Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 601–612, Valencia, Spain, April 3-7, 2017. c⃝2017 Association for Computational Linguistics Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge Bonan Min and Marjorie Freedman and Talya Meltzer ∗ Raytheon BBN Technologies 10 Moulton St, Cambridge, MA 02138, USA {bonan.min, marjorie.freedman}@raytheon.com Abstract Building knowledge bases (KB) automat- ically from text corpora is crucial for many applications such as question an- swering 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, com- bining these components has shown to be under-constrained and often produces KBs with supersize entities and common-sense errors in relations (a person has multi- ple birthdates). The errors are difficult to resolve solely with IE tools but be- come obvious with world knowledge at the corpus level. By analyzing Freebase and a large text collection, we found that per-relation cardinality and the popular- ity of entities follow the power-law dis- tribution favoring flat long tails with low- frequency instances. We present a proba- bilistic joint inference algorithm to incor- porate 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 re- spectively. ∗The third author is currently with The Affinity project. This work was done while she was at BBN. This work was supported by DARPA/I2O Contract No. FA8750-13-C-0008 under the DEFT program. The views, opinions, and/or findings contained in this article are those of the author and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the De- partment of Defense. This document does not contain tech- nology or technical data controlled under either the U.S. In- ternational Traffic in Arms Regulations or the U.S. Export Administration Regulations. 1 Introduction Automatically transforming a large corpus into a structured knowledge base (KB) has long been a goal of information extraction (IE) research. KB population incorporates many IE tasks including named entity recognition, entity linking and rela- tion extraction, each of which rely on deeper lin- guistic analysis, e.g., syntactic parsing or anaphora resolution. Since 2012, NIST 1 has run an open shared task in KB population (KBP) under TAC 2. Most participating systems (Mayfield et al., 2014; Min et al., 2015; Roth et al., 2015; Angeli et al., 2014; Nguyen et al., 2014; Monahan and Carpen- ter, 2012) combine many independent components to perform the full task. As will be familiar to most IE researchers, the individual components are not perfect. When combined into a pipeline, errors compound. We found that a KB produced with a simple combina- tion of state-of-the-art IE components (Ramshaw et al., 2011) is very sensitive to component-level errors (Grishman, 2013). Table 1 illustrates …

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