A Machine Learning Filter for the Slot Filling Task

Information

Ludovic Jean-Louis¹  Kevin Lange Di Cesare²  Michel Gagnon²  Amal Zouaq³ 

[1] Netmail Inc., 180 Peel Street, Montreal, QC H3C 2G7, Canada;Polytechnique Montréal, Computer Engineering and Software Engineering, The WeST Lab, Montreal, QC H3T 1J4, Canada;School of Electrical Engineering and Computer Science, University of Ottawa, School of Electrical Engineering and Computer Science, The WeST Lab, Ottawa, ON K1N 6N5, Canada;

关键词: information retrieval;    information extraction;    relation extraction;    slot filling;    knowledge base population;    most frequent patterns;    precision;    data mining;   

DOI  :  10.3390/info9060133

来源: DOAJ

【 摘 要 】

Slot Filling, a subtask of Relation Extraction, represents a key aspect for building structured knowledge bases usable for semantic-based information retrieval. In this work, we present a machine learning filter whose aim is to enhance the precision of relation extractors while minimizing the impact on the recall. Our approach consists in the filtering of relation extractors’ output using a binary classifier. This classifier is based on a wide array of features including syntactic, semantic and statistical features such as the most frequent part-of-speech patterns or the syntactic dependencies between entities. We experimented the classifier on the 18 participating systems in the TAC KBP 2013 English Slot Filling track. The TAC KBP English Slot Filling track is an evaluation campaign that targets the extraction of 41 pre-identified relations (e.g., title, date of birth, countries of residence, etc.) related to specific named entities (persons and organizations). Our results show that the classifier is able to improve the global precision of the best 2013 system by 20.5% and improve the F1-score for 20 relations out of 33 considered.

【 授权许可】

Unknown