Global Relation Embedding for Relation Extraction
| Autor: | Izzeddin Gur, Semih Yavuz, Honglei Liu, Huan Sun, Yu Su, Xifeng Yan |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Relation (database) Computer science business.industry 02 engineering and technology computer.software_genre Relationship extraction Knowledge base 020204 information systems 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence Noise (video) business computer Computation and Language (cs.CL) Natural language processing |
| Zdroj: | NAACL-HLT |
| Popis: | We study the problem of textual relation embedding with distant supervision. To combat the wrong labeling problem of distant supervision, we propose to embed textual relations with global statistics of relations, i.e., the co-occurrence statistics of textual and knowledge base relations collected from the entire corpus. This approach turns out to be more robust to the training noise introduced by distant supervision. On a popular relation extraction dataset, we show that the learned textual relation embedding can be used to augment existing relation extraction models and significantly improve their performance. Most remarkably, for the top 1,000 relational facts discovered by the best existing model, the precision can be improved from 83.9% to 89.3%. Accepted to NAACL HLT 2018 |
| Databáze: | OpenAIRE |
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