Character-level Based Conference Named Entity Recognition Using Bi-LSTM
Rui Xiong, Chensheng Wu, Li Xiong, Luyu Li
Available Online May 2019.
- 10.2991/cnci-19.2019.11How to use a DOI?
- Conference named entity recognition, character, BiLSTM, viterbi, open domain.
For name entity recognition in specific field, a character-level bidirectional LSTM(Bidirectional Long Short-Term Memory, BiLSTM-based conference named entity recognition method is proposed. Introducing word embedding as the input of BiLSTM. each character in a sentence is mapped from a one-hot vector to a low-dimensional dense character embedding through word embedding. After the BiLSTM layer, viterbi algorithm is used to decode the output of BiLSTM. The results show that the precision rate, the recall rate and F value can reach 89.17%, 90.24% and 89.70%, indicating the effectiveness and practicability of the method of conference named entity recognition for open domain data.
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Rui Xiong AU - Chensheng Wu AU - Li Xiong AU - Luyu Li PY - 2019/05 DA - 2019/05 TI - Character-level Based Conference Named Entity Recognition Using Bi-LSTM BT - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019) PB - Atlantis Press SP - 83 EP - 88 SN - 2352-538X UR - https://doi.org/10.2991/cnci-19.2019.11 DO - 10.2991/cnci-19.2019.11 ID - Xiong2019/05 ER -