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In this paper, we present the evaluation of our CLIR system performed as part of our participation in FIRE 2008. We participated in Hindi to English, Marathi to English, English to Hindi bilingual task and English, Hindi, Marathi mono-lingual task. We take a query translation based approach using bilingual dictionaries. Query words not found in the(More)
Generic rule-based systems for Information Extraction (IE) have been shown to work reasonably well out-of-the-box, and achieve state-of-the-art accuracy with further domain customization. However, it is generally recognized that manually building and customiz-ing rules is a complex and labor intensive process. In this paper, we discuss an approach that(More)
Distant supervision, a paradigm of relation extraction where training data is created by aligning facts in a database with a large unannotated corpus, is an attractive approach for training relation extractors. Various models are proposed in recent literature to align the facts in the database to their mentions in the corpus. In this paper, we discuss and(More)
Fast algorithms of a transform, like fast Fourier transform (FFT) algorithms, are based on different decomposition techniques. It is shown that these decomposition techniques can also be applied to the computation of the discrete Hartley transform (DHT) for a real-valued sequence. Recently, an efficient decomposition technique for radix-3 decimation-in-time(More)
We describe a novel max-margin learning approach to optimize non-linear performance measures for distantly-supervised relation extraction models. Our approach can be generally used to learn latent variable models under multivariate non-linear performance measures, such as F β-score. Our approach interleaves Concave-Convex Procedure (CCCP) for populating(More)
Emotion analysis, a recent sub discipline at the crossroads of information retrieval and computational linguistics is becoming increasingly important from application viewpoints of affective computing.Emotion is crucial to identify as it is not open to any objective observation or verification. In this paper, emotion analysis on blog texts has been carried(More)
Automatic short answer grading (ASAG) techniques are designed to automatically assess short answers written in natural language having a length of a few words to a few sentences. In this paper, we report an intriguing finding that the set of short answers to a question, collectively, share significant lexical commonalities. Based on this finding, we propose(More)
This paper is an attempt to raise pertinent questions and act as platform to generate fruitful discussions within the AKBC community about the need for a large scale dataset for relation extraction. For proper training and evaluation of relation extraction tasks, the weaknesses of datasets used so far need to be tackled: mainly the size (too small) and the(More)