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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

Chinese word sense disambiguation based on neural networks

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Journal of Harbin Institute of Technology(New Series) 2005年第4期12卷 408-414页

作者：刘挺卢志茂郎君李生 Information Retrieval Laboratory of Computer Science and Technology School Harbin Institute of Technology Harbin 150001 China Information Retrieval Laboratory of Computer Science and Technology School Harbin Institute of Technology Harbin 150001 ChinaHumanity and Society Science School Harbin Institute of Technology Harbin 150001 China

The input of a network is the key problem for Chinese word sense disambiguation utilizing the neural network. This paper presents an input model of the neural network that calculates the mutual information between contextual words and the ambiguous word by using statistical methodology and taking the contextual words of a certain number beside the ambiguous word according to (-M,+N).The experiment adopts triple-layer BP Neural Network model and proves how the size of a training set and the value of Mand Naffect the performance of the Neural Network Model. The experimental objects are six pseudowords owning three word-senses constructed according to certain principles. The tested accuracy of our approach on a closed-corpus reaches 90.31%, and 89.62% on an open-corpus. The experiment proves that the Neural Network Model has a good performance on word sense disambiguation.

关键词： word sense disambiguation artificial neural network mutual information pseudowords

Improving the Collocation Extraction Method Using an Untagged Corpus for Persian word sense disambiguation

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Journal of Computer and Communications 2016年第4期4卷 109-124页

作者： Noushin Riahi Fatemeh Sedghi Computer Engineering Department Alzahra University Tehran Iran

word sense disambiguation is used in many natural language processing fields. One of the ways of disambiguation is the use of decision list algorithm which is a supervised method. Supervised methods are considered as the most accurate machine learning algorithms but they are strongly influenced by knowledge acquisition bottleneck which means that their efficiency depends on the size of the tagged training set, in which their preparation is difficult, time-consuming and costly. The proposed method in this article improves the efficiency of this algorithm where there is a small tagged training set. This method uses a statistical method for collocation extraction from a big untagged corpus. Thus, the more important collocations which are the features used for creation of learning hypotheses will be identified. Weighting the features improves the efficiency and accuracy of a decision list algorithm which has been trained with a small training corpus.

关键词： Collocation Extraction word sense disambiguation Untagged Corpus Decision List

CHINESE word sense disambiguation BY COMBINING PSEUDO TRAINING DATA

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CHINESE WORD SENSE DISAMBIGUATION BY COMBINING PSEUDO TRAINI...

Unsupervised WSD by Finding the Predominant sense Using Context as a Dynamic Thesaurus

2003 International Conference on Natural Language Processing and Knowledge Engineering

作者： Xiaojie Wang Yuji Matsumoto Graduate School of Information Science Nara Institute of Science and Technology School of Information Engineering Beijing University of Posts and Telecommunications

＜正＞In supervised methods of word sense disambiguation, sense tagged samples for training classifiers are *** sense tagging for ambiguous words is expensive and labor intensive,it is worth to look for some reasonable *** paper suggests a kind of substitute for Chinese sense tagged data, which is called pseudo training *** suggestion is based on a linguistic phenomenon in Chinese, which some multi-character-words inherit only one sense and some syntactical features from ambiguous *** derived from unambiguous multi-character-words are employed as pseudo training *** training data have an advantage of being able to be collected automatically. Our experiments show that classifiers trained by not too much pseudo training data outperform classifiers trained by small quantities of sense tagged samples for Chinese ambiguous word senses,further experiments show combination of a small set of tagged data and a large quantities pseudo training data is a more promising way to word sense disambiguation.

关键词： word sense disambiguation Pseudo training samples Chinese

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Journal of Computer Science & Technology 2010年第5期25卷 1030-1039页

作者： Javier Tejada-Carcamo Hiram Calvo Alexander Gelbukh Kazuo Hara San Pablo Catholic University ArequipaPeru Center for Computing Research National Polytechnic InstituteMexico City07738Mexico Nara Institute of Science and Technology TakayamaIkomaNara 630-0192Japan

We present and analyze an unsupervised method for word sense disambiguation（WSD）.Our work is based on the method presented by McCarthy et *** 2004 for finding the predominant sense of each word in the entire *** maximization algorithm allows weighted terms（similar words） from a distributional thesaurus to accumulate a score for each ambiguous word sense,i.e.,the sense with the highest score is chosen based on votes from a weighted list of terms related to the ambiguous *** list is obtained using the distributional similarity method proposed by Lin Dekang to obtain a *** the method of McCarthy et al.,every occurrence of the ambiguous word uses the same thesaurus,regardless of the context where the ambiguous word *** method accounts for the context of a word when determining the sense of an ambiguous word by building the list of distributed similar words based on the syntactic context of the ambiguous *** obtain a top precision of 77.54%of accuracy versus 67.10%of the original method tested on *** also analyze the effect of the number of weighted terms in the tasks of finding the Most Precuent sense（MFS） and WSD,and experiment with several corpora for building the word Space Model.

关键词： word sense disambiguation word space model semantic similarity text corpus thesaurus

Improving Entity Linking in Chinese Domain by sense Embedding Based on Graph Clustering

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Journal of Computer Science & Technology 2023年第1期38卷 196-210页

作者：张照博钟芷漫袁平鹏金海 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Service Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China

Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity *** is of great significance to some NLP(natural language processing)tasks,such as question *** English entity linking,Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words,which is more evident in certain *** Chinese domains,such as industry,the generated candidate entities are usually composed of long strings and are heavily *** addition,the meanings of the words that make up industrial entities are sometimes *** semantic space is a subspace of the general word embedding space,and thus each entity word needs to get its exact ***,we propose two schemes to achieve better Chinese entity ***,we implement an ngram based candidate entity generation method to increase the recall rate and reduce the nesting ***,we enhance the corresponding candidate entity ranking mechanism by introducing sense *** the contradiction between the ambiguity of word vectors and the single sense of the industrial domain,we design a sense embedding model based on graph clustering,which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with *** test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general *** confirm that our method can better learn candidate entities’fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.

关键词： natural language processing(NLP) domain entity linking computational linguistics word sense disambiguation knowledge graph

Overcoming Data Sparseness Problem in Statistical Corpus Based sense disambiguation

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Overcoming Data Sparseness Problem in Statistical Corpus Bas...

Research Progress of Polysemous disambiguation

The 10th EPMESC

作者： Francisco Oliveira Fai Wong Anna Ho Faculty of Science and Technology University of Macau Macao China Faculty of Science and Technology University of Macau Macao China Faculty of Science and Technology University of Macau Macao China

The problem of data sparseness is considered as a common problem for Statistical Corpus based word sense disambiguation (WSD) approaches [1]. Usually large amounts of data are required in the corpus t

关键词： word sense disambiguation Data Sparseness problem Singular Value Decomposition

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Research Progress of Polysemous Disambiguation