Name
Abstract | ||
|---|---|---|
In support of speech-driven question answering, we propose a method to construct N-gram language models for recognizing spoken questions with high accuracy. Question-answering sys- tems receive queries that often consist of two parts: one conveys the query topic and the other is a fixed phrase used in query sentences. A language model constructed by using a target col- lection of QA, for example, newspaper articles, can model the former part, but cannot model the latter part appropriately. We tackle this problem as task adaptation from language models ob- tained from background corpora (e.g., newspaper articles) to the fixed phrases, and propose a method that does not use the task- specific corpus, which is often difficult to obtain, but instead uses only manually listed fixed phrases. The method empha- sizes a subset of N-grams obtained from a background corpus that corresponds to fixed phrases specified by the list. Theoret- ically, this method can be regarded as maximizing a posteriori probability (MAP) estimation using the subset of the N-grams as a posteriori distribution. Some experiments show the effec- tiveness of our method. |
Year | Venue | Keywords |
|---|---|---|
2003 | INTERSPEECH | language model,col,question answering |
Field | DocType | Citations |
Computer science,A priori and a posteriori,Phrase,Task adaptation,Newspaper,A posteriori probability,Natural language processing,n-gram,Artificial intelligence,Language model,Question answering,Pattern recognition,Speech recognition | Conference | 3 |
PageRank | References | Authors |
0.50 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tomoyosi Akiba | 1 | 176 | 29.08 |
| Katunobu Itou | 2 | 319 | 44.36 |
| Atsushi Fujii | 3 | 486 | 59.25 |