Abstract

With the growth of web, people are using it as a medium for expressing their opinions, thoughts through blog posts, reviews (in the form of ratings), and forums. Blogosphere is a place where people read, write their views and make comments on others views or thoughts there by exchanging information. It will be very difficult for any business, organization or individual to go through and understand thoughts expressed by others on a product or topic which they are interested in. Hence a summarization system which extracts, analyze and summarize opinions will be useful. Our Summarization system exactly does the same for blog posts. The entire process of summary generation is done in three stages, extract sentences which are sources for opinion (Opinion Mining), then analyze the extracted opinions to determine polarity (Opinion Analysis) and finally ranking the opinion sentences (Opinion Summarization). We present a classification based approach for extracting and analyzing opinions and how we use this approach to rank sentences for generating summary.

Abstract

This work focuses on attribute extraction from product descriptions. We propose a novel solution to extract attributes of a product from a set of text documents. A graph is constructed from the text using word co-occurrence statistics. We compute word clusters and extract attributes from these clusters using graph based methods. Our solution is able to achieve nearly 80% precision and 45% recall. Experiments show that the methods employed are effective in identifying attributes for different dataset sizes.

Abstract

Summarization is the process of condensing a piece of text while retaining important information. A well composed and coherent summary is the solution for information overload. Sentence extractive summarization system requires different features to rank sentences and then generate summaries. In this paper we provide a detailed analysis about effect of various features in context of update summarization. We adapt a machine learning algorithm for combining features while scoring a sentence. Further, we propose a new feature that can effectively capture novelty along with relevancy of a sentence in a topic. Evaluation results show that our summmarizer is able to surpass top performing systems participated at Text analysis conference 2008. Gap between oracle summaries and state of art summaries is analyzed to depict the scope of improvement in sentence extractive summarization.

Abstract

In this paper, we report our participation in Update Summarization, Knowledge Base Population and Recognizing Textual Entailment at TAC 2009. This year, we enhanced our basic summaization system with support vector regression to better estimate the combined affect of different features in ranking. A Novelty measure is devised to effectively capture relevance and novelty of a term. For Knowledge Base Population, we analyzed IR approaches and Naive Bayes Classification with Phrase and Token searches. Finally for RTE, we built templates using WordNet and predict entailments.

Abstract

In this paper, we describe an approach for creating semantic wiki pages from regular wiki pages, in the domain of scientific museums, using information extraction methods in general and named entity recognition in particular. We make use of a domain specific ontology called CIDOC-CRM as a base structure for representing and processing knowledge. We have described major components of the proposed approach and a three-step process involving name entity recognition, identifying domain classes using the ontology and establishing the properties for the entities in order to generate semantic wiki pages. Our initial evaluation of the prototype shows promising results in terms of enhanced efficiency and time and cost benefits.

Abstract

This paper talks about the system which we have submitted for the ResPubliQA task. We participated in building the QA system for en-en part. We followed a different method for each question type. In this paper we outline the methods which we adapted and the results which we obtained.

Abstract

Software engineering (SE) courses aim to make students well-versed in solving authentic and complex problems by applying varied SE knowledge skills along with problem solving, critical thinking, use of tools, communication skills etc. Thus they have multiple, complex and some higher order cognitive learning goals. Traditional assessment tools like multiple choice questions, subjective questions, etc., may not always be an optimal choice for such a SE course and may not satisfy learning sciences principle and guidelines of assessment design. Case studies in SE have been used since long for research and investigation, recently in teaching, but not as assessment tools. In this paper, we propose and examine the use of SE case studies as assessment tools. We also describe an approach for objective and easy evaluation of solutions. This work reasons that carefully designed case studies can be effective as well as efficient assessment tools for SE education such that they are not only closely aligned with learning goals, but are also in accordance with learning sciences.

Abstract

A software architect engages in solving Software Engineering (SE) problems throughout his career. Thus inculcating problem solving skills should be one of the learning objectives of SE academic and training programs. But structured problem solving is usually latent or missing in most of the current curriculum. In this paper, we describe an effective learning environment for SE education and training with problem solving as an integral part.

Abstract

Automatic Document summarization is proving to be an increasingly important task to overcome the information overload. The primary task of document summarization process is to pick subset of sentences as a representative of whole document set. We treat this as a decision making problem and estimate the risk involve in making this decision. We calculate the risk of information loss associated with each sentence and extract sentences based on ascending order of their risk. The experimental result shows that the proposed approach performs better than various state of the art approaches.

Abstract

A new method for sentence extraction on the basis of language model with relative entropy is presented in this paper. The proposed technique first builds a sentence language model and document cluster language model respectively for the sentence and the documents. The sentences are then ranked according to the relative entropies of the estimated document language model with respect to the estimated sentence language model. The overall results on DUC and MSE corpus demonstrate that the proposed approach outperforms some of the best reported results for generic multi-document summarization.