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Word Sense Disambiguation Based Sentiment Classification Using Linear Kernel Learning Scheme
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作者 P.Ramya B.Karthik 《Intelligent Automation & Soft Computing》 SCIE 2023年第5期2379-2391,共13页
Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the... Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning.Mining core features and performing the text classification still exist as a challenging task.Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach.This paper presented the text document classification that has wide applications in information retrieval,which uses movie review datasets.Here the document indexing based on controlled vocabulary,adjective,word sense disambiguation,generating hierarchical cate-gorization of web pages,spam detection,topic labeling,web search,document summarization,etc.Here the kernel support vector machine learning algorithm helps to classify the text and feature extract is performed by cuckoo search opti-mization.Positive review and negative review of movie dataset is presented to get the better classification accuracy.Experimental results focused with context mining,feature analysis and classification.By comparing with the previous work,proposed work designed to achieve the efficient results.Overall design is per-formed with MATLAB 2020a tool. 展开更多
关键词 Text classification word sense disambiguation kernel support vector machine learning algorithm cuckoo search optimization feature extraction
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Word sense disambiguation using semantic relatedness measurement 被引量:7
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作者 YANG Che-Yu 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2006年第10期1609-1625,共17页
All human languages have words that can mean different things in different contexts, such words with multiple meanings are potentially “ambiguous”. The process of “deciding which of several meanings of a term is in... All human languages have words that can mean different things in different contexts, such words with multiple meanings are potentially “ambiguous”. The process of “deciding which of several meanings of a term is intended in a given context” is known as “word sense disambiguation (WSD)”. This paper presents a method of WSD that assigns a target word the sense that is most related to the senses of its neighbor words. We explore the use of measures of relatedness between word senses based on a novel hybrid approach. First, we investigate how to “literally” and “regularly” express a “concept”. We apply set algebra to WordNet’s synsets cooperating with WordNet’s word ontology. In this way we establish regular rules for constructing various representations (lexical notations) of a concept using Boolean operators and word forms in various synset(s) defined in WordNet. Then we establish a formal mechanism for quantifying and estimating the semantic relatedness between concepts—we facilitate “concept distribution statistics” to determine the degree of semantic relatedness between two lexically expressed con- cepts. The experimental results showed good performance on Semcor, a subset of Brown corpus. We observe that measures of semantic relatedness are useful sources of information for WSD. 展开更多
关键词 Word sense disambiguation (WSD) Semantic relatedness WORDNET Natural language processing
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Chinese multi-document personal name disambiguation 被引量:8
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作者 Wang Houfeng(王厚峰) Mei Zheng 《High Technology Letters》 EI CAS 2005年第3期280-283,共4页
This paper presents a new approach to determining whether an interested personal name across doeuments refers to the same entity. Firstly,three vectors for each text are formed: the personal name Boolean vectors deno... This paper presents a new approach to determining whether an interested personal name across doeuments refers to the same entity. Firstly,three vectors for each text are formed: the personal name Boolean vectors denoting whether a personal name occurs the text the biographical word Boolean vector representing title, occupation and so forth, and the feature vector with real values. Then, by combining a heuristic strategy based on Boolean vectors with an agglomeratie clustering algorithm based on feature vectors, it seeks to resolve multi-document personal name coreference. Experimental results show that this approach achieves a good performance by testing on "Wang Gang" corpus. 展开更多
关键词 personal name disambiguation Chinese multi-document heuristic strategy. agglomerative clustering
