Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/120959
Title: Towards Intelligent Text Mining Under Limited Linguistic Resources
Researcher: Niraj Kumar
Guide(s): Dr. Kannan Srinathan, Dr. Vasudeva Varma
Keywords: Automatic Question Answering
Automatic Summarization Evaluation
Document Clustering
Document Summarization
Information Retrieval
Keyphrase Extraction
Text Mining
University: International Institute of Information Technology, Hyderabad
Completed Date: 03/06/2015
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.
Pagination: xvi, 160
URI: http://hdl.handle.net/10603/120959
Appears in Departments:Computer Science and Engineering

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04_contents.pdf111.42 kBAdobe PDFView/Open
05_preface.pdf203.78 kBAdobe PDFView/Open
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07_chapter 1.pdf528.35 kBAdobe PDFView/Open
08_chapter 2.pdf402.09 kBAdobe PDFView/Open
09_chapter 3.pdf749.27 kBAdobe PDFView/Open
10_chapter 4.pdf482.23 kBAdobe PDFView/Open
11_chapter 5.pdf496 kBAdobe PDFView/Open
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13_chapter 7.pdf540.51 kBAdobe PDFView/Open
14_chapter 8.pdf616.25 kBAdobe PDFView/Open
15_chapter 9.pdf230.37 kBAdobe PDFView/Open
16_references.pdf166.5 kBAdobe PDFView/Open


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