Keyword Extraction in News Articles Using Table based K Nearest Neighbors

In this research, we propose the KNN version where words are encoded into tables, instead of numerical vectors, as the approach to the keyword extraction. The keyword extraction is mapped into a binary classification task within a domain, and the task should be distinguished from the topic based word categorization. In this research, words are encoded into tables each of which consists of entries of text identifiers and their weights, the KNN algorithm is modified by adopting the proposed similarity metric, and it is applied to the keyword extraction which is mapped into a binary classification. It is validated empirically that the proposed KNN version is better than the traditional version in extracting keywords from a text which is tagged with its own domain. In future, we will connect the task with the text categorization, in order to process texts which are untagged with their domains.

I. Introduction

The keyword extraction is referred to the process of extracting important words which reflect essentially the content from a text, automatically. Even if the indexing is the process of extracting words from a text is similar as the task, the two tasks should be distinguished from each other. In this research, the keyword extraction is viewed into a binary classification where each word is classified into keyword or non-keyword, and the modified KNN version is proposed as the approach. A text is indexed into a list of words, they are classified into one of the two categories, and the words which are classified into keyword are extracted as the output. In this section, we briefly describe the motivation, the idea, and the validation of this research.