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Adaptive Resonance Theory Based Two-Stage Chinese Name Disambiguation 被引量:1
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作者 Xin Wang Yuanchao Liu +2 位作者 Xiaolong Wang Ming Liu Bingquan Liu 《International Journal of Intelligence Science》 2012年第4期83-88,共6页
It’s common that different individuals share the same name, which makes it time-consuming to search information of a particular individual on the web. Name disambiguation study is necessary to help users find the per... It’s common that different individuals share the same name, which makes it time-consuming to search information of a particular individual on the web. Name disambiguation study is necessary to help users find the person of interest more readily. In this paper, we propose an Adaptive Resonance Theory (ART) based two-stage strategy for this problem. We get a first-stage clustering result with ART1 model and then merge similar clusters in the second stage. Our strategy is a mimic process of manual disambiguation and need not to predict the number of clusters, which makes it competent for the disambiguation task. Experimental results show that, in comparison with the agglomerative clustering method, our strategy improves the performance by respectively 0.92% and 5.00% on two kinds of name recognition results. 展开更多
关键词 NAME disambiguation Adaptive RESONANCE Theory TEXT Clustering Natural LANGUAGE Processing
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Graph-Based Chinese Word Sense Disambiguation with Multi-Knowledge Integration 被引量:1
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作者 Wenpeng Lu Fanqing Meng +4 位作者 Shoujin Wang Guoqiang Zhang Xu Zhang Antai Ouyang Xiaodong Zhang 《Computers, Materials & Continua》 SCIE EI 2019年第7期197-212,共16页
Word sense disambiguation(WSD)is a fundamental but significant task in natural language processing,which directly affects the performance of upper applications.However,WSD is very challenging due to the problem of kno... Word sense disambiguation(WSD)is a fundamental but significant task in natural language processing,which directly affects the performance of upper applications.However,WSD is very challenging due to the problem of knowledge bottleneck,i.e.,it is hard to acquire abundant disambiguation knowledge,especially in Chinese.To solve this problem,this paper proposes a graph-based Chinese WSD method with multi-knowledge integration.Particularly,a graph model combining various Chinese and English knowledge resources by word sense mapping is designed.Firstly,the content words in a Chinese ambiguous sentence are extracted and mapped to English words with BabelNet.Then,English word similarity is computed based on English word embeddings and knowledge base.Chinese word similarity is evaluated with Chinese word embedding and HowNet,respectively.The 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.The graph scoring algorithm evaluates the importance of each word sense node and judge the right senses of the ambiguous words.Extensive experimental results on SemEval dataset show that our proposed WSD method significantly outperforms the baselines. 展开更多
关键词 Word sense disambiguation graph model multi-knowledge integration word similarity
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An Error-Correcting Code-Based Robust Watermarking Scheme for Stereolithographic Files 被引量:1
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作者 Zhuorong Li Huawei Tian +2 位作者 Yanhui Xiao Yunqi Tang Anhong Wang 《Computer Systems Science & Engineering》 SCIE EI 2021年第5期247-263,共17页
Stereolithographic(STL)files have been extensively used in rapid prototyping industries as well as many other fields as watermarking algorithms to secure intellectual property and protect three-dimensional models from... Stereolithographic(STL)files have been extensively used in rapid prototyping industries as well as many other fields as watermarking algorithms to secure intellectual property and protect three-dimensional models from theft.However,to the best of our knowledge,few studies have looked at how watermarking can resist attacks that involve vertex-reordering.Here,we present a lossless and robust watermarking scheme for STL files to protect against vertexreordering attacks.Specifically,we designed a novel error-correcting code(ECC)that can correct the error of any one-bit in a bitstream by inserting several check digits.In addition,ECC is designed to make use of redundant information according to the characteristics of STL files,which introduces further robustness for defense against attacks.No modifications are made to the geometric information of the three-dimensional model,which respects the requirements of a highprecision model.The experimental results show that the proposed watermarking scheme can survive numerous kinds of attack,including rotation,scaling and translation(RST),facet reordering,and vertex-reordering attacks. 展开更多
