Published on Sat Jun 25 2016

Corpus-level Fine-grained Entity Typing Using Contextual Information

Yadollah Yaghoobzadeh, Hinrich Schütze

This paper addresses the problem of corpus-level entity typing. The application of entity typing we are interested in is knowledge base completion. We propose FIGMENT to tackle this problem.

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Abstract

This paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist". The application of entity typing we are interested in is knowledge base completion, specifically, to learn which classes an entity is a member of. We propose FIGMENT to tackle this problem. FIGMENT is embedding-based and combines (i) a global model that scores based on aggregated contextual information of an entity and (ii) a context model that first scores the individual occurrences of an entity and then aggregates the scores. In our evaluation, FIGMENT strongly outperforms an approach to entity typing that relies on relations obtained by an open information extraction system.

Mon Aug 07 2017
NLP
Corpus-level Fine-grained Entity Typing
This paper addresses the problem of corpus-level entity typing. The application of entity typing is to learn which classes an entity is a member of. We propose FIGMENT to tackle this problem.
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Artificial Intelligence
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Existing methods require pre-defining a set of types and training a multi-class classifier from a large labeled data set. They are thus limited to certain domains, genres and languages. We propose a novel unsupervised entity typing framework by combining symbolic and distributional semantics.
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Previous attempts to incorporate hierarchical structure have yielded little benefit and are restricted to shallow ontologies. This paper presents new methods using real and complex bilinear mappings.
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Wed Oct 24 2018
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Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
MVET is a large multiview - and, in particular, multilingual - entity typing dataset. Mono- and multilingual fine-grained entity typing systems can be evaluated on this dataset.
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Mon Mar 29 2021
Machine Learning
Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web
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Sun Jul 07 2019
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The problem of entity-typing has been studied predominantly in supervised learning fashion. In this work we propose a zero-shot entity typing approach that requires no annotated data. Our system is shown to be competitive with state-of-the-art supervised NER systems.
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