... ACL and the 4th IJCNLP of the AFNLP, pages 396–404,Suntec, Singapore, 2-7 August 2009.c2009 ACL and AFNLPReducing semantic drift with bagginganddistributional similarity Tara McIntosh and ... and WMEB using just thehand-picked seeds (Shand) and 50 sample super-vised bagging (SgoldBAG). Bagging with samples from Sgoldsuccessfullyincreased the performance of both BASILISK and WMEB ... Lhandtosample from and then another round with the 50sets of randomly unsupervised seeds, Srand.The next decision is how to sample SrandfromLhand. One approach is to use uniform randomsampling...
... correct alignments and thiscauses many mistakes in the distributional simi-larity algorithm. We have given some examples inrows 4 and 5 of table 5.We have used the distributionalsimilarity scoreonly ... Alignment and Measures of Distributional Similarity Lonneke van der Plas & J¨org TiedemannAlfa-InformaticaUniversity of GroningenP.O. Box 7169700 AS GroningenThe Netherlands{vdplas,tiedeman}@let.rug.nlAbstractThere ... usingmeasures of distributional similarity, butthese typically are not able to distin-guish between synonyms and other typesof semantically related words such asantonyms, (co)hyponyms and hypernyms.We...
... combined and the distributional weighting schemas.The combined weighting schema thus showedrelative improvement on the distributional one:1.5% (BNC) and 2.3% (AP) in terms of precision and 9.2% ... each weightedby the distributionalsimilarity of the neighbor tothe test word. Figure 3 compares the precision and learning accuracy of the combined weightingschema to the distributional weighting. ... semantic similarity to other classes.Besides distributional data, our method integratesthis semantic information: the classification decisionis a function of both (1) the distributional similarity of...
... (see Geffet and Dagan, 2004 and Gef-fet and Dagan, 2005, who improve the output of a distributional similarity system for an entailmenttask using a web-based feature inclusion check, and comment ... ‘murder’ and ‘abduct’kill murder abducttwo birds with babies that life her and makecancer cells and his wife and an innocent mana mocking bird thousands of innocent unsuspecting people and or ... Geffet and Ido Dagan. 2004. Feature VectorQuality andDistributional Similarity. ProceedingsOf the 20th International Conference on Computa-tional Linguistics, 2004.Maayan Geffet and Ido...
... Speech and Language, 9:123-152. Ido Dagan, Lillian Lee, and Fernando Pereira. 1999. Similarity- based models of cooccur- rence probabilities. Machine Learning, 34(1- 3) :43-69. Ute Essen and ... Thomas M. Cover and Joy A. Thomas. 1991. Elements of Information Theory. John Wiley. Ido Dagan, Shanl Marcus, and Shanl Marko- vitch. 1995. Contextual word similarity and estimation from ... nando Pereira, and Stuart Shieber for helpful discussions, the anonymous reviewers for their insightful comments, Fernando Pereira for ac- cess to computational resources at AT&T, and...
... 1995. Contextual word similarityand estimation from sparse data. Computer Speech and Lan- guage, 9:123-152. Ido Dagan, Lillian Lee, and Fernando Pereira. 1999. Similarity- based models ... parison of distributional clustering and nearest- neighbors averaging on several large datasets, exploring the tradeoff in similarity- based mod- eling between memory usage on the one hand and estimation ... Douglas Baker and Andrew Kachites McCallum. 1998. Distributional clustering of words for text classification. In Plst Annual International A CM SIGIR Conference on Research and Development...
... Scotlandsubj-of subj-ofwin losecontext reductionPakistanScotland-subj-of-losePakistan-subj-of-win similarity semantic classhead similarity role similarity Pakistanhad ... in the semi-finalScotlandFigure 1: Context reduction andsimilarity levelsdraw this inference, two levels of similarity need tobe taken into account. One concerns the similarity ofthe words ... both the similarity of the heads in the gram-matical relation (e.g., “win” and “lose”) and that ofthe grammatical role (e.g. subject). Figure 1 illus-trates context reduction and similarity...
