Publication Date: 2022/05/23
Abstract: Large amounts of text data is generated on a daily basis through social media posts, reviews, emails, blogs and search queries etc. Most of this text data is unstructured. To help make sense of this large amount of text data we need keyword extraction which helps in obtaining the important word(keywords) or important phrases(key phrases) without having to go through all the text data ourselves. However over the years it has been found to be quite difficult to extract keywords from short text (text spanning across one or maybe two sentences) and many of the traditional methods such as classification, RAKE, TextRank and TF-IDF have been found to be not as effective as we would wish them to be. In this paper, we compare the traditional methods and also propose an new Neural Network based algorithm, such as the sequence to sequence based encoder-decoder model which we show in this paper, for better keyword extraction form short text. We conduct some preliminary application based investigation on some sentences, which show the superiority of neural network method and can also form the basis of future research.
Keywords: extraction, text, data, short text, neural network, NLP
DOI: https://doi.org/10.5281/zenodo.6571341
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT22APR1084_(1).pdf
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