Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge
| Autor: | Marjorie Freedman, Talya Meltzer, Bonan Min |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
education.field_of_study
Computer science business.industry 010102 general mathematics Population Inference 010501 environmental sciences computer.software_genre Machine learning 01 natural sciences Relationship extraction Entity linking Cold start Named-entity recognition Knowledge base Question answering Artificial intelligence 0101 mathematics education business computer Natural language processing 0105 earth and related environmental sciences |
| Zdroj: | EACL (1) |
| DOI: | 10.18653/v1/e17-1057 |
| Popis: | Building knowledge bases (KB) automatically from text corpora is crucial for many applications such as question answering and web search. The problem is very challenging and has been divided into sub-problems such as mention and named entity recognition, entity linking and relation extraction. However, combining these components has shown to be under-constrained and often produces KBs with supersize entities and common-sense errors in relations (a person has multiple birthdates). The errors are difficult to resolve solely with IE tools but become obvious with world knowledge at the corpus level. By analyzing Freebase and a large text collection, we found that per-relation cardinality and the popularity of entities follow the power-law distribution favoring flat long tails with low-frequency instances. We present a probabilistic joint inference algorithm to incorporate this world knowledge during KB construction. Our approach yields state-of-the-art performance on the TAC Cold Start task, and 42% and 19.4% relative improvements in F1 over our baseline on Cold Start hop-1 and all-hop queries respectively. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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