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Time-Aware Word Embeddings for Three Lebanese News Archives
2020
International Conference on Language Resources and Evaluation
Word embeddings have proven to be an effective method for capturing semantic relations among distinct terms within a large corpus. In this paper, we present a set of word embeddings learnt from three large Lebanese news archives, which collectively consist of 609,386 scanned newspaper images and spanning a total of 151 years, ranging from 1933 till 2011. The diversified ideological nature of the news archives alongside the temporal variability of the embeddings offer a rare glimpse onto the
dblp:conf/lrec/DoughmanSE20
fatcat:gkhr2nhjxfbrrmmh6kptqlrj6a