Terminology-based Text Embedding for Computing Document Similarities on Technical Content
Computation and Language
2019-07-02 v2 Machine Learning
Machine Learning
Abstract
We propose in this paper a new, hybrid document embedding approach in order to address the problem of document similarities with respect to the technical content. To do so, we employ a state-of-the-art graph techniques to first extract the keyphrases (composite keywords) of documents and, then, use them to score the sentences. Using the ranked sentences, we propose two approaches to embed documents and show their performances with respect to two baselines. With domain expert annotations, we illustrate that the proposed methods can find more relevant documents and outperform the baselines up to 27% in terms of NDCG.
Cite
@article{arxiv.1906.01874,
title = {Terminology-based Text Embedding for Computing Document Similarities on Technical Content},
author = {Hamid Mirisaee and Eric Gaussier and Cedric Lagnier and Agnes Guerraz},
journal= {arXiv preprint arXiv:1906.01874},
year = {2019}
}