scholarly journals Named entity recognition for Chinese judgment documents based on BiLSTM and CRF

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Wenming Huang ◽  
Dengrui Hu ◽  
Zhenrong Deng ◽  
Jianyun Nie
Keyword(s):  
Viterbi Algorithm ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Input Sequence ◽  
Entity Recognition ◽  
Computational Time ◽  
Short Term ◽  
Named Entity ◽  
Long Short Term Memory

AbstractChinese named entity recognition (CNER) in the judicial domain is an important and fundamental task in the analysis of judgment documents. However, only a few researches have been devoted to this task so far. For Chinese named entity recognition in judgment documents, we propose the use a bidirectional long-short-term memory (BiLSTM) model, which uses character vectors and sentence vectors trained by distributed memory model of paragraph vectors (PV-DM). The output of BiLSTM is used by conditional random field (CRF) to tag the input sequence. We also improved the Viterbi algorithm to increase the efficiency of the model by cutting the path with the lowest score. At last, a novel dataset with manual annotations is constructed. The experimental results on our corpus show that the proposed method is effective not only in reducing the computational time, but also in improving the effectiveness of named entity recognition in the judicial domain.

scholarly journals Pengenalan Entitas Bernama Otomatis untuk Bahasa Indonesia dengan Pendekatan Pembelajaran Mesin

2018 ◽  
Author(s):  
Yudi Wibisono ◽  
Masayu Leylia Khodra
Keyword(s):  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Short Term ◽  
Term Memory ◽  
Named Entity ◽  
Long Short Term Memory ◽  
F Measure ◽  
Bahasa Indonesia

Pengenalan entitas bernama (named-entity recognition atau NER) adalah proses otomatis mengekstraksi entitas bernama yang dianggap penting di dalam sebuah teks dan menentukan kategorinya ke dalam kategori terdefinisi. Sebagai contoh, untuk teks berita, NER dapat mengekstraksi nama orang, nama organisasi, dan nama lokasi. NER bermanfaat dalam berbagai aplikasi analisis teks, misalnya pencarian, sistem tanya jawab, peringkasan teks dan mesin penerjemah. Tantangan utama NER adalah penanganan ambiguitas makna karena konteks kata pada kalimat, misalnya kata “Cendana” dapat merupakan nama lokasi (Jalan Cendana), atau nama organisasi (Keluarga Cendana), atau nama tanaman. Tantangan lainnya adalah penentuan batas entitas, misalnya “[Istora Senayan] [Jakarta]”. Berbagai kakas NER telah dikembangkan untuk berbagai bahasa terutama Bahasa Inggris dengan kinerja yang baik, tetapi kakas NER bahasa Indonesia masih memiliki kinerja yang belum baik. Makalah ini membahas pendekatan berbasis pembelajaran mesin untuk menghasilkan model NER bahasa Indonesia. Pendekatan ini sangat bergantung pada korpus yang menjadi sumber belajar, dan teknik pembelajaran mesin yang digunakan. Teknik yang akan digunakan adalah LSTM - CRF (Long Short Term Memory – Conditional Random Field). Hasil terbaik (F-measure = 0.72) didapatkan dengan menggunakan word embedding GloVe Wikipedia Bahasa Indonesia.

scholarly journals Chinese Named Entity Recognition Method in History and Culture Field Based on BERT

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Shuang Liu ◽  
Hui Yang ◽  
Jiayi Li ◽  
Simon Kolmanič
Keyword(s):  
Short Term Memory ◽  
Conditional Random Field ◽  
Language Model ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Knowledge Graph ◽  
Recognition Method ◽  
Short Term ◽  
Named Entity ◽  
Long Short Term Memory

AbstractWith rapid development of the Internet, people have undergone tremendous changes in the way they obtain information. In recent years, knowledge graph is becoming a popular tool for the public to acquire knowledge. For knowledge graph of Chinese history and culture, most researchers adopted traditional named entity recognition methods to extract entity information from unstructured historical text data. However, the traditional named entity recognition method has certain defects, and it is easy to ignore the association between entities. To extract entities from a large amount of historical and cultural information more accurately and efficiently, this paper proposes one named entity recognition model combining Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory-Conditional Random Field (BERT-BiLSTM-CRF). First, a BERT pre-trained language model is used to encode a single character to obtain a vector representation corresponding to each character. Then one Bidirectional Long Short-Term Memory (BiLSTM) layer is applied to semantically encode the input text. Finally, the label with the highest probability is output through the Conditional Random Field (CRF) layer to obtain each character’s category. This model uses the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model to replace the static word vectors trained in the traditional way. In comparison, the BERT pre-trained language model can dynamically generate semantic vectors according to the context of words, which improves the representation ability of word vectors. The experimental results prove that the model proposed in this paper has achieved excellent results in the task of named entity recognition in the field of historical culture. Compared with the existing named entity identification methods, the precision rate, recall rate, and $$F_1$$ F 1 value have been significantly improved.

