Reference:

Tiina Lindh-Knuutila and Timo Honkela. Exploratory text analysis: Data-driven versus human semantic similarity judgments. In M. Tomassini, A. Antonioni, F. Daolio, and P. Buesser, editors, Adaptive and Natural Computing Algorithms, Proceedings of ICANNGA 2013, pages 428–437, 2013.

Abstract:

We present an approach for comparing human-made and automatically generated semantic representations with an assumption that neither of these has a primary status over the other. In the experimental part, we compare the results gained by using independent component analysis and the self-organizing map algorithm on word context analysis with a semantically labeled dictionary called BLESS. The data-driven methods are useful in assessing the quality of the hand-created semantic resources and these resources can be used to evaluate the outcome of the automated process. We present a number of specific findings that go beyond typical quantitative evaluations of the results of data-driven methods in which the manually created resources are usually taken as a gold standard.

Suggested BibTeX entry:

@inproceedings{LindhKnuutilaHonkelaICANNGA13,
    author = {Tiina Lindh-Knuutila and Timo Honkela},
    booktitle = {Adaptive and Natural Computing Algorithms, Proceedings of ICANNGA 2013},
    editor = {Tomassini, M. and Antonioni, A. and Daolio, F. and Buesser, P.},
    language = {en},
    pages = {428-437},
    title = {Exploratory Text Analysis: Data-Driven versus Human Semantic Similarity Judgments},
    year = {2013},
}