Journal
ELECTRONICS
Volume 10, Issue 23, Pages -Publisher
MDPI
DOI: 10.3390/electronics10232938
Keywords
word sense disambiguation; Korean WordNet; knowledge-based model; data mining; information extraction
Categories
Funding
- Institute of Information & communications Technology Planning & Evaluation (IITP) - Korea government(MSIT) [2020-0-01450]
Ask authors/readers for more resources
This study proposes an unsupervised disambiguation method based on the Korean WordNet, which outperforms supervised disambiguation methods by calculating the chi(2) statistic between related words to resolve the data deficiency problem.
Supervised disambiguation using a large amount of corpus data delivers better performance than other word sense disambiguation methods. However, it is not easy to construct large-scale, sense-tagged corpora since this requires high cost and time. On the other hand, implementing unsupervised disambiguation is relatively easy, although most of the efforts have not been satisfactory. A primary reason for the performance degradation of unsupervised disambiguation is that the semantic occurrence probability of ambiguous words is not available. Hence, a data deficiency problem occurs while determining the dependency between words. This paper proposes an unsupervised disambiguation method using a prior probability estimation based on the Korean WordNet. This performs better than supervised disambiguation. In the Korean WordNet, all the words have similar semantic characteristics to their related words. Thus, it is assumed that the dependency between words is the same as the dependency between their related words. This resolves the data deficiency problem by determining the dependency between words by calculating the chi(2) statistic between related words. Moreover, in order to have the same effect as using the semantic occurrence probability as prior probability, which is used in supervised disambiguation, semantically related words of ambiguous vocabulary are obtained and utilized as prior probability data. An experiment was conducted with Korean, English, and Chinese to evaluate the performance of our proposed lexical disambiguation method. We found that our proposed method had better performance than supervised disambiguation methods even though our method is based on unsupervised disambiguation (using a knowledge-based approach).
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available