The effect of morphology in named entity recognition with sequence tagging

2018 ◽  
Vol 25 (1) ◽  
pp. 147-169 ◽  
Author(s):  
ONUR GÜNGÖR ◽  
TUNGA GÜNGÖR ◽  
SUZAN ÜSKÜDARLI
Keyword(s):  
Morphological Analysis ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Short Term ◽  
Named Entity ◽  
Long Short Term Memory ◽  
Morphologically Rich Languages ◽  
The Impact

AbstractThis work proposes a sequential tagger for named entity recognition in morphologically rich languages. Several schemes for representing the morphological analysis of a word in the context of named entity recognition are examined. Word representations are formed by concatenating word and character embeddings with the morphological embeddings based on these schemes. The impact of these representations is measured by training and evaluating a sequential tagger composed of a conditional random field layer on top of a bidirectional long short-term memory layer. Experiments with Turkish, Czech, Hungarian, Finnish and Spanish produce the state-of-the-art results for all these languages, indicating that the representation of morphological information improves performance.

scholarly journals Enhanced character embedding for Chinese named entity recognition

2020 ◽  
pp. 002029402095245
Author(s):  
Bingjing Jia ◽  
Zhongli Wu ◽  
Bin Wu ◽  
Yutong Liu ◽  
Pengpeng Zhou
Keyword(s):  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Short Term ◽  
Named Entity ◽  
Ancient Literature ◽  
Conditional Random Field Model ◽  
Level Information ◽  
Long Short Term Memory

Traditional named entity recognition methods mainly explore the application of hand-crafted features. Currently, with the popularity of deep learning, neural networks have been introduced to capture deep features for named entity recognition. However, most existing methods only aim at modern corpus. Named entity recognition in ancient literature is challenging because names in it have evolved over time. In this paper, we attempt to recognise entities by exploring the characteristics of characters and strokes. The enhanced character embedding model, named ECEM, is proposed on the basis of bidirectional encoder representations from transformers and strokes. First, ECEM can generate the semantic vectors dynamically according to the context of the words. Second, the proposed algorithm introduces morphological-level information of Chinese words. Finally, the enhanced character embedding is fed into the bidirectional long short term memory-conditional random field model for training. To explore the effect of our proposed algorithm, experiments are carried out on both ancient literature and modern corpus. The results indicate that our algorithm is very effective and powerful, compared with traditional ones.

scholarly journals Chinese Named Entity Recognition Based on Character-Word Vector Fusion

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Na Ye ◽  
Xin Qin ◽  
Lili Dong ◽  
Xiang Zhang ◽  
Kangkang Sun
Keyword(s):  
Short Term Memory ◽  
Conditional Random Field ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Short Term ◽  
Term Memory ◽  
Named Entity ◽  
The Neural Network ◽  
Long Short Term Memory ◽  
Character Vector

Due to the lack of explicit markers in Chinese text to define the boundaries of words, it is often more difficult to identify named entities in Chinese than in English. At present, the pretreatment of the character or word vector models is adopted in the training of the Chinese named entity recognition model. Aimed at the problems that taking character vector as an input of the neural network cannot use the words’ semantic meanings and give up the words’ explicit boundary information, and taking the word vector as an input of the neural network relies on the accuracy of the segmentation algorithms, a Chinese named entity recognition model based on character word vector fusion CWVF-BiLSTM-CRF (Character Word Vector Fusion-Bidirectional Long-Short Term Memory Networks-Conditional Random Field) is proposed in this paper. First, the Word2Vec is used to obtain the corresponding dictionaries of character-character vector and word-word vector. Second, the character-word vector is integrated as the input unit of the BiLSTM (Bidirectional Long-Short Term Memory) network, and then, the problem of an unreasonable tag sequence is solved using the CRF (conditional random field). By using the presented model, the dependence on the accuracy of the word segmentation algorithm is reduced, and the words’ semantic characteristics are effectively applied. The experimental results show that the model based on character-word vector fusion improves the recognition effect of the Chinese named entity.

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