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

Graphs can be used for effective representation of images for recognition and retrieval purposes. The problem is often to find a proper structure that can efficiently describe an image and can be matched in reasonably low computational expense. The standard solutions to the graph matching problem are computationally expensive since the search space involves all permutations of the nodesets. We compare two graphical representations called the Nearest-Neighbor Graphs and the Collocation Trees, for the goodness of fit and the computational expense involved in matching. Various schemes to index the graphical structures have also been discussed.

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.

Abstract

The Oxford/IIIT team participated in the high-level feature extraction and interactive search tasks. A vision only approach was used for both tasks, with no use of the text or audio information. For the high-level feature extraction task, we used two different approaches, both based on a combination of visual features. One used a SVM classifier using a linear combination of kernels, the other used a random forest classifier. For both methods, we trained all high-level features using publicly available annotations [3]. The advantage of the random forest classifier is the speed of training and testing. In addition, for the people feature, we took a more targeted approach. We used a real-time face detector and an upper body detector, in both cases running on every frame. Our best performing submission, C OXVGG 1 1, which used a rank fusion of our random forest and SVM approach, achieved an mAP of 0.101 and was above the median for all but one feature. In the interactive search task, our team came third overall with an mAP of 0.158. The system used was identical to last year with the only change being a source of accurate upper body detections.

Abstract

Web search personalization has been well studied in the recent few years. Relevance feedback has been used in various ways to improve relevance of search results. In this paper, we propose a novel usage of relevance feedback to effectively model the process of query formulation and better characterize how a user relates his query to the document that he intends to retrieve using a noisy channel model. We model a user profile as the probabilities of translation of query to document in this noisy channel using the relevance feedback obtained from the user. The user profile thus learnt is applied in a re-ranking phase to rescore the search results retrieved using an underlying search engine. We evaluate our approach by conducting experiments using relevance feedback data collected from users using a popular search engine. The results have shown improvement over baseline, proving that our approach can be applied to personalization of web search. The experiments have also resulted in some valuable observations that learning these user profiles using snippets surrounding the results for a query gives better performance than learning from entire document collection

Abstract

Many events are captured using multiple cameras today. Frames of each video stream have to be synchronized and aligned to a common time axis before processing them. Synchronization of the video streams necessarily needs a hardware based solution that is applied while capturing. The alignment problem between the frames of multiple videos can be posed as a search using traditional measures for image similarity. Multiview relations and constraints developed in Computer Vision recently can provide more elegant solutions to this problem. In this paper, we provide two solutions for the video frame alignment problem using two view and three view constraints. We present solutions to this problem for the case when the videos are taken using affine cameras and for general projective cameras. Excellent experimental results are achieved by our algorithms.

Abstract

This paper discourses our CLIR experiments performed for the FIRE1 workshop. We had submitted our runs for Adhoc monolingual document retrieval in Hindi and English, and Ad-hoc cross-lingual document retrieval from Hindi to English, and English to Hindi. In this paper, we describe our English to Hindi and Hindi to English CLIR systems and the experiments conducted on them using the FIRE-2008 dataset. We had used a dictionary-based approach of query translation and transliteration of named entities in the queries using a mapping-based, Compressed Word Format (CWF) algorithm [1]. Disjunctive Query formulation with different scoring weights based on the part of the topic from which they originated gave us overall better performance.

Abstract

Named Entity Recognition (NER) is the task of identifying and classifying tokens in a text document into predefined set of classes. In this paper we show our experiments with various feature combinations for Telugu NER. We also observed that the prefix and suffix information helps a lot in finding the class of the token. We also show the effect of the training data on the performance of the system. The best performing model gave an Fβ= 1 measure of 44.91. The language independent features gave an Fβ= 1 measure of 44.89 which is close to Fβ= 1 measure obtained even by including the language dependent features.

Abstract

This work aims to enhance the matching and retrieval performance over image datasets which have similar spatial structures that occur very frequently. Instead of treating images as bags of features, we try to encode the spatial relationships in the representation. This process would help to resolve the ambiguity when two classes of images have similar sets of features although in different spatial arrangements. To demonstrate the fact a sizeable dataset of license plate images is used. We have proposed a method to use graphs to encode the spatial relationships among features. The problem of image matching thus turns to finding the maximum similarity between labelled graphs. It is shown that the precision of the retrieved results increases with this matching scheme since most of the false matches are eliminated.