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SELECTIVELY GENERATING WORD VECTOR AND PARAGRAPH VECTOR REPRESENTATIONS OF FIELDS FOR MACHINE LEARNING

机译:机器学习领域的选择性生成词向量和段向量向量表示

摘要

Word vectors are multi-dimensional vectors that represent words in a corpus of text and that are embedded in a semantically-encoded vector space; paragraph vectors extend word vectors to represent, in the same semantically-encoded space, the overall semantic content and context of a phrase, sentence, paragraph, or other multi-word sample of text. Word and paragraph vectors can be used for sentiment analysis, comparison of the topic or content of samples of text, or other natural language processing tasks. However, the generation of word and paragraph vectors can be computationally expensive. Accordingly, word and paragraph vectors can be determined only for user-specified subsets of fields of incident reports in a database.
机译:单词向量是多维向量,代表文本语料库中的单词,并嵌入在语义编码的向量空间中;段落向量扩展了词向量,以在相同的语义编码空间中表示短语,句子,段落或其他多词文本样本的整体语义内容和上下文。单词和段落矢量可用于情感分析,主题比较或文本样本内容或其他自然语言处理任务。但是,单词和段落矢量的生成在计算上可能是昂贵的。因此,只能为数据库中事件报告的用户指定字段子集确定单词和段落矢量。

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