Shortest-Path Graph Kernels for Document Similarity
Giannis Nikolentzos, Polykarpos Meladianos, François Rousseau, Yannis Stavrakas, Michalis Vazirgiannis
Abstract
In this paper, we present a novel document similarity measure based on the definition of a graph kernel between pairs of documents. The proposed measure takes into account both the terms contained in the documents and the relationships between them. By representing each document as a graph-of-words, we are able to model these relationships and then determine how similar two documents are by using a modified shortest-path graph kernel. We evaluate our approach on two tasks and compare it against several baseline approaches using various performance metrics such as DET curves and macro-average F1-score. Experimental results on a range of datasets showed that our proposed approach outperforms traditional techniques and is capable of measuring more accurately the similarity between two documents.- Anthology ID:
- D17-1202
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1890–1900
- Language:
- URL:
- https://aclanthology.org/D17-1202
- DOI:
- 10.18653/v1/D17-1202
- Cite (ACL):
- Giannis Nikolentzos, Polykarpos Meladianos, François Rousseau, Yannis Stavrakas, and Michalis Vazirgiannis. 2017. Shortest-Path Graph Kernels for Document Similarity. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1890–1900, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Shortest-Path Graph Kernels for Document Similarity (Nikolentzos et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/D17-1202.pdf
- Data
- WebKB