... distinct typesof word pair similarity: lexical similarity and relational similarity. We present anefficient and flexible technique for imple-menting relational similarityand show theeffectiveness ... relational similarity but not both in com-bination. Previously proposed lexical models in-clude the WordNet-based methods of Kim and Baldwin (2005) and Girju et al. (2005), and the distributional ... co-occurrence probabilityvectors for w1 and w2. Taking kjsdas a measure ofword similarityand introducing parameters α and β to scale the contributions of w1 and w2respec-tively, we retrieve...
... and intriguing question, whereby we construct the syn-tactic and semantic distributionalsimilarity net-work (DSN) and analyze their spectrum to un-derstand their global topology. We observe thatthere ... commonalities and differences be-tween the syntactic and semantic distributional patterns of the words of a language? This study isan initial attempt to answer this fundamental and intriguing ... popular, visualization of distributional similarity is through graphs or networks, whereeach word is represented as nodes and weightededges indicate the extent of distributional similar-ity...
... and describe two methods that can make use of distributionalsimilarity information in Section 3. Experiments and results are presented in Section 4. The last section contains summaries and ... of valid words in certain contexts (Golding and Roth, 1996; Mangu and Brill, 1997). Distributional similarity between words has been investigated and successfully applied in many natural language ... 1998) and language model smoothing (Essen and Steinbiss, 1992; Dagan et al., 1997). An investi-gation on distributionalsimilarity functions can be found in (Lillian Lee, 1999). 3 Distributional...
... similarity into twocategories: taxonomic similarityand associative similarity. Taxonomic similarity, or categorical similarity, is a kind of semantic similarity betweenwords in the same level ... LSA-based, cooccurrence-based and dictionary-based methods, were com-pared in terms of the ability to representtwo kinds of similarity, i.e., taxonomic similarity and associative similarity. Theresult ... addresses threemethods, LSA-based, cooccurrence-based, and dictionary-based methods, and two kinds of sim-ilarity, taxonomic similarityand associative sim-ilarity. Word vectors constructed...
... 1994; Lee, 1997; Lin, 1998; Pantel and Lin, 2002; Weeds and Weir, 2003). As it turns out, distributionalsimilarity captures a somewhat loose notion of semantic similarity (see Table 1). It does ... Southampton, U.K. Geffet, Maayan and Ido Dagan, 2004. Feature Vector Quality andDistributional Similarity. In Proc. of Col-ing-04. Geneva. Switzerland. Grefenstette, Gregory. 1994. ... using the filter, with 20 and 40 feature sampling, com-pared to RFF top-40 and RFF top-26 simi-larities. ITA-20 and ITA-40 denote the web-sampling method with 20 and random 40 features, respectively....
... quality photocopies, and faxes are still difficult to process and cause many errors. The accu- racy of handwritten OCR is still about 90% (Hilde- brandt and Liu, 1993), and it worsens dramatically ... 91% for magazines and introductory textbooks of science and technology. (Ito and Maruyama, 1992) used part of speech bigram model and beam search in order to get multiple candidates in their ... al., 1991; Golding and Schabes, 1996). Similar techniques are used for correcting the output of English OCRs (Tong and Evans, 1996) and English speech recognizers (Ring- ger and Allen, 1996)....
... grammaticality, and coherence of the essay (Higgins et al., 2004), and the assessment of short student answers (Lea-cock and Chodorow, 2003; Pulman and Sukkarieh,2005; Mohler and Mihalcea, 2009), ... & St. Onge (1998) [HSO], and two corpus-based measures: Latent Semantic Analysis [LSA](Landauer and Dumais, 1997) and Explicit Seman-tic Analysis [ESA] (Gabrilovich and Markovitch,2007).Briefly, ... MA.G. Hirst and D. St-Onge, 1998. Lexical chains as repre-sentations of contexts for the detection and correctionof malaproprisms. The MIT Press.J. Jiang and D. Conrath. 1997. Semantic similarity...
... SIGIR. Fernando Pereira, Naftali Tishby, and Lillian Lee. 1993. Distributional clustering of English words. In Proc. of the Annual Meeting of the ACL. Philip Resnik. 1992. Wordnet anddistributional ... using frequency information (Good, 1953; Katz, 1987; Jelinek and Mercer, 1985; Church and Gale, 1991). Church and Gale (Church and Gale, 1991) show, that for unobserved bigrams, the estimates ... 150 pairs, were constructed randomly and were restricted to words with indi- vidual frequencies between 500 and 2500. We term these two sets as the occurring and non-occurring sets. The...