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Uppsala University and Gavagai at CLEF Erisk: Comparing word embedding models
KTH, School of Electrical Engineering and Computer Science (EECS), Theoretical Computer Science, TCS.ORCID iD: 0000-0003-4042-4919
2019 (English)In: CEUR Workshop Proceedings, CEUR-WS , 2019, Vol. 2380Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes an experiment to evaluate the performance of three different types of semantic vectors or word embeddings-random indexing, GloVe, and ELMo-and two different classification architectures-linear regression and multi-layer perceptrons-for the specific task of identifying authors with eating disorders from writings they publish on a discussion forum. The task requires the classifier to process texts written by the authors in the sequence they were published, and to identify authors likely to be at risk of suffering from eating disorders as early as possible. The data are part of the eRISK evaluation task of CLEF 2019 and evaluated according to the eRISK metrics. Contrary to our expectations, we did not observe a clear-cut advantage using the recently popular contextualized ELMo vectors over the commonly used and much more light-weight GloVe vectors, or the more handily learnable random indexing vectors.

Place, publisher, year, edition, pages
CEUR-WS , 2019. Vol. 2380
Keywords [en]
Author classification, Semantic vectors, Word embeddings
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-257945Scopus ID: 2-s2.0-85070497793OAI: oai:DiVA.org:kth-257945DiVA, id: diva2:1349538
Conference
20th Working Notes of CLEF Conference and Labs of the Evaluation Forum, CLEF 2019, 9 September 2019 through 12 September 2019
Note

QC 20190909

Available from: 2019-09-09 Created: 2019-09-09 Last updated: 2019-09-09Bibliographically approved

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