Semantic Unsupervised Automatic Keyphrases Extraction by Integrating Word Embedding with Clustering Methods

Multimodal Technologies and Interaction

MariaTeresa Artese¹  Isabella Gagliardi¹ 

[1] Institute for Applied Mathematics and Information Technologies “Enrico Magenes” (IMATI), National Research Council—CNR, Via Bassini, 15, 20133 Milan, Italy;

关键词: unsupervised automatic keyword extraction;    clustering algorithms;    word embedding models;    Italian datasets;    information retrieval;    evaluation;   

DOI  :  10.3390/mti4020030

来源: DOAJ

【 摘 要 】

Increasingly, the web produces massive volumes of texts, alone or associated with images, videos, photographs, together with some metadata, indispensable for their finding and retrieval. Keywords/keyphrases that characterize the semantic content of documents should be, automatically or manually, extracted, and/or associated with them. The paper presents a novel method to address the problem of the automatic unsupervised extraction of keywords/phrases from texts, expressed both in English and in Italian. The main feature of this approach is the integration of two methods that have given interesting results: word embedding models, such as Word2Vec or GloVe able to capture the semantics of words and their context, and clustering algorithms, able to identify the essence of the terms and choose the more significant one(s), to represent the contents of a text. In the paper, the datasets used are presented, together with the method implemented and the results obtained. These results will be discussed, commented, and compared with those obtained in previous experimentations, using TextRank, Rapid Automatic Keyword Extraction (RAKE), and TF-IDF.

【 授权许可】

Unknown