@inproceedings{zhou-etal-2020-probabilistic,
title = "A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction",
author = "Zhou, Yang and
Zhao, Tong and
Jiang, Meng",
editor = "Christodoulopoulos, Christos and
Thorne, James and
Vlachos, Andreas and
Cocarascu, Oana and
Mittal, Arpit",
booktitle = "Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.fever-1.3",
doi = "10.18653/v1/2020.fever-1.3",
pages = "18--25",
abstract = "Textual patterns (e.g., Country{'}s president Person) are specified and/or generated for extracting factual information from unstructured data. Pattern-based information extraction methods have been recognized for their efficiency and transferability. However, not every pattern is reliable: A major challenge is to derive the most complete and accurate facts from diverse and sometimes conflicting extractions. In this work, we propose a probabilistic graphical model which formulates fact extraction in a generative process. It automatically infers true facts and pattern reliability without any supervision. It has two novel designs specially for temporal facts: (1) it models pattern reliability on two types of time signals, including temporal tag in text and text generation time; (2) it models commonsense constraints as observable variables. Experimental results demonstrate that our model significantly outperforms existing methods on extracting true temporal facts from news data.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhou-etal-2020-probabilistic">
<titleInfo>
<title>A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yang</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tong</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Meng</namePart>
<namePart type="family">Jiang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2020-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Christos</namePart>
<namePart type="family">Christodoulopoulos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">James</namePart>
<namePart type="family">Thorne</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andreas</namePart>
<namePart type="family">Vlachos</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Oana</namePart>
<namePart type="family">Cocarascu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Arpit</namePart>
<namePart type="family">Mittal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Online</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Textual patterns (e.g., Country’s president Person) are specified and/or generated for extracting factual information from unstructured data. Pattern-based information extraction methods have been recognized for their efficiency and transferability. However, not every pattern is reliable: A major challenge is to derive the most complete and accurate facts from diverse and sometimes conflicting extractions. In this work, we propose a probabilistic graphical model which formulates fact extraction in a generative process. It automatically infers true facts and pattern reliability without any supervision. It has two novel designs specially for temporal facts: (1) it models pattern reliability on two types of time signals, including temporal tag in text and text generation time; (2) it models commonsense constraints as observable variables. Experimental results demonstrate that our model significantly outperforms existing methods on extracting true temporal facts from news data.</abstract>
<identifier type="citekey">zhou-etal-2020-probabilistic</identifier>
<identifier type="doi">10.18653/v1/2020.fever-1.3</identifier>
<location>
<url>https://aclanthology.org/2020.fever-1.3</url>
</location>
<part>
<date>2020-07</date>
<extent unit="page">
<start>18</start>
<end>25</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction
%A Zhou, Yang
%A Zhao, Tong
%A Jiang, Meng
%Y Christodoulopoulos, Christos
%Y Thorne, James
%Y Vlachos, Andreas
%Y Cocarascu, Oana
%Y Mittal, Arpit
%S Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhou-etal-2020-probabilistic
%X Textual patterns (e.g., Country’s president Person) are specified and/or generated for extracting factual information from unstructured data. Pattern-based information extraction methods have been recognized for their efficiency and transferability. However, not every pattern is reliable: A major challenge is to derive the most complete and accurate facts from diverse and sometimes conflicting extractions. In this work, we propose a probabilistic graphical model which formulates fact extraction in a generative process. It automatically infers true facts and pattern reliability without any supervision. It has two novel designs specially for temporal facts: (1) it models pattern reliability on two types of time signals, including temporal tag in text and text generation time; (2) it models commonsense constraints as observable variables. Experimental results demonstrate that our model significantly outperforms existing methods on extracting true temporal facts from news data.
%R 10.18653/v1/2020.fever-1.3
%U https://aclanthology.org/2020.fever-1.3
%U https://doi.org/10.18653/v1/2020.fever-1.3
%P 18-25
Markdown (Informal)
[A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction](https://aclanthology.org/2020.fever-1.3) (Zhou et al., FEVER 2020)
ACL