Abstract

Social networking sites provide users a virtual community interaction platform to share their thoughts, life experiences and opinions. Online debate forum is one such platform where people can take a stance and argue in support or opposition of debate topics. An important feature of such forums is that, they are dynamic and increase rapidly. In such situations, e↵ective opinion summarization approaches are needed so that readers need not go through the entire debate. This paper aims to summarize online debates by extracting highly topic relevant and sentiment rich sentences. The proposed approach takes into account topic relevant, document relevant and sentiment based features to capture topic opinionated sentences. ROUGE scores are used to evaluate our system. Our system significantly outperforms several baseline systems and show 5.2% (ROUGE-1), 7.3%(ROUGE-2) and 5.5% (ROUGE-L) improvement over the state-of-the-art opinion summarization system. The results verify that topic directed sentiment features are most important to generate e↵ective debate summaries.

Abstract

This paper presents a novel approach to categorize, model and identify contextual information in natural language interface to database (NLIDB) systems. The interactions between user and system are categorized and modeled based on the way in which the contextual information is utilized in the interactions. A relationship schema among the responses (user and system responses) is proposed. We present a novel method to identify contextual information in one specific type of usersystem interaction. We report on results of experiments with the university related queries.

Abstract

In this paper, we study the usefulness of the best path and the complete trellis in dynamic programming based template matching approach for detecting pronunciation mismatch. We show that there exists cues in trellis (a matrix representing all paths), which could be exploited for detecting pronunciation mismatch. Such an approach could be used to build a template based approach for detecting pronunciation mismatch independent of the language

Abstract

Sandhi splitting is a primary and an important step for any natural language processing (NLP) application for languages which have agglutinative morphology. This paper presents a statistical approach to build a sandhi splitter for agglutinative languages. The input to the model is a valid string in the language and the output is a split of that string into meaningful word/s. The approach adopted comprises of two stages namely Segmentation and Word generation, both of which use conditional random fields (CRFs). Our approach is robust and language independent. The results for two Dravidian languages viz. Telugu and Malayalam show an accuracy of 89.07% and 90.50% respectively