Abstract
Named entity lists provide important features for language understanding, but typical lists can contain many ambiguous or incorrect phrases. We present an approach for automatically learning weighted entity lists by mining user clicks from web search logs. The approach significantly outperforms multiple baseline approaches and the weighted lists improve spoken language understanding tasks such as domain detection and slot filling. Our methods are general and can be easily applied to large quantities of entities, across any number of lists.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 705-708 |
| Number of pages | 4 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| State | Published - 2011 |
| Externally published | Yes |
| Event | 12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy Duration: Aug 27 2011 → Aug 31 2011 |
Keywords
- Click logs
- Domain detection
- Named entity lists
- Slot filling
- Spoken language understanding
ASJC Scopus subject areas
- Language and Linguistics
- Human-Computer Interaction
- Signal Processing
- Software
- Modeling and Simulation