关键词 Three-dimensional watermarking stereolithographic file robust watermarking error-correcting code
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WORD SENSE DISAMBIGUATION BASED ON IMPROVED BAYESIAN CLASSIFIERS 被引量:1
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作者 Liu Ting Lu Zhimao Li Sheng 《Journal of Electronics(China)》 2006年第3期394-398,共5页
Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in prac... Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%. 展开更多
关键词 Word Sense disambiguation (WSD) Natural Language Processing (NLP) Unsupervised learning algorithm Dependency Grammar (DG) Bayesian classifier
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Word sense disambiguation based on rough set
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作者 陈清才 王晓龙 +2 位作者 赵健 陈滨 王长风 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2002年第2期201-204,共4页
A sense feature system (SFS) is first automatically constructed from the text corpora to structurize the textural information. WSD rules are then extracted from SFS according to their certainty factors and are applied... A sense feature system (SFS) is first automatically constructed from the text corpora to structurize the textural information. WSD rules are then extracted from SFS according to their certainty factors and are applied to disambiguate the senses of polysemous words. The entropy of a deterministic rough prediction is used to measure the decision quality of a rule set. Finally, the back off rule smoothing method is further designed to improve the performance of a WSD model. In the experiments, a mean rate of correction achieved during experiments for WSD in the case of rule smoothing is 0.92. 展开更多
关键词 word SENSE disambiguation ROUGH SET SENSE FEATURE system
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Word Sense Disambiguation in Information Retrieval
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作者 Francis de la C. Fernández REYES Exiquio C. Pérez LEYVA Rogelio Lau FERNáNDEZ 《Intelligent Information Management》 2009年第2期122-127,共6页
The natural language processing has a set of phases that evolves from lexical text analysis to the pragmatic one in which the author’s intentions are shown. The ambiguity problem appears in all of these tasks. Previo... The natural language processing has a set of phases that evolves from lexical text analysis to the pragmatic one in which the author’s intentions are shown. The ambiguity problem appears in all of these tasks. Previous works tries to do word sense disambiguation, the process of assign a sense to a word inside a specific context, creating algorithms under a supervised or unsupervised approach, which means that those algorithms use or not an external lexical resource. This paper presents an approximated approach that combines not supervised algorithms by the use of a classifiers set, the result will be a learning algorithm based on unsupervised methods for word sense disambiguation process. It begins with an introduction to word sense disambiguation concepts and then analyzes some unsupervised algorithms in order to extract the best of them, and combines them under a supervised approach making use of some classifiers. 展开更多
关键词 disambiguation ALGORITHMS NATURAL LANGUAGE processing word SENSE disambiguation
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An Improved Name Disambiguation Method Based on Atom Cluster
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作者 Yu-Feng Yao 《Communications and Network》 2012年第1期30-33,共4页
An improved name disambiguation method based on atom cluster. Aiming at the method of character-related properties of similarity based on information extraction depends on the character information, a new name disambi... An improved name disambiguation method based on atom cluster. Aiming at the method of character-related properties of similarity based on information extraction depends on the character information, a new name disambiguation method is proposed, and improved k-means algorism for name disambiguation is proposed in this paper. The cluster analysis cluster is introduced to the name disambiguation process. Experiment results show that the proposed method having the high implementation efficiency and can distinguish the different people with the same name. 展开更多
