Positioning for Conceptual Development using Latent Semantic Analysis

Fridolin Wild, Bernhard Hoisl, Gaston Burek

Publikation: Beitrag in Buch/KonferenzbandBeitrag in Konferenzband


With increasing opportunities to learn online, the problem of positioning learners in an educational network of content offers new possibilities for the utilisation of geometry-based natural language processing techniques.
In this article, the adoption of latent semantic analysis (LSA) for guiding learners in their conceptual development is investigated. We propose five new algorithmic derivations of LSA and test their validity for positioning in an experiment in order to draw back conclusions on the suitability of machine learning from previously accredited evidence. Special attention is thereby directed towards the role of distractors and the calculation of thresholds when using similarities as a proxy for assessing conceptual closeness.
Results indicate that learning improves positioning. Distractors are of low value and seem to be replaceable by generic noise to improve threshold calculation. Furthermore, new ways to flexibly calculate thresholds could be identified.
Titel des SammelwerksProceedings of the EACL Workshop on GEMS: Geometrical Models of Natural Language Semantics
Herausgeber*innen R. Basili and M. Pennacchiotti
VerlagAssociation for Computational Linguistics
Seiten41 - 48
PublikationsstatusVeröffentlicht - 1 Sept. 2009

Österreichische Systematik der Wissenschaftszweige (ÖFOS)

  • 102
  • 101029 Mathematische Statistik
  • 102009 Computersimulation
  • 602011 Computerlinguistik