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Jointly Modeling Intent Identification and Slot Filling with Contextual and Hierarchical Information Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Unsupervised Slot Filler Refinement via Entity Community Construction

Authors : Zengzhuang Xu, Rui Song, Bowei Zou, Yu Hong

Published in: Natural Language Processing and Chinese Computing

Publisher: Springer International Publishing

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Abstract

Given an entity (query), slot filling aims to find and extract the values (slot fillers) of its specific attributes (slot types) from a large-scale of document collections. Most existing work of slot filling models slot fillers separately and only considers direct relations between slot fillers and query, ignoring other slot fillers in context. In this paper we propose an unsupervised slot filler refinement approach via entity community construction to filter out the incorrect fillers collaboratively. The community-based framework mainly consists of (1) filler community generated by a point-wise mutual information-based hierarchical clustering, and (2) query community constructed by a co-occurrence graph model.

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Footnotes
1
queryID: SF13_ENG_038, in KBP 2013 ESF data set.
 
3
per:{cause_of_death, date_of_birth, date_of_death, age, charges} and
org:{date_founded, date_dissolved, number_of_employees_members, website}.
 
5
With slot type per:parents.
 
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Metadata
Title
Unsupervised Slot Filler Refinement via Entity Community Construction
Authors
Zengzhuang Xu
Rui Song
Bowei Zou
Yu Hong
Copyright Year
2018
Book
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-319-73617-4

Electronic ISBN: 978-3-319-73618-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-73618-1_54

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