关键词 RELATION NAME disambiguation Data Mining ENTITY
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USING ERROR-CORRECTING ENCODERS TO DESIGN LOCAL-RANDOM SEQUENCE GENERATORS
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作者 杨义先 《Journal of Electronics(China)》 1995年第1期9-14,共6页
This paper proved the statement that a good linear block encoder is in fact a good local-random sequence generator. Furthermore, this statement discovers the deep relationship between the error-correcting coding theor... This paper proved the statement that a good linear block encoder is in fact a good local-random sequence generator. Furthermore, this statement discovers the deep relationship between the error-correcting coding theory and the modern cryptography. 展开更多
关键词 error-correcting CODING CRYPTOGRAPHY RANDOM SEQUENCES
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Using Wikipedia as an External Knowledge Source for Supporting Contextual Disambiguation
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作者 Shahida Jabeen Xiaoying Gao Peter Andreae 《Journal of Software Engineering and Applications》 2012年第12期175-180,共6页
Every term has a meaning but there are terms which have multiple meanings. Identifying the correct meaning of a term in a specific context is the goal of Word Sense Disambiguation (WSD) applications. Identifying the c... Every term has a meaning but there are terms which have multiple meanings. Identifying the correct meaning of a term in a specific context is the goal of Word Sense Disambiguation (WSD) applications. Identifying the correct sense of a term given a limited context is even harder. This research aims at solving the problem of identifying the correct sense of a term given only one term as its context. The main focus of this research is on using Wikipedia as the external knowledge source to decipher the true meaning of each term using a single term as the context. We experimented with the semantically rich Wikipedia senses and hyperlinks for context disambiguation. We also analyzed the effect of sense filtering on context extraction and found it quite effective for contextual disambiguation. Results have shown that disambiguation with filtering works quite well on manually disambiguated dataset with the performance accuracy of 86%. 展开更多
关键词 CONTEXTUAL disambiguation WIKIPEDIA HYPERLINKS SEMANTIC Relatedness
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Name Disambiguation Method Based on Attribute Match and Link Analysis
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作者 Yu-Feng Yao 《Journal of Software Engineering and Applications》 2012年第1期29-32,共4页
A name disambiguation method is proposed based on attribute match and link analysis applying in the field of insurance. Aiming at the former name disambiguation methods such as text clustering method needs to be consi... A name disambiguation method is proposed based on attribute match and link analysis applying in the field of insurance. Aiming at the former name disambiguation methods such as text clustering method needs to be considered in a lot of useless words, a new name disambiguation method is advanced. Firstly, the same attribute matching is applied, merging the identity of a successful match, secondly, the link analysis is used, structural analysis of customers network is analyzed, Finally, the same cooperating information is merged. Experiment results show that the proposed method can realize name disambiguation successfully. 展开更多
关键词 NAME disambiguation Data MINING ATTRIBUTE MATCH LINK Analysis
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Chinese word sense disambiguation based on neural networks
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作者 刘挺 卢志茂 +1 位作者 郎君 李生 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第4期408-414,共7页
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 con... 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. 展开更多
关键词 word sense disambiguation artificial neural network mutual information pseudowords
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Quantum secret sharing based on quantum error-correcting codes
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作者 张祖荣 刘伟涛 李承祖 《Chinese Physics B》 SCIE EI CAS CSCD 2011年第5期91-95,共5页
