Keyphrase Extraction as Sequence Labeling Using Contextualized Embeddings

In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM-CRF, where the words in the input text are represented using deep contextualized embeddings. We evaluate the proposed architecture using both contextualized and fixed word embed...

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Detalles Bibliográficos
Autores principales: Sahrawat, Dhruva, Mahata, Debanjan, Zhang, Haimin, Kulkarni, Mayank, Sharma, Agniv, Gosangi, Rakesh, Stent, Amanda, Kumar, Yaman, Shah, Rajiv Ratn, Zimmermann, Roger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148038/
http://dx.doi.org/10.1007/978-3-030-45442-5_41

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148038/
http://dx.doi.org/10.1007/978-3-030-45442-5_41

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