Abstract
Since named entities are often written in different ways, question answering (QA) and other language processing tasks stand to benefit from entity matching. We address the problem of finding equivalent person names in unstructured text. Our approach is a generalization of spelling correction: We compare to candidate matches by applying a set of edits to an input name. We introduce a novel unsupervised method for learning spelling edit probabilities which improves overall F-Measure on our own name-matching task by 12%. Relevance is demonstrated by application to the GALE Distillation task.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 467-470 |
| Number of pages | 4 |
| Journal | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
| State | Published - 2008 |
| Externally published | Yes |
| Event | INTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association - Brisbane, QLD, Australia Duration: Sep 22 2008 → Sep 26 2008 |
Keywords
- Entity matching
- Equivalent names
- Unsupervised learning
ASJC Scopus subject areas
- Human-Computer Interaction
- Signal Processing
- Software
- Sensory Systems