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        <title>Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge</title>
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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge
%A Min, Bonan
%A Freedman, Marjorie
%A Meltzer, Talya
%Y Lapata, Mirella
%Y Blunsom, Phil
%Y Koller, Alexander
%S Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F min-etal-2017-probabilistic
%X 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.
%U https://aclanthology.org/E17-1057
%P 601-612

Markdown (Informal)

[Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge](https://aclanthology.org/E17-1057) (Min et al., EACL 2017)

• Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge (Min et al., EACL 2017)

ACL

• Bonan Min, Marjorie Freedman, and Talya Meltzer. 2017. Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 601–612, Valencia, Spain. Association for Computational Linguistics.