Cargando…
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...
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: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148038/ http://dx.doi.org/10.1007/978-3-030-45442-5_41 |
Ejemplares similares
-
Keyphrase Identification Using Minimal Labeled Data with Hierarchical Context and Transfer Learning
por: Goli, Rohan, et al.
Publicado: (2023) -
DAKE: Document-Level Attention for Keyphrase Extraction
por: Santosh, Tokala Yaswanth Sri Sai, et al.
Publicado: (2020) -
Learning Based Methods for Code Runtime Complexity Prediction
por: Sikka, Jagriti, et al.
Publicado: (2020) -
Multi-level Memory Network with CRFs for Keyphrase Extraction
por: Zhou, Tao, et al.
Publicado: (2020) -
Deep neural model with self-training for scientific keyphrase extraction
por: Zhu, Xun, et al.
Publicado: (2020)