Abstract-World Wide Web (WWW) which is predominant source for Information Retrieval today (IR) is essentially a set of hyperlinked documents. A web page containing more number of related hyperlinks satisfy the user needs in a single page. The IR systems should give high priority to such web pages. While assigning a rank for a web page, existing web mining techniques such as Hypertext Induced Topic Selection (HITS) and Page Ranking algorithms focus on the number of in links and out links present in the web page. Instead of just relying on the number of links present in the web page, the discovery of semantic relations between the web page and the hyperlinks present in the web page can improve the quality of the IR systems. The Rhetorical Structure Theory (RST) is widely used to find the semantic relations between text fragments by analysing the discourse structure of a text. In this paper, we propose a novel approach to find the semantic relation between a web page and the links present in the web page using RST. The proposed approach uses RST based discourse relations to find the relation between a web page and the hyperlinks present in the web page. We have implemented and evaluated our approach on an IR system using 500 Tamil language and 50 English tourism domain specific web pages. A comparison between the proposed approach and an existing page ranking algorithm has also been done.

Tamil literature has many valuable thoughts that can help the human community to lead a successful and a happy life. Tamil literary works are abundantly available and searched on the World Wide Web (WWW), but the existing search systems follow a keyword-based match strategy which fails to satisfy the user needs. This necessitates the demand for a focused Information Retrieval System that semantically analyses the Tamil literary text which will eventually improve the search system performance. This paper proposes a novel Information Retrieval framework that uses discourse processing techniques which aids in semantic analysis and representation of the Tamil Literary text. The proposed framework has been tested using two ancient literary works, the Thirukkural and Naladiyar, which were written during 300 BCE. The Thirukkural comprises 1330 couplets, each 7 words long, while the Naladiyar consists of 400 quatrains, each 15 words long. The proposed system, tested with all the 1330 Thirukkural couplets and 400 Naladiyar quatrains, achieved a mean average precision (MAP) score of 89%. The performance of the proposed framework has been compared with Google Tamil search and a keyword-based search which is a substandard version of the proposed framework. Google Tamil search achieved a MAP score of 56% and keyword-based method achieved a MAP score of 62% which shows that the discourse processing techniques improves the search performance of an Information Retrieval system.

Thirukkural, a Tamil classic literature, which was written in 300 BCE is a didactic literature. Though Thirukkural comprises 1330 couplets which are organized into three sections and 133 chapters, in order to retrieve meaningful Thirukkural for a given query in search systems, a better organization of the Thirukkural is needed. This paper lays such a foundation by classifying the Thirukkural into ten new categories called superclasses that is helpful for building a better Information Retrieval (IR) system. The classifier is trained using Multinomial Naïve Bayes algorithm. Each superclass is further classified into two subcategories based on the didactic information. The proposed classification framework is evaluated using precision, recall and F-score metrics and achieved an overall F-score of 82.33% and a comparison analysis has been done with the Support Vector Machine, Logistic Regression and Random Forest algorithms. An IR system is built on top of the proposed system and the performance comparison has been done with the Google search and a locally built keyword search. The proposed classification framework has achieved a mean average precision score of 89%, whereas the Google search and keyword search have yielded 59% and 68% respectively.

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