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A New word Clustering Algorithm Based on word similarity

Chinese Journal of Electronics 2017年第6期26卷 1221-1226页

作者： YUAN Lichi School of Information Technology Jiangxi University of Finance and Economics

Category-based statistic language model is an important method to solve the problem of sparse data in statistical language models. But there are two bottlenecks about this model: 1) The problem of word clustering, it is hard to find a suitable clustering method that has good performance and has not large amount of computation; 2)Class-based method always loses some prediction ability to adapt the text of different domain. In order to solve above problems, a novel definition of word similarity by utilizing mutual information was presented. Based on word similarity, the definition of word set similarity was given and a bottom-up hierarchical clustering algorithm was *** results show that the word clustering algorithm based on word similarity is better than conventional greedy clustering method in speed and performance, the perplexity is reduced from 283 to 207.8.

关键词： word similarity word clustering Statistical language model

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Enhancing Embedding-Based Chinese word similarity Evaluation with Concepts and Synonyms Knowledge

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Computer Modeling in Engineering & Sciences 2020年第8期124卷 747-764页

作者： Fulian Yin Yanyan Wang Jianbo Liu Meiqi Ji Communication University of China Beijing100024China

word similarity(WS)is a fundamental and critical task in natural language *** approaches to WS are mainly to calculate the similarity or relatedness of word pairs based on word embedding obtained by massive and high-quality ***,it may suffer from poor performance for insufficient corpus in some specific fields,and cannot capture rich semantic and sentimental *** address these above problems,we propose an enhancing embedding-based word similarity evaluation with character-word concepts and synonyms knowledge,namely EWS-CS model,which can provide extra semantic information to enhance word similarity *** core of our approach contains knowledge encoder and word *** knowledge encoder,we incorporate the semantic knowledge extracted from knowledge resources,including character-word concepts,synonyms and sentiment lexicons,to obtain knowledge *** encoder is to learn enhancing embedding-based word representation from pre-trained model and knowledge representation based on similarity ***,compared with baseline models,the experiments on four similarity evaluation datasets validate the effectiveness of our EWS-CS model in WS task.

关键词： word representation concepts and synonyms knowledge word similarity information security

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Graph-Based Chinese word Sense Disambiguation with Multi-Knowledge Integration

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Computers, Materials & Continua 2019年第7期61卷 197-212页

作者： Wenpeng Lu Fanqing Meng Shoujin Wang Guoqiang Zhang Xu Zhang Antai Ouyang Xiaodong Zhang School of Computer Science and Technology Qilu University of Technology(Shandong Academy of Sciences)Jinan250353China School of Information Science and Engineering Zaozhuang UniversityZaozhuang277160China Department of Computing Macquarie UniversitySydneyNSW 2109Australia Centre for Audio Acoustics and VibrationUniversity of Technology SydneySydneyNSW 2006Australia School of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Jinan Intellectual Property Information Center Jinan250099China

word sense disambiguation(WSD)is a fundamental but significant task in natural language processing,which directly affects the performance of upper ***,WSD is very challenging due to the problem of knowledge bottleneck,i.e.,it is hard to acquire abundant disambiguation knowledge,especially in *** solve this problem,this paper proposes a graph-based Chinese WSD method with multi-knowledge ***,a graph model combining various Chinese and English knowledge resources by word sense mapping is ***,the content words in a Chinese ambiguous sentence are extracted and mapped to English words with ***,English word similarity is computed based on English word embeddings and knowledge *** word similarity is evaluated with Chinese word embedding and HowNet,*** weights of the three kinds of word similarity are optimized with simulated annealing algorithm so as to obtain their overall similarities,which are utilized to construct a disambiguation *** graph scoring algorithm evaluates the importance of each word sense node and judge the right senses of the ambiguous *** experimental results on SemEval dataset show that our proposed WSD method significantly outperforms the baselines.

关键词： word sense disambiguation graph model multi-knowledge integration word similarity

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Vari-gram language model based on word clustering

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Journal of Central South University 2012年第4期19卷 1057-1062页

作者：袁里驰 School of Information Technology Jiangxi University of Finance and Economics School of Information Science and Engineering Central South University

Category-based statistic language model is an important method to solve the problem of sparse *** there are two bottlenecks:1) The problem of word *** is hard to find a suitable clustering method with good performance and less computation.2) Class-based method always loses the prediction ability to adapt the text in different *** order to solve above problems,a definition of word similarity by utilizing mutual information was *** on word similarity,the definition of word set similarity was *** show that word clustering algorithm based on similarity is better than conventional greedy clustering method in speed and performance,and the perplexity is reduced from 283 to *** the same time,an absolute weighted difference method was presented and was used to construct vari-gram language model which has good prediction *** perplexity of vari-gram model is reduced from 234.65 to 219.14 on Chinese corpora,and is reduced from 195.56 to 184.25 on English corpora compared with category-based model.

关键词： word similarity word clustering statistical language model vari-gram language model

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Novel Representations of word Embedding Based on the Zolu Function

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Journal of Beijing Institute of Technology 2020年第4期29卷 526-530页

作者： Jihua Lu Youcheng Zhang School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China Department of Electronic Engineering Tsinghua UniversityBeijing 100091China

Two learning models,Zolu-continuous bags of words(ZL-CBOW)and Zolu-skip-grams(ZL-SG),based on the Zolu function are *** slope of Relu in word2vec has been changed by the Zolu *** proposed models can process extremely large data sets as well as word2vec without increasing the ***,the models outperform several word embedding methods both in word similarity and syntactic *** method of ZL-CBOW outperforms CBOW in accuracy by 8.43%on the training set of capital-world,and by 1.24%on the training set of ***,experimental simulations on word similarity and syntactic accuracy show that ZL-CBOW and ZL-SG are superior to LL-CBOW and LL-SG,respectively.

关键词： Zolu function word embedding continuous bags of words word similarity accuracy

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基于蚁群聚类算法的中文本体学习

基于蚁群聚类算法的中文本体学习

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第二十三届中国数据库学术会议

作者：刘强林世平福州大学计算机科学与技术系

1引言随着对语义Web研究的深入,需要构建越来越多的领域本体。目前大家在公认的构建领域本体的过程中,都需要领域专家的参与和协作,由知识工程师根据该领域的结构,将相关词汇和术语组成分级

关键词： Ant colony clustering algorithm Ontology learning Hownet word similarity

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