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Exploiting Multiple Embeddings for Chinese Named Entity Recognition

Canwen Xu, Feiyang Wang, Jialong Han, Chenliang Li
<span title="">2019</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6g37zvjwwrhv3dizi6ffue642m" style="color: black;">Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM &#39;19</a> </i> &nbsp;
The experimental results demonstrate that the proposed approach achieves a large performance improvement on Weibo dataset and comparable performance on MSRA news dataset with lower computational cost against  ...  NER in formal Chinese corpus.  ...  Ma and Hovy [10] used a CNN (Convolutional Neural Network)-BiLSTM-CRF network to utilize both word and character level representations. Chinese NER is more challenging compared to English.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3357384.3358117">doi:10.1145/3357384.3358117</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/cikm/XuWHL19.html">dblp:conf/cikm/XuWHL19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yy2iysmomzg5vcidndp65hd3ri">fatcat:yy2iysmomzg5vcidndp65hd3ri</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200829051523/https://arxiv.org/pdf/1908.10657v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ce/17/ce17af84ac2319ed685a20e0cc2d8f0916e4b2aa.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3357384.3358117"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF [article]

Yan Shao and Christian Hardmeier and Jörg Tiedemann and Joakim Nivre
<span title="2017-09-12">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We present a character-based model for joint segmentation and POS tagging for Chinese.  ...  The proposed model is extensively evaluated and compared with a state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese.  ...  This work is supported by the Chinese Scholarship Council (CSC) (No. 201407930015).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1704.01314v3">arXiv:1704.01314v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vvztyzjryzckxf7fulgxlfsfx4">fatcat:vvztyzjryzckxf7fulgxlfsfx4</a> </span>
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New Research on Transfer Learning Model of Named Entity Recognition

Guoliang Guan, Min Zhu
<span title="">2019</span> <i title="IOP Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wxgp7pobnrfetfizidmpebi4qy" style="color: black;">Journal of Physics, Conference Series</a> </i> &nbsp;
The new neural network model has obtained F1 scores on three Chinese datasets exceeds the previous BiLSTM-CRF model, especially on the value of recall.  ...  A bi-directional LSTM model can consider an effectively infinite amount of context on both sides of a word and eliminates the problem of limited context that applies to any feed-forward models.  ...  Acknowledgments The authors would like to thank Kang He, Yu Liu for helpful discussions, and the anonymous reviewers for insightful comments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1742-6596/1267/1/012017">doi:10.1088/1742-6596/1267/1/012017</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p46ct6xxijakfhwp73ovsfw36u">fatcat:p46ct6xxijakfhwp73ovsfw36u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191114085122/https://iopscience.iop.org/article/10.1088/1742-6596/1267/1/012017/pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ec/c9/ecc9291b2f46113d23a8d46a35ef5b204fa00776.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1742-6596/1267/1/012017"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> iop.org </button> </a>

Iterated dilated convolutional neural networks for word segmentation

Han He, Xiaokun Yang, Lei Wu, Guan Wang
<span title="">2020</span> <i title="Czech Technical University in Prague - Central Library"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pdm5r35z65fkvamed4nxpeyqqy" style="color: black;">Neural Network World</a> </i> &nbsp;
The latest development of neural word segmentation is governed by bi-directional Long Short-Term Memory Networks (Bi-LSTMs) that utilize Recurrent Neural Networks (RNNs) as standard sequence tagging models  ...  , resulting in expressive and accurate performance on large-scale dataset.  ...  Then, a novel word-based approach [12, 24] was proposed to directly model candidate segmented results.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14311/nnw.2020.30.022">doi:10.14311/nnw.2020.30.022</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hdad5zcnjbhgfccjoihiwzieaq">fatcat:hdad5zcnjbhgfccjoihiwzieaq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210123035439/http://nnw.cz/doi/2020/NNW.2020.30.022.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/54/13/541324f0b9a2894ae631ddf5ccb83d90a6ea7007.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14311/nnw.2020.30.022"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Chinese Named Entity Recognition System with Neural Networks

