基于海量文本数据的学术n-Gram构建

Myunggwon Hwang, Ha-neul Yeom, Mi-Nyeong Hwang, Hanmin Jung
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摘要

本研究的最终目标是提供专门用于学术用途的n-gram数据。为此,本文概述了通过处理大型文本文档构建学术n-gram的方法。许多研究人员,特别是非英语母语人士,发现很难用合适的和消歧的词来构建句子和段落。可以帮助他们的方法之一是提供n-gram数据。一个具有代表性的n-gram(称为Web 1t5 -gram Version 1)已经存在,它是通过处理使用谷歌检索的几乎所有文档而构建的。然而,这些数据包含了没有重点的单词推荐,因此,它们不适合。因此,我们正在构建一个学术性的n图。在本文中,我们使用Web 1T图展示了n-gram的效率,并介绍和讨论了我们与学术n-gram相关的研究计划的细节。
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Construction of Scholarly n-Gram from Huge Text Data
The ultimate goal of this research is to provide n-gram data that is specialized for scholarly utilization. To this end, this paper outlines the construction of a scholarly n-gram through the processing of large text documents. Many researchers, especially non-native English language speakers, find it difficult to construct sentences and paragraphs with appropriate and disambiguated words. One of the methods that can assist them is the provision of n-gram data. A representative n-gram known as Web 1T 5-Gram Version 1, which was constructed by processing virtually all documents retrieved using Google, already exists. However, this data contain unfocused word recommendations, therefore, they are not suitable. Consequently, we are constructing a scholarly n-gram. In this paper, we demonstrate the efficiency of n-gram using Web 1T unigram and introduce and discuss the specifics of our research plan related to scholarly n-gram.
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