Name
Abstract | ||
|---|---|---|
In this paper, we investigate a problem existing in Japanese word sense disambiguation (WSD) through a HiraganaKanji conversion task. In choosing words to consider as features, we propose a method that employs word embeddings and pointwise mutual information and evaluate the proposed method. The experimental results suggest that our method is more effective than other methods using word embeddings. We also compare the accuracy when changing the amount of training data. We find that the difference in accuracy between the methods becomes small when a very large amount of training data is used. We have confirmed that the method of improving accuracy while using fewer training data is important in WSD because the number of sentences required to obtain high accuracy increases exponentially. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/IALP.2017.8300557 | 2017 International Conference on Asian Language Processing (IALP) |
Keywords | Field | DocType |
word sense disambiguation,lexical substitution | Training set,Computer science,Artificial intelligence,Natural language processing,Pointwise mutual information,Word-sense disambiguation | Conference |
ISSN | ISBN | Citations |
2159-1962 | 978-1-5386-1982-7 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuki Gumizawa | 1 | 0 | 0.34 |
| Kazuhide Yamamoto | 2 | 207 | 39.66 |