Name
Abstract | ||
|---|---|---|
Entity disambiguation, also known as entity linking, is the task of mapping mentions in text to the corresponding entities in a given knowledge base, e.g. Wikipedia. Two key challenges are making use of mention's context to disambiguate (i.e. local objective), and promoting coherence of all the linked entities (i.e. global objective). In this paper, we propose a deep neural network model to effectively measure the semantic matching between mention's context and target entity. We are the first to employ the long short-term memory (LSTM) and attention mechanism for entity disambiguation. We also propose Pair-Linking, a simple but effective and significantly fast linking algorithm. Pair-Linking iteratively identifies and resolves pairs of mentions, starting from the most confident pair. It finishes linking all mentions in a document by scanning the pairs of mentions at most once. Our neural network model combined with Pair-Linking, named NeuPL, outperforms state-of-the-art systems over different types of documents including news, RSS, and tweets.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3132847.3132963 | CIKM |
Keywords | Field | DocType |
Entity Disambiguation, Semantic Matching, Pair-Linking | Entity linking,Information retrieval,Computer science,Coherence (physics),Natural language processing,Artificial intelligence,Knowledge base,Artificial neural network,RSS,Semantic matching | Conference |
ISBN | Citations | PageRank |
978-1-4503-4918-5 | 11 | 0.61 |
References | Authors | |
33 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minh Phan | 1 | 48 | 4.37 |
| Aixin Sun | 2 | 3071 | 156.89 |
| Yi Tay | 3 | 229 | 28.97 |
| Jialong Han | 4 | 97 | 8.65 |
| Chenliang Li | 5 | 590 | 39.20 |