Abstract
Online shoppers depend on customer reviews when evaluating products or services. However, in the international online marketplace, reviews in a user's language may not be available. Translation of online customer reviews is therefore an important service. A crucial aspect of this task is translating opinion words, key words that capture the reviewers' sentiments. This is challenging because opinion words often have multiple translations. We propose an unsupervised opinion word translation disambiguation scoring method using dependency distance and feature-opinion association as weighting factors. The scores of an opinion word's translation and its surrounding words' translations are estimated using Google snippets. We focus on Japanese-Chinese translation of hotel reviews from Rakutan Travel, using the 10 most common polysemous Japanese opinion words to evaluate system performance. Results show our weighting factors significantly improve translation accuracy compared to Google and Excite.
| Original language | English |
|---|---|
| Pages | 1503-1518 |
| Number of pages | 16 |
| State | Published - 2012 |
| Event | 24th International Conference on Computational Linguistics, COLING 2012 - Mumbai, India Duration: 8 Dec 2012 → 15 Dec 2012 |
Conference
| Conference | 24th International Conference on Computational Linguistics, COLING 2012 |
|---|---|
| Country/Territory | India |
| City | Mumbai |
| Period | 8/12/12 → 15/12/12 |
Keywords
- Dependency distance
- Feature-opinion association
- Opinion word translation disambiguation