Towards Intelligent Text Mining Under Limited Linguistic Resources

Abstract

The establishment of new techniques or improvements in established core techniques to extract knowledge from the text document(s) by using limited linguistic resources is a challenging task of significant interest. The demand of such techniques is due to (1) the heavy increase in the size and variety of text resources, (2) the continuous arrival of text resources having different languages and different levels of computational capabilities and (3) the increase in the demand of variety of information needs. newline newline The graph based automated text analysis and text mining methods have received a great deal of attention in solving these issues. Actually, an important aspect of graph-based method is that it does not require deep linguistic knowledge, nor domain or language specific annotated corpora, which makes it highly portable to other domains, genres, or languages. The development of advanced graph theoretical techniques for social media mining has also enriched this area. newline newlineBased on the above discussed facts, we have identified some core issues (and techniques for them) like: (i) meaningful phrase identification (ii) differentiating role and sense of words, preferably via a single measure, (iii) handling information gap at the phrase level by using unsupervised scheme, (iv) integrating the importance of words as a core feature and (v) identifying group semantics and/or logically related features, (vi) sentence abstraction and so on. newline newlineThese techniques are very useful for multiple text mining applications like: (a) Document summarization, (b) Summarization Evaluation, (c) Document Clustering, (d) Key phrase Extraction and (e) Automatic Question Answering. The effective improvement in the results of our devised applications, over state-of-the-arts supervised, unsupervised applications, which use linguistic support and domain knowledge etc., prove the effectiveness of the proposed techniques.