Word embedding has been widely used in word sense disambiguation(WSD)and many other tasks in recent years for it can well represent the semantics of words.However,the existing word embedding methods mostly represent e...Word embedding has been widely used in word sense disambiguation(WSD)and many other tasks in recent years for it can well represent the semantics of words.However,the existing word embedding methods mostly represent each word as a single vector,without considering the homonymy and polysemy of the word;thus,their performances are limited.In order to address this problem,an effective topical word embedding(TWE)‐based WSD method,named TWE‐WSD,is proposed,which integrates Latent Dirichlet Allocation(LDA)and word embedding.Instead of generating a single word vector(WV)for each word,TWE‐WSD generates a topical WV for each word under each topic.Effective integrating strategies are designed to obtain high quality contextual vectors.Extensive experiments on SemEval‐2013 and SemEval‐2015 for English all‐words tasks showed that TWE‐WSD outperforms other state‐of‐the‐art WSD methods,especially on nouns.展开更多
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
针对API RP 2A-WSD 22版规范更新将对固定式海洋平台带来设计变化的问题,采用与旧版规范对比的方法对API新规范的引用标准、暴露分级及设计标准和方法等方面进行分析,包括底甲板高度算法、鲁棒性分析和抗震能力储备系数等重要变化。以...针对API RP 2A-WSD 22版规范更新将对固定式海洋平台带来设计变化的问题,采用与旧版规范对比的方法对API新规范的引用标准、暴露分级及设计标准和方法等方面进行分析,包括底甲板高度算法、鲁棒性分析和抗震能力储备系数等重要变化。以实际项目为算例,基于Pushover法计算平台的结构极限强度状态,分析表明,根据新版规范设计海洋平台,可以显著提高平台遭受极端工况的生存能力。展开更多
基金National Natural Science Foundation of China,Grant/Award Number:61562054The Fund of China Scholarship Council,Grant/Award Number:201908530036Talents Introduction Project of Guangxi University for Nationalities,Grant/Award Number:2014MDQD020。
文摘Word embedding has been widely used in word sense disambiguation(WSD)and many other tasks in recent years for it can well represent the semantics of words.However,the existing word embedding methods mostly represent each word as a single vector,without considering the homonymy and polysemy of the word;thus,their performances are limited.In order to address this problem,an effective topical word embedding(TWE)‐based WSD method,named TWE‐WSD,is proposed,which integrates Latent Dirichlet Allocation(LDA)and word embedding.Instead of generating a single word vector(WV)for each word,TWE‐WSD generates a topical WV for each word under each topic.Effective integrating strategies are designed to obtain high quality contextual vectors.Extensive experiments on SemEval‐2013 and SemEval‐2015 for English all‐words tasks showed that TWE‐WSD outperforms other state‐of‐the‐art WSD methods,especially on nouns.
文摘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.
文摘针对API RP 2A-WSD 22版规范更新将对固定式海洋平台带来设计变化的问题,采用与旧版规范对比的方法对API新规范的引用标准、暴露分级及设计标准和方法等方面进行分析,包括底甲板高度算法、鲁棒性分析和抗震能力储备系数等重要变化。以实际项目为算例,基于Pushover法计算平台的结构极限强度状态,分析表明,根据新版规范设计海洋平台,可以显著提高平台遭受极端工况的生存能力。
