词嵌入技术与文本相似度度量的比较分析

Nagothi Vaibhav Anjani Kumar, S. Mehrotra
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引用次数: 0

摘要

数字文本数据的各种用途日益增加,如临床记录、实验室测试报告、研究论文等。上面提到的大多数数据都是非结构化的。在搜索信息时,会根据查询返回许多不相关的信息。本文介绍了词嵌入技术和文本相似度度量的比较分析,以确定两个文本在各自的词汇、语义特征和接近度方面的相似程度。本文的主要目的是对患者数据的病史笔记进行预处理处理,然后采用Word2Vec、FastText、Doc2Vec等词嵌入技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comparative Analysis of word embedding techniques and text similarity Measures
Digital text data is increasing daily in various uses, such as clinical notes, lab test reports, research articles, etc. Most of the mentioned data are unstructured. While searching for information lot of unrelated information is returned against the query. The paper presents a comparative analysis of word embedding techniques and text similarity measures to determine how similar two bits of text are in respective lexical, semantic characteristics, and closeness. The principal aim of this paper is to perform pre-processing process of medical history notes of the patient's data followed by word embedding techniques such as Word2Vec, FastText, and Doc2Vec.
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