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Meaning as use: from Wittgenstein to Google’s Word2vec. (English) Zbl 1446.68169
Skansi, Sandro (ed.), Guide to deep learning basics. Logical, historical and philosophical perspectives. Cham: Springer. 41-53 (2020).
Summary: Modern natural language processing (NLP) systems are based on neural networks that learn concept representation directly from data. In such systems, concepts are represented by real number vectors, with the background idea that mapping words into vectors should take into account the context of their use. The idea is present in Wittgenstein’s both early and late works, as well as in contemporary general linguistics, especially in the works of Firth. In this article, we investigate the relevance of Wittgenstein’s and Firth’s ideas for the development of Word2vec, a word vector representation used in a machine translation model developed by Google. We argue that one of the chief differences between Wittgenstein’s and Firth’s approaches to the word meaning, compared to the one applied in Word2vec, lies in the fact that, although all of them emphasise the importance of context, its scope is differently understood.
For the entire collection see [Zbl 1435.68042].
MSC:
68T50 Natural language processing
03A05 Philosophical and critical aspects of logic and foundations
03B65 Logic of natural languages
91F20 Linguistics
Software:
word2vec
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References:
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