Quantum secret sharing(QSS) is a procedure of sharing classical information or quantum information by using quantum states. This paper presents how to use a [2k- 1, 1, k] quantum error-correcting code (QECC) to im... Quantum secret sharing(QSS) is a procedure of sharing classical information or quantum information by using quantum states. This paper presents how to use a [2k- 1, 1, k] quantum error-correcting code (QECC) to implement a quantum (k, 2k-1) threshold scheme. It also takes advantage of classical enhancement of the [2k-1, 1, k] QECC to establish a QSS scheme which can share classical information and quantum information simultaneously. Because information is encoded into QECC, these schemes can prevent intercept-resend attacks and be implemented on some noisy channels. 展开更多
关键词 quantum secret sharing quantum error-correcting code classically enhanced quantumerror-correcting code
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Improving the Collocation Extraction Method Using an Untagged Corpus for Persian Word Sense Disambiguation
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作者 Noushin Riahi Fatemeh Sedghi 《Journal of Computer and Communications》 2016年第4期109-124,共16页
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 ... 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. 展开更多
关键词 Collocation Extraction Word Sense disambiguation Untagged Corpus Decision List
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Word Sense Disambiguation Model with a Cache-Like Memory Module
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作者 LIN Qian LIU Xin +4 位作者 XIN Chunlei ZHANG Haiying ZENG Hualin ZHANG Tonghui SU Jinsong 《Journal of Donghua University(English Edition)》 CAS 2021年第4期333-340,共8页
Word sense disambiguation(WSD),identifying the specific sense of the target word given its context,is a fundamental task in natural language processing.Recently,researchers have shown promising results using long shor... Word sense disambiguation(WSD),identifying the specific sense of the target word given its context,is a fundamental task in natural language processing.Recently,researchers have shown promising results using long short term memory(LSTM),which is able to better capture sequential and syntactic features of text.However,this method neglects the dependencies among instances,such as their context semantic similarities.To solve this problem,we proposed a novel WSD model by introducing a cache-like memory module to capture the semantic dependencies among instances for WSD.Extensive evaluations on standard datasets demonstrate the superiority of the proposed model over various baselines. 展开更多
关键词 word sense disambiguation(WSD) memory module semantic dependencies
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数字图像混沌序列抽样加权强置乱算法仿真
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作者 贺国平 张国荣 《计算机仿真》 2024年第10期192-195,350,共5页
数字图像置乱关系到个人隐私和信息安全问题,为了保护图像中的敏感信息,提出一种基于位交换和混沌优化的数字图像置乱算法。使用散列函数获得数字图像明文的密钥流,采用与明文相关的子密钥推导像素初始值,通过非线性交叉生成位交换算子... 数字图像置乱关系到个人隐私和信息安全问题,为了保护图像中的敏感信息,提出一种基于位交换和混沌优化的数字图像置乱算法。使用散列函数获得数字图像明文的密钥流,采用与明文相关的子密钥推导像素初始值,通过非线性交叉生成位交换算子,增强明文信息敏感性;运用Logistic方程得到混沌序列,引入抽样加权方法提高图像置乱强度,采用混合蛙跳方法按照族群划分实施信息传输,将混沌序列内的实数从小到大排列,初始混沌抽样后构成的混沌序列用于图像中,显著提升图像置乱强度,完成数字图像置乱。仿真结果表明,上述方法拥有极高的安全性和优秀的加密性能,可以为数字图像在各领域的安全使用提供可靠借鉴。 展开更多
关键词 位交换 混沌优化 数字图像 置乱算法 敏感性增强 混合蛙跳方法
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基于人工智能神经网络的上下文介词消歧方法
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作者 张明 廖希 《西南大学学报(自然科学版)》 CAS CSCD 北大核心 2024年第10期222-232,共11页
介词结构的分析难点在于如何对介词及其结构进行有效分类,挖掘其语义信息,并对介词结构进行有效的消歧处理.为了应对这一难题,结合人工智能和神经网络技术,提出一种基于长短期记忆和注意力机制的树递归神经网络模型,旨在解决自然语言处... 介词结构的分析难点在于如何对介词及其结构进行有效分类,挖掘其语义信息,并对介词结构进行有效的消歧处理.为了应对这一难题,结合人工智能和神经网络技术,提出一种基于长短期记忆和注意力机制的树递归神经网络模型,旨在解决自然语言处理中的上下文介词消歧问题.该模型通过引入注意力机制,将模型注意力集中在与介词含义相关的关键信息上.首先,通过嵌入上下文解析树和上下文词向量,捕捉上下文词汇之间的语义关系.然后,采用带有长短期记忆功能的树递归神经网络(Long Short-Term Memory Tree Recurrent Neural Network, Tree-LSTM)模型为树中的每个节点生成隐藏特征,并递归地跟踪树中不同分支上的传播来计算树节点的上下文表示.最后,为了减少噪声对上下文中与介词含义相关的关键信息的影响,引入注意机制卷积神经网络(Attention-based Convolutional Neural Network, ACNN),使模型专注于需要消除歧义的文档中的重要部分.这种方式使模型能够自动选择并关注与当前介词含义最相关的词汇,从而提高消歧准确性.实验结果表明:在Semeval 2013 Task 12词义消歧数据集上,该文提出的模型取得了88.04%的F1-score,优于现有主流深度学习模型,验证了该文方法的有效性. 展开更多
关键词 人工智能 神经网络 介词消歧 深度学习 注意力机制
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命名实体消歧研究综述 被引量:1
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作者 李欣宇 赵震 《计算机技术与发展》 2024年第2期1-8,共8页
实体消歧是指在一个具体的知识库中,把一个被标识的实体指称链向它对应条目的过程。实体消歧的任务是根据上下文信息解决一个命名实体指称项对应多个实体概念的一词多义问题,它在从海量数据准确提取信息的知识图谱构建过程中起到重要作... 实体消歧是指在一个具体的知识库中,把一个被标识的实体指称链向它对应条目的过程。实体消歧的任务是根据上下文信息解决一个命名实体指称项对应多个实体概念的一词多义问题,它在从海量数据准确提取信息的知识图谱构建过程中起到重要作用,是自然语言处理中的一项基本任务。该文主要对实体消歧技术的相关研究内容进行综述。首先,阐述了实体消歧的国内外研究背景,并对命名实体识别、候选实体生成、候选实体排序等实体消歧相关理论进行全面梳理。其次,对实体消歧的具体含义及其研究内容进行详细综述,并对实体消歧研究内容的特点进行了分析。再次,将实体消歧技术的实现方法划分为三类并对涉及到的数据集进行归纳,并从四个方面讨论了实体消歧领域存在的难点和提高实体消歧准确率的途径,对消歧方法的优缺点及评价指标进行了总结,意在为改善实体消歧效果提供新的解决思路。最后,对实体消歧技术的应用和发展前景进行总结。 展开更多
关键词 实体消歧 命名实体识别 知识图谱 自然语言处理 综述
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