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Diachronic Embeddings for People in the News
Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science
Previous English-language diachronic change models based on word embeddings have typically used single tokens to represent entities, including names of people. This leads to issues with both ambiguity (resulting in one embedding representing several distinct and unrelated people) and unlinked references (leading to several distinct embeddings which represent the same person). In this paper, we show that using named entity recognition and heuristic name linking steps before training a diachronicdoi:10.18653/v1/2020.nlpcss-1.19 fatcat:f62hksvcz5cgdpcxj6igkbsejq