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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

SeRI: A Dataset for Sub-event Relation Inference from an Encyclopedia

verfasst von : Tao Ge, Lei Cui, Baobao Chang, Zhifang Sui, Furu Wei, Ming Zhou

Erschienen in: Natural Language Processing and Chinese Computing

Verlag: Springer International Publishing

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Abstract

Mining sub-event relations of major events is an important research problem, which is useful for building event taxonomy, event knowledge base construction, and natural language understanding. To advance the study of this problem, this paper presents a novel dataset called SeRI (Sub-event Relation Inference). SeRI includes 3,917 event articles from English Wikipedia and the annotations of their sub-events. It can be used for training or evaluating a model that mines sub-event relation from encyclopedia-style texts. Based on this dataset, we formally define the task of sub-event relation inference from an encyclopedia, propose an experimental setting and evaluation metrics and evaluate some baseline approaches’ performance on this dataset.

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Vorheriges Kapitel Cross-Scenario Inference Based Event-Event Relation Detection
Nächstes Kapitel Densely Connected Bidirectional LSTM with Applications to Sentence Classification
Fußnoten
1
Event articles refer to the articles that describe a major event in Wikipedia, like Fig. 1.
 
2
Please contact the first author to request the access to the dataset.
 
3
To distinguish from a pair which is denoted as \(\langle e_i,e_j \rangle \) for which we do not consider the order of items, we say a pair instance denoted as (\(e_i\),\(e_j\))) when we consider the order of items in a pair.
 
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Metadaten
Titel
SeRI: A Dataset for Sub-event Relation Inference from an Encyclopedia
verfasst von
Tao Ge
Lei Cui
Baobao Chang
Zhifang Sui
Furu Wei
Ming Zhou
Copyright-Jahr
2018
Buch
Natural Language Processing and Chinese Computing

Print ISBN: 978-3-319-99500-7

Electronic ISBN: 978-3-319-99501-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-99501-4_23

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