Name
Abstract | ||
|---|---|---|
Generally inquiries through Web forms and e-mails are increasing. These inquiry texts usually include many informal expressions use of the colloquial style and many omitted words. An omitted word causes the meaning of a sentence to become ambiguous and makes the reader misread and misunderstand a context. In this paper we propose a method to predict omitted words from context and knowledge using topic information. From the results of evaluation experiment, we have confirmed that some of our methods can predict omitted words at the accuracy rate more than 40% for the expression that we used in the experiment. (C) 2013 The Authors. Published by Elsevier B.V. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.procs.2013.09.219 | 17TH INTERNATIONAL CONFERENCE IN KNOWLEDGE BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS - KES2013 |
Keywords | Field | DocType |
Colloquial expressions, Ellipsis, Statistical topic models, Gibbs sampling, LDA | Computer science,Natural language processing,Artificial intelligence,Topic model,Ellipse,Sentence,Gibbs sampling | Conference |
Volume | ISSN | Citations |
22 | 1877-0509 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomohiko Harada | 1 | 1 | 1.45 |
| Yoshikatsu Fujita | 2 | 14 | 9.36 |
| Kazuhiko Tsuda | 3 | 108 | 47.18 |