Evaluating text coherence based on semantic similarity graph

Jan Wira Gotama Putra, Takenobu Tokunaga


Abstract
Coherence is a crucial feature of text because it is indispensable for conveying its communication purpose and meaning to its readers. In this paper, we propose an unsupervised text coherence scoring based on graph construction in which edges are established between semantically similar sentences represented by vertices. The sentence similarity is calculated based on the cosine similarity of semantic vectors representing sentences. We provide three graph construction methods establishing an edge from a given vertex to a preceding adjacent vertex, to a single similar vertex, or to multiple similar vertices. We evaluated our methods in the document discrimination task and the insertion task by comparing our proposed methods to the supervised (Entity Grid) and unsupervised (Entity Graph) baselines. In the document discrimination task, our method outperformed the unsupervised baseline but could not do the supervised baseline, while in the insertion task, our method outperformed both baselines.
Anthology ID:
W17-2410
Volume:
Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venues:
TextGraphs | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
76–85
Language:
URL:
https://www.aclweb.org/anthology/W17-2410
DOI:
10.18653/v1/W17-2410
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2410.pdf
Presentation:
 W17-2410.Presentation.pdf