Local Word Embeddings for Query Expansion based on Co-Authorship and Citations

André Rattinger, Jean-Marie Le Goff, Christian Gütl

Research output: Contribution to conferencePaper

Abstract

Word embedding techniques have gained a lot of interest
from natural language processing researchers recently and they are valuable resource in identifying a list of semantically related terms for a
search query. These related terms build a natural addition for query expansion, but might mismatch when the application domains use different
jargon. Using the Skip-Gram algorithm of Word2Vec, terms are selected
only from a specific subset of the corpus, which is extended by documents
from co-authorship and citations. We demonstrate that locally-trained
word embeddings with this extension provides a valuable augmentation
and can improve retrieval performance. First result suggest that query
expansion and word embeddings could also benefit from other related
information.
Original languageEnglish
Pages46
Number of pages53
Publication statusPublished - 26 Mar 2018
EventBibliometric-enhanced Information Retrieval - Frenoble, France
Duration: 26 Mar 201826 Mar 2018
Conference number: 7
https://www.gesis.org/en/services/events/events-archive/conferences/ecir-workshops/ecir-workshop-2018/

Workshop

WorkshopBibliometric-enhanced Information Retrieval
Abbreviated titleBIR 2018
CountryFrance
CityFrenoble
Period26/03/1826/03/18
Internet address

Fields of Expertise

  • Information, Communication & Computing

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