Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Automatic identification of word translations from unrelated English and German corpora
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Wordica: Emergence of linguistic representations for words by independent component analysis
Natural Language Engineering
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Blind Signal Separation (BSS) based on Independent Component Analysis (ICA) is an emerging approach which application is not limited to the signal processing research, where its application principle is rather straight forward. For an increasing amount of information processing fields, ICA has meaningful application which are still undiscovered. The aim of this paper is to investigate the ability of linguistic feature extraction based on word context preprocessing by ICA. The work refers to a first brief analysis in which ICA was applied to an English corpus. We continue this analysis depending on the number of components and the amount of syntactical information that we take into account. Furthermore we discuss to which extent the results deliver general linguistic features, or linguistic features giving us information about the text.