RT Conference Proceedings
T1 A Method to Generate Soft Reference Data for Topic Identification
A1 Vélez Serrano, Daniel
A1 Villarino, Guillermo
A1 Rodríguez González, Juan Tinguaro
A1 Gómez González, Daniel
AB Text mining and topic identification models are becoming increasingly relevant to extract value from the huge amount of unstructured textual information that companies obtain from their users and clients nowadays. Soft approaches to these problems are also gaining relevance, as in some contexts it may be unrealistic to assume that any document has to be associated to a single topic without any further consideration of the involved uncertainties. However, there is an almost total lack of reference documents allowing a proper assessment of the performance of soft classifiers in such soft topic identification tasks. To address this lack, in this paper a method is proposed that generates topic identification reference documents with a soft but objective nature, and which proceeds by combining, in random but known proportions, phrases of existing documents dealing with different topics. We also provide a computational study illustrating the application of the proposed method on a well-known benchmark for topic identification, as well as showing the possibility of carrying out an informative evaluation of soft classifiers in the context of soft topic identification.
SN 9783030501525
SN 9783030501532
SN 1865-0929
SN 1865-0937
YR 2020
FD 2020
LK https://hdl.handle.net/20.500.14352/115196
UL https://hdl.handle.net/20.500.14352/115196
LA eng
NO Vélez, D., Villarino, G., Rodríguez, J.T., Gómez, D. (2020). A Method to Generate Soft Reference Data for Topic Identification. In: Lesot, MJ., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020. Communications in Computer and Information Science, vol 1239. Springer, Cham. https://doi.org/10.1007/978-3-030-50153-2_5
NO Ministerio de Ciencia, Innovación y Universidades
NO Universidad Complutense de Madrid
DS Docta Complutense
RD 9 abr 2025