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

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

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

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

Retrieving answer containing passages is a challenging task in Question Answering. In this paper, we describe a novel passage retrieval methodology using answer type profiles. Our methodology includes two steps: estimation and ranking. In the estimation step, answer type profiles are constructed from question-answer sentence pairs parallel corpus using a statistical alignment model. Each answer type profile consists of triples: the query word, the answering sentence word and the probability of translation. In the ranking step, answer type profiles are incorporated into the Language Modeling framework called Statistical Machine Translation models for Information Retrieval. Using this framework a set of relevant passages are retrieved, given a question. We conducted experiments on FACTOID questions from TREC 2002 to 2006 QA tracks. The experimental results showed significant improvements over different retrieval models including TFIDF, Okapi BM25, Indri and KL-divergence.

Abstract

Indian software industry has set up huge growth targets for future. These targets would be heavily affected by the software engineering (SE) education scenario in the country. The purpose of this paper is to provide a holistic understanding of SE education issues and challenges specific to Indian context, from both industry and academic perspective. This study is based on our (a) interaction with industry through SE education related projects, surveys and discussions. (B) Observations as an integral part of Indian SE educators' community. There is an urgent need for addressing these deep rooted issues, as the lack of proper SE education may be the single largest factor that may negatively affect the industry. Understanding these issues will help to identify the action items that initiate software engineering educational reforms in the country. We also discuss the essential and minimal set of SE knowledge, skills and dispositions that the industry expects from engineers willing to join the industry. Though Indian software industry is growing at phenomenal rates, there not many studies on the issues and effects associated with SE education in the Indian context. Though the discussion is limited to India, but we believe that it represents the existing conditions in many developing countries where IT and ITES (IT Enabled Services) industry is becoming important.

Abstract

Software Engineering (SE) and Information Technology (IT) jobs are the most sought after career options for Indian youth in the recent times. Indian Software industry is expected to grow at a very healthy rate and each major software company has ambitious plans and growth targets for future. However, lack of proper Software Engineering (SE) education may have severe consequences and may negatively affect these growth targets. In this paper we discuss challenges and issues related to software engineering education and training in Indian academia and industry. These are based on our interaction with industry and through our experience as Software engineering educators. These challenges arise from deep rooted issues in Software Engineering educational goals, pedagogy and instruction as well as the infrastructure. We will discuss their long term effects on various aspects of software development. We put forth our suggestions that may handle these challenges to an extent. We also discuss the essential and minimal set of software engineering knowledge, skills and dispositions that the industry requires from young engineers willing to join the industry. This paper provides course designing guidelines for the academia and learning centers of the industry by focusing on important SE education issues, their causes and possible solutions. This, in turn would help to make SE Education more effective and inline with requirements of the Indian Software industry.

Abstract

It is observed that information technology (IT) is being used to automate the traditional government processes and to achieve easy access of the information. They are developed to increase the efficiency of the government departments and represent their needs. As a result, they achieved a huge inventory of IT applications providing disparate processes implemented using different technologies and platforms. It thus became very important to use IT as an" integrator" allowing disparate processes running in different departments to interconnect. This helps the citizen to access different government services through a common single-point window and helps the government to transform from government-centric organization to citizen-centric organization

Abstract

In this paper we present a statistical transliteration technique that is language independent. This technique uses Hidden Markov Model (HMM) alignment and Conditional Random Fields (CRF), a discriminative model. HMM alignment maximizes the probability of the observed (source, target) word pairs using the expectation maximization algorithm and then the character level alignments (n-gram) are set to maximum posterior predictions of the model. CRF has efficient training and decoding processes which is conditioned on both source and target languages and produces globally optimal solutions. We apply this technique for Hindi-English transliteration task. The results show that our technique perfoms better than the existing transliteration system which uses HMM alignment and conditional probabilities derived from counting the alignments.

Abstract

This paper presents the experiments of Language Technologies Research Centre (LTRC) as part of their participation in CLEF2 2007 Indian language to English ad-hoc cross language document retrieval task. In this paper we discuss our Hindi and Telugu to English CLIR system and the experiments using CLEF 2007 dataset. We used a variant of TFIDF algorithm in combination with a bilingual lexicon for query translation. We also explored the role of a document summary in fielded queries and two different boolean formulations of query translations. We find that a hybrid boolean formulation using a combination of boolean AND and boolean OR operators improves ranking of documents. We also find that simple disjunctive combination of translated query keywords results in maximum recall.