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An Improved Historical Embedding without Alignment
[article]
2019
arXiv
pre-print
Many words have evolved in meaning as a result of cultural and social change. Understanding such changes is crucial for modelling language and cultural evolution. Low-dimensional embedding methods have shown promise in detecting words' meaning change by encoding them into dense vectors. However, when exploring semantic change of words over time, these methods require the alignment of word embeddings across different time periods. This process is computationally expensive, prohibitively time
arXiv:1910.08692v1
fatcat:eyitam6ybvfu3cauvpkzs2n3v4