Name
Title | ||
|---|---|---|
Sense Extraction and Disambiguation for Chinese Words from Bilingual Terminology Bank |
Abstract | ||
|---|---|---|
Using lexical semantic knowledge to solve natural language processing problems has been getting popular in recent years. Because semantic processing relies heavily on lexical semantic knowledge, the construction of lexical semantic databases has become urgent. WordNet is the most famous English semantic knowledge database at present; many researches of word sense disambiguation adopt it as a standard. Because of the success of WordNet, there is a trend to construct WordNet in different languages. In this paper, we propose a methodology for constructing Chinese WordNet by extracting information from a bilingual terminology bank. We developed an algorithm of word-to-word alignment to extract the English-Chinese translation-equivalent word pairs first. Then, the algorithm disambiguates word senses and maps Chinese word senses to WordNet synsets to achieve the goal. In the word-to-word alignment experiment, this alignment algorithm achieves the f-score of 98.4%. In the word sense disambiguation experiment, the extracted senses cover 36.89% of WordNet synsets and the accuracy of the three proposed disambiguation rules achieve the accuracies of 80%, 83% and 87%, respectively. |
Year | Venue | Keywords |
|---|---|---|
2006 | IJCLCLP | word sense disambiguation,sense tagging.,wordnet,em algorithm,word alignment,lexical semantics,semantic processing,natural language processing |
Field | DocType | Volume |
Semantic memory,Normalized Google distance,SemEval,Chen,Terminology,Computer science,eXtended WordNet,Natural language processing,Artificial intelligence,WordNet,Word-sense disambiguation | Journal | 11 |
Issue | Citations | PageRank |
3 | 1 | 0.37 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming-Hong Bai | 1 | 88 | 10.36 |
| Keh-Jiann Chen | 2 | 761 | 131.86 |
| Jason S. Chang | 3 | 345 | 62.64 |