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A machine learning filter for the slot filling task

Kevin Lange Di Cesare, Amal Zouaq, Michel Gagnon and Ludovic Jean-Louis

Article (2018)

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Cite this document: Lange Di Cesare, K., Zouaq, A., Gagnon, M. & Jean-Louis, L. (2018). A machine learning filter for the slot filling task. Information, 9(6). doi:10.3390/info9060133
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Abstract

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.

Uncontrolled Keywords

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

Open Access document in PolyPublie
Subjects: 2700 Technologie de l'information > 2700 Technologie de l'information
2800 Intelligence artificielle > 2800 Intelligence artificielle (Vision artificielle, voir 2603)
2800 Intelligence artificielle > 2803 Représentation des connaissances
Department: Département de génie informatique et génie logiciel
Research Center: Non applicable
Funders: FRQNT
Date Deposited: 09 Mar 2020 11:21
Last Modified: 10 Mar 2020 01:20
PolyPublie URL: https://publications.polymtl.ca/3565/
Document issued by the official publisher
Journal Title: Information (vol. 9, no. 6)
Publisher: MDPI
Official URL: https://doi.org/10.3390/info9060133

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