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Word Embeddings to Classify Types of Diachronic Semantic Shift

Koldenhof, Dylan (2021) Word Embeddings to Classify Types of Diachronic Semantic Shift.

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Abstract:Languages are constantly evolving, in many ways. One of the ways they evolve is in semantics, the meaning of words. This presents an interesting challenge for automated Natural Language Processing (NLP), as a thorough manual inspection of this phenomenon is difficult. Much work has already shown promising results in detection of the semantic shift but there is little in the field investigating the nature of these shifts. This research aims to fill this gap by investigating whether different types of semantic shifts can be classified using word embeddings trained with Word2Vec. Different machine learning classifiers are trained on embeddings which are themselves trained on Project Gutenberg ebooks spanning the period 1800-1849, and embeddings trained on Wikipedia. Results show promise, but with a top accuracy of 0.5 when validated on another time period, there is room for improvement in future work.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:17 linguistics and theory of literature, 54 computer science
Programme:Computer Science BSc (56964)
Link to this item:http://purl.utwente.nl/essays/87304
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