Abstract
Random Indexing is a recent technique for dimensionality reduction that allows to obtain a word space model from a set of contexts. This technique is less computationally expensive in comparison with others like LSI, Word2Vec or LDA. These characteristics turn it an attractive prospect to be used in an online learning environment. In this work, we compare several variants reported in the Random Indexing literature with the aim of using on the profile learning task. Experiments conducted in a subcollection of the dataset Reuter-21578 show that Random Indexing produces promising results, identifying some versions without actual advantage for the task at hand. Results obtained, by comparing Random Indexing with LDA, Word2Vec or LSI, also show that this technique is a viable alternative for representing documents.
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Acknowledgments
We thank the CPU Lab for the use of its facilities for running the experiments.
The first author was supported by Conacyt through scholarship 635046, and the second author was partially supported by SNI, México.
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Fonseca Bruzón, A., López-López, A., Medina Pagola, J. (2016). Exploring Random Indexing for Profile Learning. In: Quesada, J., Martín Mateos, FJ., Lopez-Soto, T. (eds) Future and Emergent Trends in Language Technology. FETLT 2015. Lecture Notes in Computer Science(), vol 9577. Springer, Cham. https://doi.org/10.1007/978-3-319-33500-1_7
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