Hui-Kang Yi, Jiu-Ming Huang, Shu-Qiang Yang, L. Long, Y. Li, X. Li, Y. Dai, H. Yang
<span title="">2017</span> <i title="EDP Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mzzzerkv2zd63fbnrpvy24xm54" style="color: black;">ITM Web of Conferences</a> </i> &nbsp;
In this paper, we discussed the details of applying a comprehensive model aggregating neural networks and conditional random field (CRF) on Chinese NER tasks, and how to discovery character level features  ...  We compared the difference between Chinese and English when modeling the character embeddings.  ...  [9] combines Chiu's CNN model [10] and Lample's bidirectional LSTM-CRF model [8] for end-to-end SLPs.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/itmconf/20171204002">doi:10.1051/itmconf/20171204002</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n4g3wmwhgvdjxp6ra7v6dlquqq">fatcat:n4g3wmwhgvdjxp6ra7v6dlquqq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180721090600/https://www.itm-conferences.org/articles/itmconf/pdf/2017/04/itmconf_ita2017_04002.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/dd/91/dd9181580c5409b43b8306baa201c473aa3221e8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/itmconf/20171204002"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

BIBC: A Chinese Named Entity Recognition Model for Diabetes Research

Lei Yang, Yufan Fu, Yu Dai
<span title="2021-10-16">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
Based on the deep and bidirectional transformer network structure, the pre-training language model BERT model can solve the problem of polysemous word representation, and supplement the features by large-scale  ...  This method combines Iterated Dilated CNN to enable the model to take into account global and local features at the same time, and uses the BERT-WWM model based on whole word masking to further extract  ...  The deep learning based NER models mostly consist of three parts. The first one is the embedding stage.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app11209653">doi:10.3390/app11209653</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/grlagc4vevcupgp3nlseutihym">fatcat:grlagc4vevcupgp3nlseutihym</a> </span>
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A Survey on Recent Advances in Sequence Labeling from Deep Learning Models [article]

Zhiyong He, Zanbo Wang, Wei Wei, Shanshan Feng, Xianling Mao, Sheng Jiang
<span title="2020-11-13">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Furthermore, we also present an in-depth analysis of different SL models on the factors that may affect the performance and future directions in the SL domain.  ...  In this paper, we aim to present a comprehensive review of existing deep learning-based sequence labeling models, which consists of three related tasks, e.g., part-of-speech tagging, named entity recognition  ...  LSTM CRF NER [103] \ Word2vec CNN CNN LSTM NER [55] \ Glove \ Bi-GRU Pointer network Text segmentation [27] \ - CNN Bi-LSTM Softmax POS [20] \ Word2vec, FastText LSTM+attention  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.06727v1">arXiv:2011.06727v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lbephd7kdjh6libg2v5xju7lri">fatcat:lbephd7kdjh6libg2v5xju7lri</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201117004422/https://arxiv.org/pdf/2011.06727v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/39/cb/39cba8da65a0d042f4a40a1e97156080fdca46ff.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.06727v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Classical Arabic Named Entity Recognition Using Variant Deep Neural Network Architectures and BERT

Norah Alsaaran, Maha Alrabiah
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
ACKNOWLEDGMENT The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University for funding and supporting this work through Graduate Students Research  ...  A recent study [65] applied transfer learning with deep neural networks to build a Pooled-GRU model for MSA NER. Their model outperformed the BLSTM-CRF model proposed by [66] .  ...  RNN-based models such LSTM and GRU are the most dominating used models in NLP.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3092261">doi:10.1109/access.2021.3092261</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kt5alxl4k5bbrlzc7vpuhxwyd4">fatcat:kt5alxl4k5bbrlzc7vpuhxwyd4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210625135456/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09464352.pdf?tp=&amp;arnumber=9464352&amp;isnumber=6514899&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c4/69/c469cbe728c8bddb5e00fbc8345a2cf3b3e62513.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3092261"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Chinese Knowledge Base Question Answering by Attention-Based Multi-Granularity Model

