the difference of parsing performance .
Conversion took about 25 minutes CPU time on a 700 Mhz Pentium III Xeon with four gigabytes main memory . The original and the obtained grammar generated exactly the same number of derivation trees in the parsing experiment with 457 sentences from the ATIS corpus ( Marcus et al. , 1994 ) 6 ( the average length is 6.32 words ) . This result empirically attested the strong equivalence of our algorithm .
confirms , percent-subdials ) . The use of running tallies and percentages is based on the assumption that these features are likely to produce generalized predictors ( Litman et al. , 1999 ) . The features obtained via hand-labelling were human transcripts of each user utterance ( tscript ) , a set of semantic labels that are closely related to the system task-type labels ( human-label ) , age ( age ) and gender ( gender ) of the user , the actual modality of the user utterance ( user-modality ) ( one of : nothing , speech , touchtone , speech + touchtone , non-speech ) ,
LDOCE provides considerably more syntactic information than a traditional dictionary . The Longman lexicographers have developed a grammar coding system capable of representing in compact form a nontrivial amount of information , usually to be found only in large descriptive grammars of English ( such as Quirk et al. , 1985 ) . A grammar code describes a particular pattern of behaviour of a word .
18 In this article , we use a newer version of the corpus by Alkuhlani and Habash ( 2011 ) than the one we used in Marton , Habash , and Rambow ( 2011 ) . 19 The paper by Alkuhlani and Habash ( 2012 ) presents additional , more sophisticated models that we do not use in this article . shrunk with these functional feature combinations to 0.3 % .
Note that the focus of this paper is comparing feature utilities in different helpfulness models rather than predicting those types of helpfulness ratings . ( Details of how the average-expert model performs can be found in our prior work ( Xiong and Litman , 2011 ) . )
Better results would be expected by combining the PCFG-LA parser with discriminative reranking approaches ( Charniak and Johnson , 2005 ; Huang , 2008 ) for self training . Self-training should also benefit other discriminatively trained parsers with latent annotations ( Petrov and Klein , 2008 ) , although training would be much slower compared to using generative models , as in our case . In future work , we plan to scale up the training process with more unlabeled training data ( e.g. , gigaword ) and investigate automatic selection of materials that are most suitable for self-training .
The system is trained on the Arabic ACE 2003 and part of the 2004 data . We introduce here a clearly defined and replicable split of the ACE 2004 data , so that future investigations can accurately and correctly compare against the results presented here . There are 689 Arabic documents in LDC 's 2004 release ( version 1.4 ) of ACE data from three sources : the Arabic Treebank , a subset of the broadcast ( bnews ) and newswire ( nwire ) TDT-4 documents .
Typically in the approaches based on raw text , a number of subcategorization patterns are predefined , a set of verb subcategorization frame associations are hypothesized from the data , and statistical methods are applied to reliably select hypotheses for the final lexicon . Brent ( 1993 ) relies on morphosyntactic cues in the untagged Brown corpus as indicators of six predefined subcategorization frames . The frames do not include details of specific prepositions .
We then obtain the composed rules by composing two or three adjacent minimal rules . To build the above s2t system , we first use the parse tree , which is generated by parsing the English side of the bilingual data with the Berkeley parser ( Petrov et al. , 2006 ) . Then , we binarize the English parse trees using the head binarization approach ( Wang et al. , 2007 ) and use the resulting binary parse trees to build another s2t system .
The goal being not necessarily to obtain better parse performance , but to exploit the structure induced from human labeled treebank data while targeting specific extrinsic metrics of quality , which can include task specific metrics or external weak constraints on the parse structure . One obvious approach to this problem is to employ parser reranking ( Collins , 2000 ) . In such a setting , an auxiliary reranker is added in a pipeline following the parser .
Thirdly , it seems clear that the Object Raising rule is straining the limits of what can be reliably extracted from the LDOCE coding system . Ideally , to distinguish between raising and equi verbs , a number of syntactic criteria should be employed ( Perlmutter and Soames , 1979:460 ff . ) .
Table 2 ( b ) reports accuracy , precision , recall , and Fmeasure for four separate binary classifiers ( HMM with LDA ) specifically trained for each of the sections ( one per row in the table ) . The table also presents the closest comparable experimental results reported by McKnight and Srinivasan ( 2003 ) .1 McKnight and Srinivasan ( henceforth , M&S ) created a test collection consisting of 37,151 RCTs from approximately 12 million MEDLINE abstracts dated between 1976 and 2001 . This collection has
Thus , referential communication between the human and the robot becomes difficult . How this mismatched perceptual basis affects referential communication in situated dialogue was investigated in our previous work ( Liu et al. , 2012 ) . In that work , the main focus is on reference resolution : given referential descriptions from human partners , how to identify referents in the environment even though the robot only has imperfect perception of the environment .
Each English word has around 1.5 translations on average . A cooccurrence based stemmer ( Xu and Croft , 1998 ) was used to stem Spanish words . One difference from the treatment of Chinese is to include the English word as one of its own translations in addition to its Spanish translations in the lexicon .
An alternative representation based on Liberman and Prince ( 1977 ) is presented in Selkirk ( 1984 ) , which contends that prosody , including prosodic phrasing , is more properly represented as a grid instead of a tree . Although a grid may be more descriptively suitable for some aspects of prosody ( for example , Sproat and Liberman ( 1987 ) use the grid representation for their implementation of stress assignment in compound nominals ) , we are not aware of any evidence for or against a grid representation of discourseneutral phrasing . Figure 8 shows the phonological phrase tree that is built from the syntactic structure of Figure 7 .
2The message may have been encrypted initially also , as in the figure , but this is not important in this paper ; the key point is that the hidden message is a sequence of bits . 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 .
The first version ( TIMIT ) was developed for the 450 phonetically rich sentences of the TIMIT database ( Lamel et al. 1986 ) . The second version ( RM ) concerns the Resource Management task ( Pallett 1989 ) that has been popular within the DARPA community in recent years . The third version ( VOYAGER ) serves as an interface both with a recognizer and with a functioning database back-end ( Zue et al. 1990 ) .
accuracy . The studies presented by Goldwater and Griffiths ( 2007 ) and Johnson ( 2007 ) differed in the number of states that they used . Goldwater and Griffiths ( 2007 ) evaluated against the reduced tag set of 17 tags developed by Smith and Eisner ( 2005 ) , while Johnson ( 2007 ) evaluated against the full Penn Treebank tag set .
In future work , we would like to identify a set of features , latent variables , and training methods that port well across languages and string-transduction tasks . We would like to use features that look at wide context on the input side , which is inexpensive ( Jiampojamarn et al. , 2007 ) . Latent variables we wish to consider are an increased number of word classes ; more flexible regions -- see Petrov et al. ( 2007 ) on learning a state transition diagram for acoustic regions in phone recognition -- and phonological features and syllable boundaries .
First , the phrasal lexicon is segmented using the marker hypothesis to produce a marker lexicon . This is then generalized , following a methodology based on Block ( 2000 ) , to generate the `` generalized marker lexicon . '' Finally , as a result of the