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Labeling Gaps Between Words: Recognizing Overlapping Mentions with Mention Separators

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In this paper, we propose a new model that is capable of recognizing overlapping mentions. We introduce a novel notion of mention separators that can be effectively used to capture how mentions overlap with one another. On top of a novel multigraph representation that we introduce, we show that efficient and exact inference can still be performed. We present some theoretical analysis on the differences between our model and a recently proposed model for recognizing overlapping mentions, and discuss the possible implications of the differences. Through extensive empirical analysis on standard datasets, we demonstrate the effectiveness of our approach.

Aldrian Obaja Muis, Wei Lu• 2018

Related benchmarks

TaskDatasetResultRank
Nested Named Entity RecognitionACE 2004 (test)
F1 Score64.5
166
Nested Named Entity RecognitionACE 2005 (test)
F1 Score63.1
153
Nested Named Entity RecognitionGENIA (test)
F1 Score70.8
140
Named Entity RecognitionCoNLL English 2003 (test)
F1 Score84.3
135
Entity extractionACE05 (test)
F1 Score63.1
53
Named Entity RecognitionACE05
F1 Score63.1
38
Named Entity RecognitionGENIA
F1 Score70.8
37
Nested Mention DetectionACE2005 (test)
F1 Score63.1
30
Overlapping Mention RecognitionGENIA (standard)
Precision75.4
18
Overlapping Mention RecognitionACE 2005 (standard)
Precision69.1
17
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