A method for automatic detection of acronyms in texts and building a dataset for acronym disambiguation

被引：3

作者：

Azimi, Sasan ^{[1
]}

Veisi, Hadi ^{[1
]}

Amouie, Reyhaneh ^{[1
]}

机构：

[1] Univ Tehran, Tehran, Iran

来源：

2019 5TH IRANIAN CONFERENCE ON SIGNAL PROCESSING AND INTELLIGENT SYSTEMS (ICSPIS 2019) | 2019年

关键词：

Acronym disambiguation; Tech-mining; Text Mining; Natural Language Processing;

D O I：

10.1109/icspis48872.2019.9066084

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Nowadays, there is an increasing tendency for using acronyms in technical texts, which has led to ambiguous acronyms with different possible expansions. Diversity of expansions of a single acronym makes recognizing its expansion a challenging task. Replacing acronyms with incorrect expansions will lead to problems in text mining procedures, namely text normalization, summarization, machine translation, and tech-mining. Tech-mining involves exploring and analyzing technical texts to recognize the relations between technologies. This paper is aimed at proposing a method for building a dataset that meets the requirements for training acronym disambiguation models in technical texts. In this paper, challenges in automatic acronym disambiguation are presented. We have proposed a method for building the dataset and the accuracy of the acronym disambiguation model is 86%.

引用

页数：4

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