Name
Abstract | ||
|---|---|---|
Existing techniques for disambiguating named entities in text mostly focus on Wikipedia as a target catalog of entities. Yet for many types of entities, such as restaurants and cult movies, relational databases exist that contain far more extensive information than Wikipedia. This paper introduces a new task, called Open-Database Named-Entity Disambiguation (Open-DB NED), in which a system must be able to resolve named entities to symbols in an arbitrary database, without requiring labeled data for each new database. We introduce two techniques for Open-DB NED, one based on distant supervision and the other based on domain adaptation. In experiments on two domains, one with poor coverage by Wikipedia and the other with near-perfect coverage, our Open-DB NED strategies outperform a state-of-the-art Wikipedia NED system by over 25% in accuracy. |
Year | Venue | Keywords |
|---|---|---|
2012 | EMNLP-CoNLL | state-of-the-art wikipedia ned system,near-perfect coverage,new task,new database,open-db ned,arbitrary database,cult movie,open-database named-entity disambiguation,poor coverage,open-db ned strategy |
Field | DocType | Volume |
Relational database,Information retrieval,Computer science,Domain adaptation,Artificial intelligence,Natural language processing,Labeled data,Database | Conference | D12-1 |
Citations | PageRank | References |
15 | 0.72 | 29 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Avirup Sil | 1 | 131 | 13.85 |
| Ernest Cronin | 2 | 15 | 0.72 |
| Penghai Nie | 3 | 15 | 0.72 |
| Yinfei Yang | 4 | 99 | 16.53 |
| Ana-Maria Popescu | 5 | 2826 | 155.13 |
| Alexander Yates | 6 | 898 | 51.53 |