Abstract
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.
Cite
CITATION STYLE
Muis, A. O., & Lu, W. (2017). Labeling gaps between words: Recognizing overlapping mentions with mention separators. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 2608–2618). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d17-1276
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