2014 (English)In: Proceedings of the 23d ACM international conference on Conference on information & knowledge management (CIKM '14), New York: Association for Computing Machinery (ACM), 2014, 1939-1942 p.Conference paper, Poster (Refereed)
A reasonable requirement (among many others) for a lexical or semantic component in an information system is that it should be able to learn incrementally from the linguistic data it is exposed to, that it can distinguish between the topical impact of various terms, and that it knows if it knows stuff or not.
We work with a specific representation framework – semantic spaces – which well accommodates the first requirement; in this short paper, we investigate the global qualities of semantic spaces by a topological procedure – mapper – which gives an indication of topical density of the space; we examine the local context of terms of interest in the semantic space using another topologically inspired approach which gives an indication of the neighbourhood of the terms of interest. Our aim is to be able to establish the qualities of the semantic space under consideration without resorting to inspection of the data used to build it.
Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2014. 1939-1942 p.
IdentifiersURN: urn:nbn:se:kth:diva-157332DOI: 10.1145/2661829.2662028ISBN: 78-1-4503-2598-1OAI: oai:DiVA.org:kth-157332DiVA: diva2:769715
CIKM’14, November 3–7, 2014, Shanghai, China
FunderSwedish Research Council
QC 201501222014-12-082014-12-082015-01-22Bibliographically approved