Semantic embedding for information retrieval

Shenghui Wang, Rob Koopman

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

11 Citations (Scopus)
292 Downloads (Pure)


Capturing semantics in a computable way is desirable for many applications, such as information retrieval, document clustering or classification, etc. Embedding words or documents in a vector space is a common first-step. Different types of embedding techniques have their own characteristics which makes it difficult to choose one for an application. In this paper, we compared a few off-the-shelf word and document embedding methods with our own Ariadne approach in different evaluation tests. We argue that one needs to take into account the specific requirements from the applications to decide which embedding method is more suitable. Also, in order to achieve better retrieval performance, it is worth investigating the combination of bibliometric measures with semantic embedding to improve ranking.

Original languageEnglish
Title of host publicationBIR 2017
Subtitle of host publication5th Workshop on Bibliometric-enhanced Information Retrieval 2017
EditorsPhilipp Mayr, Ingo Frommholz, Guillaume Cabanac
Number of pages11
Publication statusPublished - 2017
Externally publishedYes
Event5th Workshop on Bibliometric-Enhanced Information Retrieval, BIR 2017 - Aberdeen, United Kingdom
Duration: 9 Apr 20179 Apr 2017
Conference number: 5

Publication series

NameCEUR workshop proceedings
PublisherRheinisch Westfälische Technische Hochschule
ISSN (Print)1613-0073


Conference5th Workshop on Bibliometric-Enhanced Information Retrieval, BIR 2017
Abbreviated titleBIR
Country/TerritoryUnited Kingdom


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