Using word embeddings to enhance keyword identification for scientific publications

Research output: Chapter in Book/Conference paperConference paper

14 Citations (Scopus)

Abstract

© Springer International Publishing Switzerland 2015. Automatic keyword identification is a desirable but difficult task. It requires considerations of not only the extraction of important words or phrases from a text, but also the generation of abstractive ones that do not appear in the text. In this paper, we propose an approach that uses word embedding vectors as an external knowledge base for both keyword extraction and generation. Our evaluation shows that our approach outperforms many baseline algorithms, and is comparable to the state-of-the-art algorithm on our chosen dataset. In addition, we also introduce a new approach for evaluating the task of keyword extraction, that overcomes a common problem of overly strict matching criteria. We show that using word embedding vectors is a simpler, yet effective, method for both keyword extraction and generation.
Original languageEnglish
Title of host publicationDatabases Theory and Applications
PublisherSpringer
Pages257-268
Volume9093
ISBN (Print)9783319195476
DOIs
Publication statusPublished - 2015
Event26th Australasian Database Conference - Melbourne, Australia
Duration: 4 Jun 20157 Jun 2015

Conference

Conference26th Australasian Database Conference
CountryAustralia
CityMelbourne
Period4/06/157/06/15

Cite this