Terminology-based Text Embedding for Computing Document Similarities on Technical Content

Hamid Mirisaee, Eric Gaussier, Cedric Lagnier, Agnes Guerraz


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.
Anthology ID:
2019.jeptalnrecital-tia.3
Volume:
Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Terminologie et Intelligence Artificielle (atelier TALN-RECITAL \& IC)
Month:
7
Year:
2019
Address:
Toulouse, France
Venue:
JEP/TALN/RECITAL
SIG:
Publisher:
ATALA
Note:
Pages:
31–42
Language:
URL:
https://aclanthology.org/2019.jeptalnrecital-tia.3
DOI:
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2019.jeptalnrecital-tia.3.pdf