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

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

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 this paper we report our performance at DUC 2007 summarization tasks. We participated both in the query-focused multidocument summarization main task and in a pilot update summary generation tasks. This year we used a term clustering approach to better estimate a sentence prior. We used only the sentence prior which is query independent, in the update summarization task and found that it’s performance is comparable with the top performing systems. In the main task our system ranked 1 in ROUGE-2, ROUGE-SU4 and ROUGE-BE scores as well as in pyramid scores.

Abstract

This paper reports on the first Oromo-English CLIR system that is based on dictionary-based query translation techniques. The basic objective of the study is to design and develop an Oromo-English CLIR system with a view to enable Afaan Oromo speakers to access and retrieve the vast online information sources that are available in English by using their own (native) language queries. We describe the major approaches and procedures that have been used in designing and developing Oromo-English CLIR system together with the information retrieval evaluation experiments recently conducted at CLEF 2006 ad hoc track. The purpose of the current initial evaluation experiments was to assess the over all performance of the Oromo-English CLIR system by using different fields of Afaan Oromo topics. Thus we submitted three official runs (experiments) that differed in terms of utilized fields in the topic set, ie title run (OMT), title and description run (OMTD), and title, description and narration run (OMTDN) to CLEF 2006. Since appropriate online language processing resources and information retrieval tools are not available for Afaan Oromo, only limited linguistic resources such as Oromo-English dictionary and Afaan Oromo stemmer that had been designed and developed at our research center were used in conducting the experiments. Yet we found the performances of our evaluation experiments at CLEF 2006 very encouraging which is a good news for development and application of CLIR systems in other indigenous African languages.

Abstract

The twin challenges of massive information overload via the web and ubiquitous computers present us with an unavoidable task: developing techniques to handle multilingual information robustly and efficiently, with as high quality performance as possible. Previous research activities on multilingual information access systems have studied cross-language information retrieval (CLIR), information extraction and factoid based question answering tasks in detail. It is believed by the research community and also previously acknowledged in a US-NSF report [Hovy et al., 1999] that a cross-language query focused summarization could play a vital role in multilingual information access, as a bridge between CLIR and machine translation. Surprisingly enough, no detailed studies exist yet on the effects of cross-linguality to query focused summarization. In this paper we study the effects of adding a cross-language dimension to query focused multi-document summarization for the Telugu-English language pair. We use a cross-lingual relevance based language modeling approach to generate extraction based summary. We evaluate the system using DUC1 2005 dataset using ROUGE2 metrics and compare with the mono-lingual baseline which uses relevance based language modeling in mono-lingual setting.

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 fuzzy 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.

Abstract

This chapter describes the role of ontologies and corporate taxonomies in managing the content and knowledge within organizations. Managing content in a reusable and effective manner is becoming increasingly important in knowledge centric organizations as the amount of content generated, both text based and rich media, is growing exponentially. Search, categorization and document characterization, content staging and content delivery are the key technology challenges in knowledge management systems. This chapter describes how corporate taxonomies and ontologies can help in making sense of huge amount of content that gets generated across the locations in different languages and formats Different information silos can be connected and workflow and collaboration can be achieved using ontologies. As the KM solutions are moving from a centralized approach to a distributed approach, a framework where multiple taxonomies and ontologies can co-exist with uniform interfaces is needed.

Abstract

An indexing model is the heart of an Information Retrieval (IR) system. Data structures such as term based inverted indices have proved to be very effective for IR using vector space retrieval models. However, when functional aspects of such models were tested, it was soon felt that better relevance models were required to more accurately compute the relevance of a document towards a query. It was shown that language modeling approaches [1] in monolingual IR tasks improve the quality of search results in comparison with TFIDF [2] algorithm. The disadvantage of language modeling approaches when used in monolingual IR task as suggested in [1] is that they would require both the inverted index (term-todocument) and the forward index (document-to-term) to be able to compute the rank of document for a given query. This calls for an additional space and computation overhead when compared to inverted index models. Such a cost may be acceptable if the quality of search results are significantly improved. In a Cross-lingual IR (CLIR) task, we have previously shown in [3] that using a bilingual dictionary along with term co-occurrence statistics and language modeling approach helps improve the functional IR performance. However, no studies exist on the performance overhead in a CLIR task due to language modeling. In this paper we present an augmented index model which can be used for fast retrieval while having the benefits of language modeling in a CLIR task. The model is capable of retrieval and ranking with or without query expansion techniques using term collocation statistics of the indexed corpus. Finally we conduct performance related experiments on our indexing model to determine the cost overheads on space and time

Abstract

Search engines today often return a large volume of results with possibly a few relevant results. The notion of relevance is subjective and depends on the user and context. Re-ranking of the results to reflect the most relevant results to the user using the relevance feedback has received wide attention in information retrieval in recent years. Also, sharing of information among users having similar interests using collaborative filtering techniques has achieved wide success in recommendation systems. In this paper, we propose a novel approach for re-ranking of the search results using collaborative filtering techniques using relevance feedback of a given user as well as the other users. Our approach is to learn the profiles of the users using machine learning techniques making use of past browsing histories including queries posed and documents found relevant or irrelevant. Re-ranking of the results is done using collaborative filtering techniques. First, the context of the query is inferred from the query category. The user’s community is determined dynamically in the context of the query by using the user profiles. The rank of a document is calculated using the user’s profile as well profiles of the other users in the community