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RISS 인기검색어
- 검색키워드
- 저자 : ( Robert W. P. Luk ) (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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A Quantitative Analysis of Word텱efinition in a Machine-Readable Dictionary
( Robert W. P. Luk ),( Venus M. K. Chan ) 한국언어정보학회 1995 국제 워크샵 Vol.1995 No.-
This paper investigates some of the distributional properties of word definitions in a machine?readable dictionary which was obtained from the Oxford Text Archive. Three types of dis?tributions were examined: (1) frequency-ranked distribution of words in the definitions, (2) the length distribution of word definitions and (3) the frequency distribution of the number of unique tags of an entry. In addition, the coverage characteristics of headwords over word defi?nitions are also explored. A rough-and-ready comparison of distributional properties between tokens and their morphologically decomposed ones are made. Our result shows that morpho?logical dcomposition does not change the length distribution of the word definitions nor the ranked-frequency distribution of words, significantly. However, it increases the coverage of word definitions dramatically compared with no decompositions. Furthermore, the frequency distribution of the number of unique tags per entry is approximately linear when the data is suitablely scaled (i.e. linear or logarithmic).
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Word-Sense Classification by Hierarchial Clustering
( Ken Y K Lau ),( Robert W P Luk ) 한국언어정보학회 1998 국제 워크샵 Vol.1998 No.-
This paper investigates the use of clustering techniques in word-sense classification, which identifies different contexts that a word was used with the same or similar sense. For simplicity, we have used the hierarchical clustering techniques: single- and complete-linkage, and we showed that the latter is a more suitable technique from our performance measurements (i.e. recall and precision) compared with manually grouping different contexts of similar meaning. We found that the use of part-of-speech tags and fixed-length context has better clustering performance than without part-of-speech tags and sentence context, respectively. The differences between manually identified groups of different contexts are measured in terms of recall and precision at about 80%, which are not v e v different from the average recall and precision performance of complete-linkage clustering at 80% and 75%, respectively.
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