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Chinese Named Entity Recognition Based on Character-Word Vector Fusion
2020
Wireless Communications and Mobile Computing
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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 ...
The experimental results show that the model based on character-word vector fusion improves the recognition effect of the Chinese named entity. ...
Acknowledgments This work is supported in part by the Key Science and Technology Program of Xi'an (Grant No. Z20180253) and the Xi'an Science and Technology Innovation Leading Project (Grant No. ...
doi:10.1155/2020/8866540
doaj:f5ef6225ca7f422cb214f57da45d3cfd
fatcat:3fwtsimpife57atvmntljnjrpm
A Joint Learning Model to Extract Entities and Relations for Chinese Literature Based on Self-Attention
2022
Mathematics
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It includes two key technologies: named entity recognition (NER) and relation extraction (RE). ...
However, previous NER models consider less about the influence of mutual attention between words in the text on the prediction of entity labels, and there is less research on how to more fully extract ...
[13] also introduced the joint training for the Chinese word segmentation model [14] and the named entity recognition model, improved the performance of named entity recognition by jointly training ...
doi:10.3390/math10132216
fatcat:5b4ykq7bazfhdmgftwvp4xgv5e
A Pragmatic Chinese Word Segmentation System
2006
Workshop on Chinese Language Processing
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In our system, Trigram model with smoothing algorithm is the core module in word segmentation, and Maximum Entropy model is the basic model in Named Entity Recognition task. ...
This paper presents our work for participation in the Third International Chinese Word Segmentation Bakeoff. ...
Named Entity Recognition We adopt Maximum Entropy model to perform the Named Entity Recognition. ...
dblp:conf/acl-sighan/JiangGW06
fatcat:rkzbvwlqwbcslppaob4uuhaam4
Joint Entity Recognition and Disambiguation
2015
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
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Existing systems typically run a named entity recognition (NER) model to extract entity names first, then run an entity linking model to link extracted names to a knowledge base. ...
We propose JERL, Joint Entity Recognition and Linking, to jointly model NER and linking tasks and capture the mutual dependency between them. ...
Thanks Shuming Shi, Bin Gao, and Yohn Cao for their helpful guidance and valuable discussions. ...
doi:10.18653/v1/d15-1104
dblp:conf/emnlp/LuoHLN15
fatcat:uojskf5ay5hi7ar4smkcwxskjq
Multi-task learning for Chinese clinical named entity recognition with external knowledge
2021
BMC Medical Informatics and Decision Making
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We incorporate dictionary features into neural networks, and a general secondary named entity segmentation is used as auxiliary task to improve the performance of the primary task of named entity recognition ...
Background Named entity recognition (NER) on Chinese electronic medical/healthcare records has attracted significantly attentions as it can be applied to building applications to understand these records ...
Multi-task network Clinical named entities segmentation and recognition are two related tasks and their outputs potentially have mutual benefits for each other as well. ...
doi:10.1186/s12911-021-01717-1
pmid:34972505
pmcid:PMC8719412
fatcat:w2lb7vq5g5gctol47nbaj5zyma
Omni-word Feature and Soft Constraint for Chinese Relation Extraction
2014
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Both Omni-word feature and soft constraint make a better use of sentence information and minimize the influences caused by Chinese word segmentation and parsing. ...
In order to utilize the structure information of a relation instance, we discuss how soft constraint can be used to capture the local dependency. ...
Based on the characteristics of Chinese, in this paper, an Omni-word feature and a soft constraint method are proposed for Chinese relation extraction. ...
doi:10.3115/v1/p14-1054
dblp:conf/acl/ChenZZ14
fatcat:cga4jamk55dvdhucmb4cwubssy
Studies on automatic recognition of Chinese adverb CAI's usages based on statistics
2009
2009 International Conference on Natural Language Processing and Knowledge Engineering
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Three statistical models, viz. CRF, ME, and SVM, are used to label several common Chinese adverbs' usages on the segmentation and part-of-speech tagged corpus of People's Daily(Jan 1998). ...
The study on Automatic Recognizing usages of Modern Chinese Adverbs is one of the important parts of the NLP-oriented research of Chinese Functional words Knowledge Base. ...
In the NLP applications, the language model based ME does not dependent on domain knowledge, and is independent of the specific task. ...
doi:10.1109/nlpke.2009.5313749
dblp:conf/nlpke/ZanZ09
fatcat:3lvsifjy5vetzi2ewblcjnfqru
Improved source-channel models for Chinese word segmentation
2003
Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - ACL '03
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Chinese words are defined as one of the following four types: lexicon words, morphologically derived words, factoids, and named entities. ...