Cun Shen, Tinglei Huang, Xiao Liang, Feng Li, Kun Fu
<span title="2018-04-19">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dmr4kpn2yreovpdxpdiqtjcrnu" style="color: black;">Information</a> </i> &nbsp;
Firstly, the question is represented as a sequence of vectors with a two-layer bidirectional Gated Recurrent Unit (GRU) hierarchical matching networks and relation is represented with a Bi-GRU respectively  ...  Our entity linker first trains a Bi-LSTM-CRF model to do the entity mention detection. Based on this detected mention, we search it in the entity vocabulary.  ...  Author Contributions: C.S. conceived and designed the algorithms and performed the experiments and analyzed the results; C.S. and X.L. wrote and revised the manuscript; T.H. and F.L. discussed the data  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/info9040098">doi:10.3390/info9040098</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rdmlxtzqvzfv3pr2t2cupgssd4">fatcat:rdmlxtzqvzfv3pr2t2cupgssd4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180726230603/https://res.mdpi.com/def502001ef6e9f98fa4553c19674c87c285e17f1fda2b5af767d6e579ec9f764f0cb06a4990b4b042c554f19b21056de2787ee609204231388f2d67fe2379ac089df858e6dd6f7f75c88b030c6d49d66f9ac8bb7417d0207574ba61d053d2be6fb2d08cad746f0ae0930ce805fd20cc2df36f7d9337746ad32c74519d204a47bc08772e5d5ed572c0eddb49c5f4e0560c66db0d02a739761f7eb19f?filename=&amp;attachment=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b1/53/b1533345d27c062cb1c47e7089b26bf43d17b7e3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/info9040098"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Recognition of Unknown Entities in Specific Financial Field Based on ERNIE-Doc-BiLSTM-CRF

Li Xin, Hao Xiaoyan, Zhao Hongru
<span title="2022-05-14">2022</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3wwzxqpotbc73bzpemzybzg7ee" style="color: black;">Computational Intelligence and Neuroscience</a> </i> &nbsp;
This paper proposes an ERNIE-Doc-BiLSTM-CRF named entity recognition model based on the pretrained language model.  ...  Compared with the traditional model, the ERNIE-Doc pretrained language model constructs a unique word vector from the word vector and combines the location coding, which solves polysemy problem well.  ...  Acknowledgments Key R&D Projects in Shanxi Province (rh2100005181); Key R&D projects in Shanxi Province (rh2100005178); Peking University Scientific Research and technology project (203290929-j).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/3139898">doi:10.1155/2022/3139898</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5j2b4p7uwjczzp5ddwdt4frxeu">fatcat:5j2b4p7uwjczzp5ddwdt4frxeu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220518201943/https://downloads.hindawi.com/journals/cin/2022/3139898.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5a/de/5adefdb400d5b8e21850e6969fbc4f46dbfc6aa3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/3139898"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Multi-Task Joint Learning Model for Chinese Word Segmentation and Syndrome Differentiation in Traditional Chinese Medicine

Chenyuan Hu, Shuoyan Zhang, Tianyu Gu, Zhuangzhi Yan, Jiehui Jiang
<span title="2022-05-05">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vyslcn4ljzdq3jes5w7fln3qyu" style="color: black;">International Journal of Environmental Research and Public Health</a> </i> &nbsp;
We use text classification to model syndrome differentiation for TCM, and use multi-task learning (MTL) and deep learning to accomplish the two challenging tasks of Chinese word segmentation and syndrome  ...  Our model yielded values of accuracy, specificity, and sensitivity of 0.93, 0.94, and 0.90, and 0.80, 0.82, and 0.78 on the Chinese word segmentation task and the syndrome differentiation task, respectively  ...  Chinese Word Segmentation We compared our method with several LSTM-based models, including LSTM, Bi-LSTM, and Bi-GRU.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijerph19095601">doi:10.3390/ijerph19095601</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/35564995">pmid:35564995</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC9103751/">pmcid:PMC9103751</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/57eh77zpxfhetiapohnkexe36m">fatcat:57eh77zpxfhetiapohnkexe36m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220507061215/https://mdpi-res.com/d_attachment/ijerph/ijerph-19-05601/article_deploy/ijerph-19-05601.pdf?version=1651741259" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/68/5d/685dfbf3aed1ce1831364dd02e8941327d58acf0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/ijerph19095601"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9103751" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

A BERT-BiGRU-CRF Model for Entity Recognition of Chinese Electronic Medical Records

