Abstract

Cross Language Information Retrieval (CLIR) between languages of the same origin is an interesting topic of research. The similarity of the writing systems used for these languages can be used effectively to not only improve CLIR, but to overcome the problems of textual variations, textual errors, and even the lack of linguistic resources like stemmers to an extent. We have conducted CLIR experiments between three languages which use writing systems (scripts) of Brahmi-origin, namely Hindi, Bengali and Marathi. We found significant improvements for all the six language pairs using a method for fuzzy text search based on Surface Similarity. In this paper we report these results and compare them with a baseline CLIR system and a CLIR system that uses Scaled Edit Distance (SED) for fuzzy string matching.

Abstract

In the context of the Document Understanding Conferences, the task of Query-Focused Multi-Document Summarization is intended to improve agreement in content among humangenerated model summaries. Query-focus also aids the automated summarizers in directing the summary at specific topics, which may result in better agreement with these model summaries. However, while query focus correlates with performance, we show that highperforming automatic systems produce summaries with disproportionally higher query term density than human summarizers do. Experimental evidence suggests that automatic systems heavily rely on query term occurrence and repetition to achieve good performance.

Abstract

Document Retrieval assumes that a document is independent of its relevance, and non-relevance. Previous works showed that the same assumption is being considered for passage retrieval in the context of Question Answering. In this paper, we relax this assumption and describe a method for estimating the prior of a passage being relevant, and non-relevant to a question. These prior probabilities are used in the process of ranking passages. We also describe a trivial method for identifying relevant and nonrelevant text to a question using the Web and AQUAINT corpus as information sources. An empirical evaluation on TREC 2006 Question Answering test set showed that in the context of Question Answering prior probabilities are necessary in ranking the passages.

Abstract

Retrieving answer containing passages is a challenging task in Question Answering. In this paper we describe a novel query expansion method which aims to rank the answer containing passages better. It uses content and structured information (link structure and category information) of Wikipedia to generate a set of terms semantically related to the question. As Boolean model allows a fine-grained control over query expansion, these semantically related terms are added to the original query to form an expanded Boolean query. We conducted experiments on TREC 2006 QA data. The experimental results show significant improvements of about 24.6%, 11.1% and 12.4% in precision at 1, MRR at 20 and TDRR scores respectively using our query expansion method.

Abstract

In this paper we present a statistical transliteration technique that is language independent. This technique uses statistical alignment models and Conditional Random Fields (CRF). Statistical alignment models maximizes the probability of the observed (source, target) word pairs using the expectation maximization algorithm and then the character level alignments 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 solution.

Abstract

Many knowledge workers are increasingly using online resources to find out latest developments in their specialty and articles of interest. To extract relevant information from such multiple online information sources summarization is being used. Current summarization systems produce a uniform version of summary for all users. However summaries which are generic in nature do not cater to the userpsilas background and interests. In this paper we propose to make the summarization process user specific and present a design for generating personalized summaries of online articles that are tailored to each personpsilas interest. The userpsilas data available on Web is used for model their background and interest. A controlled user-centered qualitative evaluation carried out on news articles of science and technology domain, indicates better user satisfaction with personalized summaries compared to generic summaries.

Abstract

In recent times, information technology (IT) has a tremendous influence on how different Indian government departments operate. At one level it has given rise to a requirement for timely delivery of information and services to the citizen round the clock. At a higher level, it enables the citizen to conduct transactions through government portals. This changed the purpose of the government portals from primarily a place to advertise the success stories of government to an effective means to provide efficient services to the citizen. As the needs of the citizen shift and IT capabilities evolve, government departments are under increasing pressure to become more innovative - both in terms of government processes and their delivery model. One of the important factors to provide efficient government services is availability of all government services through a single point delivery platform. The government departments should have an integrated platform with a secured access control system. It is thus important to design a coherent enterprise architecture framework for the government departments. The aim of this research paper is to outline an effective enterprise architecture framework and an innovative technological solution that can serve as the common platform for provision of all government services to the citizen of India.

Abstract

A cross language query based multi-document summarization task requires a system to produce a brief, coherent and readable summary serving the information need represented in the query. In this paper we present a Cross Language Query Based Multi Document Summarization system for Telugu to English language pair. The topics being in Telugu and the documents from which summary is to be generated are in English. The system uses a Telugu to English bilingual lexicon to obtain translations for each word as well as uses some transliteration techniques and a target language relevance based language modeling using Hyper Space Analogue to Language. The system was evaluated using DUC 2005 topics translated into Telugu and the English model summaries provided at DUC 2005 and the results are discussed in the evaluation section.

Abstract

Named Entity Recognition (NER) is the task of identifying and classifying all proper nouns in a document as person names, or—ganization names, location names, date & time expressions and miscellaneous. Previ—ous work (Cucerzan and Yarowsky, 1999) was done using the complete words as fea—tures which suffers from a low recall prob—lem. Character n—gram based approach (Klein et al., 2003) using generative mod—els, was experimented on English language and it proved to be useful over the word based models. Applying the same technique on Indian Languages, we experimented with Conditional Random Fields (CRFs), a dis—criminative model, and evaluated our sys—tem on two Indian Languages Telugu and Hindi. The character n—gram based models showed considerable improvement over the word based models. This paper describes the features used and experiments to increase the recall of Named Entity Recognition Systems which is also language independent

Abstract

Commonly used vocabulary in Indian language documents found on the web contain a number of words that have Sanskrit, Persian or English origin. However, such words may be written in different scripts with slight variations in spelling and morphology. In this paper we explore approximate string matching techniques to exploit this situation of relatively large number of cognates among Indian languages, which are higher when compared to an Indian language and a non-Indian language. We present an approach to identify cognates and make use of them for improving dictionary based CLIR when the query and documents both belong to two different Indian languages. We conduct experiments using a Hindi document collection and a set of Telugu queries and report the improvement due to cognate recognition and translation.