This paper presents a Chinese word segmentation system that uses improved sourcechannel models of Chinese sentence generation. ...
detection, and named entity recognition (NER). ...
doi:10.3115/1075096.1075131
dblp:conf/acl/GaoLH03
fatcat:36y73qcoe5g35mmjusulspkz5i
Clinical Named Entity Recognition from Chinese Electronic Medical Records Based on Deep Learning Pretraining
2020
Journal of Healthcare Engineering
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Aiming at these characteristics of Chinese electronic medical records, this study proposed a Chinese clinical entity recognition model based on deep learning pretraining. ...
The model used word embedding from domain corpus and fine-tuning of entity recognition model pretrained by relevant corpus. ...
In view of the above problems, this study proposes a named entity recognition method for Chinese EMR based on pretraining. e method is based on word embedding pretraining and fine-tuning of entity recognition ...
doi:10.1155/2020/8829219
pmid:33299537
pmcid:PMC7707942
fatcat:majkysnbkbgjzaucovyrqpbyyq
Retrieval of Scientific and Technological Resources for Experts and Scholars
[article]
2022
arXiv
pre-print
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However, due to information asymmetry and other reasons, the scientific and technological resources of experts and scholars cannot be connected with the society in a timely manner, and social needs cannot ...
Therefore, it is very necessary to build an expert and scholar information database and provide relevant expert and scholar retrieval services. ...
effectively captured the long text entity labels. distance dependence. (2) The method based on sequence labeling is the problem of combining the two tasks of named entity name recognition and relation ...
arXiv:2204.06142v1
fatcat:qspkjuqrhbc6he3pdoyrheukua
Construction and Research on Chinese Semantic Mapping Based on Linguistic Features and Sparse Self-Learning Neural Networks
2022
Computational Intelligence and Neuroscience
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Based on the segmented convolutional neural network, the model first introduces the dependent subtree of relational attributes to obtain the position weights of each word in the sentence, then introduces ...
of the network term entity and relational attribute recognition extraction and makes the knowledge map constructed in this paper. ...
window context and incorporate the idea of point-by-point mutual information to enhance the influence of environment vectors on entity discovery with the help of a pretrained named entity lexicon, based ...
doi:10.1155/2022/2315802
pmid:35769283
pmcid:PMC9236845
fatcat:pwd5ryxrhzay3lnvmyxa2duvyi
Chinese Word Segmentation and Named Entity Recognition: A Pragmatic Approach
2005
Computational Linguistics
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It consists of two components: (1) a generic segmenter that is based on the framework of linear mixture models and provides a unified approach to the five fundamental features of word-level Chinese language ...
processing: lexicon word processing, morphological analysis, factoid detection, named entity recognition, and new word identification; and (2) a set of output adaptors for adapting the output of (1) to ...
Acknowledgments The work reported in this article was a team effort. The number of people intimately involved in this project is far too large for us to enumerate here. We only name a few now. ...
doi:10.1162/089120105775299177
fatcat:ydmj354zdnf53k4rpaydkvksgi
A benchmark dataset and case study for Chinese medical question intent classification
2020
BMC Medical Informatics and Decision Making
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Word segmentation and named entities are obtained using the Jieba and a well-trained Lattice-LSTM model. ...
Besides the intent label, CMID also provides two types of additional information, including word segmentation and named entity. ...
About this supplement This article has been published as part of BMC Medical Informatics and Decision Making Volume 20 Supplement 3, 2020: Health Information Processing. ...
doi:10.1186/s12911-020-1122-3
pmid:32646426
fatcat:3v3pdjr4dvcclk6kmn3ykymsva
A Latent Dirichlet Allocation and Fuzzy Clustering Based Machine Learning Model for Text Thesaurus
2020
International Journal of Computers Communications & Control
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The experimental results show that the Word2vec algorithm based on machine learning model has the highest accuracy, recall and F-value indicators. ...
The topic keywords will be used as a seed dictionary for new word discovery. ...
Its main functions are: Chinese word segmentation, part-of-speech tagging, recognition of new words, entity name recognition, etc. ...
doi:10.15837/ijccc.2020.2.3811
fatcat:zjh25fqlh5gd5f6lqsnrbnt6ii
Adaptive Chinese word segmentation
2004
Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics - ACL '04
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We first present a statistical framework where domain-specific words are identified in a unified approach to word segmentation based on linear models. ...
This paper presents a Chinese word segmentation system which can adapt to different domains and standards. ...
As described in Section 2, with a context model, NWI can be performed simultaneously with other word segmentation tasks (e.g.: word break, named entity recognition and morphological analysis) in a unified ...
doi:10.3115/1218955.1219014
dblp:conf/acl/GaoWHLXQ04
fatcat:dlkc2znj2vbhbfkoesef3h4gri
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