Computational Linguistics and Intelligent Text Processing: by Namhee Kwon, Eduard Hovy (auth.), Alexander Gelbukh (eds.)

By Namhee Kwon, Eduard Hovy (auth.), Alexander Gelbukh (eds.)

CICLing 2006 (www.CICLing.org) was once the seventh Annual convention on clever textual content Processing and Computational Linguistics. The CICLing meetings are meant to supply a wide-scope discussion board for dialogue of the inner artwork and craft of average language processing study and the simplest practices in its functions. This quantity comprises the papers incorporated typically convention application (full papers) and chosen papers from the poster consultation (short papers). different poster consultation papers have been incorporated in a unique factor of the magazine learn on Computing technology; see informationonthisissue onthe site. Theprevious CICLing meetings on the grounds that 2001 have been additionally released in Springer’s Lecture Notes in laptop technological know-how (LNCS) sequence, vol. 2004, 2276, 2588, 2945, and 3406. The variety of submissions to CICLing 2006 used to be better than that of the former meetings: 141 complete papers and 35 brief papers by means of 480 authors from 37 nations have been submitted for review, see Tables 1 and a pair of. each one submission used to be reviewed through no less than self sustaining software Committee contributors. This publication comprises revised types of forty three complete papers (presented orally on the convention) and sixteen brief papers (presented as posters) via 177 authors from 24 international locations chosen for inclusion within the convention application. The attractiveness price was once 30.4% for complete papers and 45.7% for brief papers.

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The number of LUs in the frames ranges from two to 58; the average number of LUs per frame is 12. 9. 35 senses to each. Manual evaluation of sense-tagging is a notoriously problematic task, and even among human annotators there is typically no more than 80% agreement on the WordNet sense to be assigned to a given word in context. Our task here is somewhat simplified, for several reasons: 1. Sense assignments are not evaluated for words in context, but rather in terms of the word’s association with a FrameNet category and in relation to the set of LUs associated with that category.

We devised a reduced relation set that includes the following relation pairs: example - example, gloss - gloss, hypernym - hypernym, hypernym - hyponym, hyponym hypernym, hyponym - hyponym, synset - example, synset – gloss, and synset - synset. 5) was given to overlaps in example texts, glosses, and synset overlaps with examples and glosses. The rationale for this choice was to focus on synonymy (same concept) and is-a relations (more/less general expression of the same concept). , the synset for urge#v#3 includes “inspire”, which appears in the gloss for encourage#v#2, “inspire with confidence”).

Similarity scores between senses of the same word computed by WNS proved to be extremely low, which is not surprising given that the criterion for distinguishing senses in WordNet is membership in different synsets, one of the main criteria by which similarity is measured by WNS. Clustering based on the similarity matrix for the scores, however, indicated that the method holds some promise for collapsing WordNet senses: for example, the topology of the cluster tree for the verb “press” is given in Figure 1.

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