Extracting entities and their relations from text is an important task
for understanding massive text corpora. Open information extraction
(IE) systems mine relation tuples (i.e., entity arguments and a
predicate string to describe their relation) from sentences. However,
current open IE systems ignore the fact that global statistics in a
large corpus can be collectively leveraged to identify high-quality
sentence-level extractions. In this paper, we propose a novel open
IE system, called ReMine, which integrates local context signal
and global structural signal in a unified framework with distant
supervision. The new system can be efficiently applied to different
domains as it uses facts from external knowledge bases as supervision;
and can effectively score sentence-level tuple extractions
based on corpus-level statistics. Specifically, we design a joint optimization
problem to unify (1) segmenting entity/relation phrases
in individual sentences based on local context; and (2) measuring
the quality of sentence-level extractions with a translating-based
objective. Experiments on real-world corpora from different domains
demonstrate the effectiveness and robustness of ReMine
when compared to other open IE systems.
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Automatic Synonym Discovery with Knowledge Bases
Recognizing entity synonyms from text has become a crucial task in many entity-leveraging applications. However, discovering entity synonyms from domain-specific text corpora (e.g., news articles, scientific papers) is rather challenging. Current systems take an entity name string as input to find out other names that are synonymous, ignoring the fact that often times a name string can refer to multiple entities (e.g., “apple” could refer to both Apple Inc and the fruit apple). Moreover, most existing methods require training
data manually created by domain experts to construct supervised learning systems. In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus.
The manually-curated synonyms for each entity stored in a knowledge base not only form a set of name strings to disambiguate the meaning for each other, but also can serve as “distant” supervision to help determine important features for the task. We propose a novel framework, called DPE, to integrate two kinds of mutually complementing signals for synonym discovery, i.e., distributional features based on corpus-level statistics and textual patterns based on local contexts. In particular, DPE jointly optimizes the two kinds of signals in conjunction with distant supervision, so that they can mutually enhance each other in the training stage. At the inference stage, both signals will be utilized to discover synonyms for the given entities. Experimental results prove the effectiveness of the
proposed framework.
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- NSF-PAR ID:
- 10059911
- Date Published:
- Journal Name:
- Proceedings of the 23rd {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining
- Volume:
- 23
- Issue:
- 1
- Page Range / eLocation ID:
- 997 to 1005
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Extracting entities and their relations from text is an important task for understanding massive text corpora. Open information extraction (IE) systems mine relation tuples (i.e., entity arguments and a predicate string to describe their relation) from sentences. However, current open IE systems ignore the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions. In this paper, we propose a novel open IE system, called ReMine, which integrates local context signal and global structural signal in a unified framework with distant supervision. The new system can be efficiently applied to different domains as it uses facts from external knowledge bases as supervision; and can effectively score sentence-level tuple extractions based on corpus-level statistics. Specifically, we design a joint optimization problem to unify (1) segmenting entity/relation phrases in individual sentences based on local context; and (2) measuring the quality of sentence-level extractions with a translating-based objective. Experiments on real-world corpora from different domains demonstrate the effectiveness and robustness of ReMine when compared to other open IE systems.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3097983.3098185
