Abstrakti
Word embeddings have demonstrated strong performance on NLP tasks. However, lack of interpretability and the unsupervised nature of word embeddings have limited their use within computational social science and digital humanities. We propose the use of informative priors to create interpretable and domain-informed dimensions for probabilistic word embeddings. Experimental results show that sensible priors can capture latent semantic concepts better than or on-par with the current state of the art, while retaining the simplicity and generalizability of using priors.
Alkuperäiskieli | Englanti |
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Otsikko | The 2019 Conference on Empirical Methods in Natural Language Processing And the 9th International Joint Conference on Natural Language Processing |
Alaotsikko | Proceedings of System Demonstrations |
Sivut | 6324-6330 |
Tila | Julkaistu - 2019 |
OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisuussa |
Tapahtuma | Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing - Hong Kong, Kiina Kesto: 3 marraskuuta 2019 → 7 marraskuuta 2019 |
Conference
Conference | Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing |
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Lyhennettä | MNLP/IJCNLP |
Maa/Alue | Kiina |
Kaupunki | Hong Kong |
Ajanjakso | 03/11/2019 → 07/11/2019 |