Qiuli Qin, Shuang Zhao, Chunmei Liu, Abd E.I.-Baset Hassanien
<span title="2021-01-27">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/y3fh56bfunh5fgneywwba6d4ke" style="color: black;">Complexity</a> </i> &nbsp;
of which neglecting the combination of contextual information is one.  ...  The experimental results show that the F1 score of the model reaches 90.38%.  ...  Acknowledgments is research was supported by the Beijing Municipal Commission of Science and Technology Project (Z131100005613017), the model and demonstration application of the collaborative prevention  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/6631837">doi:10.1155/2021/6631837</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hr3gflfatvdsrk2m254ip2b4hq">fatcat:hr3gflfatvdsrk2m254ip2b4hq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210131052622/https://downloads.hindawi.com/journals/complexity/2021/6631837.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2e/65/2e65f18a569ffd84ebdb7e282e13e2247978de99.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/6631837"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>

Chinese Emergency Event Recognition Using Conv-RDBiGRU Model

Haoran Yin, Jinxuan Cao, Luzhe Cao, Guodong Wang
<span title="2020-05-21">2020</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3wwzxqpotbc73bzpemzybzg7ee" style="color: black;">Computational Intelligence and Neuroscience</a> </i> &nbsp;
Firstly, text corpus is preprocessed by word segmentation and stop words processing and uses word embedding to form the matrix of word vectors.  ...  gradient dispersion, the neural network joint model, Conv-RDBiGRU, integrated residual structure was proposed.  ...  However, for some languages, such as Chinese, there is no natural segmentation in Chinese, so, at first, word segmentation is required, and word segmentation tools are used to process text.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2020/7090918">doi:10.1155/2020/7090918</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32549887">pmid:32549887</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7260650/">pmcid:PMC7260650</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/inkoxawetzhchb6ddszircuahy">fatcat:inkoxawetzhchb6ddszircuahy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200522062251/http://downloads.hindawi.com/journals/cin/2020/7090918.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/23/c1/23c18267f1ab19e485cc7244005c4ec2dbfd271e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2020/7090918"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7260650" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

OPTIMIZE THE LEARNING RATE OF NEURAL ARCHITECTURE IN MYANMAR STEMMER

Yadanar Oo
<span title="2019-12-04">2019</span> <i title="Zenodo"> Zenodo </i> &nbsp;
This paper introduces a deep BiGRUCNN-CRF network that jointly learns word segmentation, stemming and named entity recognition tasks. We trained the model using manually annotated corpora.  ...  Word segmentation for Myanmar Language, like for most Asian Languages, is an important task and extensively-studied sequence labelling problem.  ...  In [14] , they proposed a character-based model for joint segmentation and POS tagging for Chinese that use bidirectional RNN-CRF architecture with novel vector representations of Chinese characters that  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3562088">doi:10.5281/zenodo.3562088</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rxlsxoxh4zd2fcx3i33uruduge">fatcat:rxlsxoxh4zd2fcx3i33uruduge</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200809232840/https://zenodo.org/record/3562089/files/1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c9/62/c962930953086136793bf62fb0916ebf6525643e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5281/zenodo.3562088"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> zenodo.org </button> </a>

A Mixed Semantic Features Model for Chinese NER with Characters and Words [chapter]

Ning Chang, Jiang Zhong, Qing Li, Jiang Zhu
<span title="">2020</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
The existing Chinese NER methods are mostly based on word segmentation, or use the character sequences as input.  ...  We validate our model on MSRA and Weibo corpora, and experiments demonstrate that our model can significantly improve the performance of the Chinese NER task.  ...  Most methods of existing state-of-the-art models for Chinese NER are usually based on word segmentation, and train neural network and Conditional Random Field (CRF) to perform sequence labeling on word-level  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-45439-5_24">doi:10.1007/978-3-030-45439-5_24</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rrp52ui4u5hvbcpo7hcimtm3gq">fatcat:rrp52ui4u5hvbcpo7hcimtm3gq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200509235205/https://link.springer.com/content/pdf/10.1007%2F978-3-030-45439-5_24.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ee/4a/ee4ab7f8e37fa579ea0927a623a1a2e8bfb917f9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-45439-5_24"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>
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