Chinese Named Entity Recognition with
Changed BiLSTM and CRF
Jie Ren
(B) , Jing Liang, and Chenkai Zhao
University of Electronic Science and Technology of China, Chengdu, China
2356670185@qq.com
Abstract. This paper is aimed at improving name entity recognition
(NER) accuracy. We replace the traditional bidirectional long short-term
memory network (BiLSTM) with a changed BiLSTM, and then uses a
CRF layer behind the changed BiLSTM layer to add the probabilistic
relation of different Chinese characters.
Keywords: NER · CRF · Changed BiLSTM
1 Introduction
Name entity recognition (NER) is also known as proper name recognition. It
is a fundamental task in natural language processing (NLP) and has a wide
range of applications. Named entity generally refers to the entity with specific
meaning or strong reference in the text, usually including person name, place
name, organization name, and so on. NER is a foundation key task in NLP.
At the same time, NER is also the basis of many NLP tasks such as relationship extraction, event extraction, knowledge mapping, machine translation, and
question answering system.
NER has been a research hotspot in the field of NLP. At beginning, NER
is based on dictionary and rule-based methods. Later, the traditional machine
learning methods, especially probabilistic graph models such as hidden Markov
model (HMM) [1,2], maximum entropy Markov model (MEMM), and conditional random field (CRF) [3,4], became the focus of NER’s research. In recent
years, NER based on deep learning such as long short-term memory (LSTM)
has been become popular [5]. However, both probability graph model and deep
learning model are incomplete. Probability graph model does not learn the deep
hidden information in sentences and deep learning model does not consider the
probabilistic relation between parts of speech and the parts of speech. Some
scholars found that the combination of two methods deals with the above problems [8]. Huang Z, et al. used bidirectional long short-term memory (BiLSTM)
and CRF to do NER, and the results is very good [6,7]. Then, Lample G, et al.
proposed that use two LSTM and CRF can obtain deeper hidden information
c
Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 398–406, 2020.
https://doi.org/10.1007/978-981-15-0187-6_47
Changed BiLSTM and CRF
Jie Ren
(B) , Jing Liang, and Chenkai Zhao
University of Electronic Science and Technology of China, Chengdu, China
2356670185@qq.com
Abstract. This paper is aimed at improving name entity recognition
(NER) accuracy. We replace the traditional bidirectional long short-term
memory network (BiLSTM) with a changed BiLSTM, and then uses a
CRF layer behind the changed BiLSTM layer to add the probabilistic
relation of different Chinese characters.
Keywords: NER · CRF · Changed BiLSTM
1 Introduction
Name entity recognition (NER) is also known as proper name recognition. It
is a fundamental task in natural language processing (NLP) and has a wide
range of applications. Named entity generally refers to the entity with specific
meaning or strong reference in the text, usually including person name, place
name, organization name, and so on. NER is a foundation key task in NLP.
At the same time, NER is also the basis of many NLP tasks such as relationship extraction, event extraction, knowledge mapping, machine translation, and
question answering system.
NER has been a research hotspot in the field of NLP. At beginning, NER
is based on dictionary and rule-based methods. Later, the traditional machine
learning methods, especially probabilistic graph models such as hidden Markov
model (HMM) [1,2], maximum entropy Markov model (MEMM), and conditional random field (CRF) [3,4], became the focus of NER’s research. In recent
years, NER based on deep learning such as long short-term memory (LSTM)
has been become popular [5]. However, both probability graph model and deep
learning model are incomplete. Probability graph model does not learn the deep
hidden information in sentences and deep learning model does not consider the
probabilistic relation between parts of speech and the parts of speech. Some
scholars found that the combination of two methods deals with the above problems [8]. Huang Z, et al. used bidirectional long short-term memory (BiLSTM)
and CRF to do NER, and the results is very good [6,7]. Then, Lample G, et al.
proposed that use two LSTM and CRF can obtain deeper hidden information
c
Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 398–406, 2020.
https://doi.org/10.1007/978-981-15-0187-6_47