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Finding good enough: a task-based evaluation of query biased summarization for cross language information retrieval

Published in:
EMNLP 2014, Proc. of Conf. on Empirical Methods in Natural Language Processing, 25-29 October, 2014, pp. 657-69.

Summary

In this paper we present our task-based evaluation of query biased summarization for cross-language information retrieval (CLIR) using relevance prediction. We describe our 13 summarization methods each from one of four summarization strategies. We show how well our methods perform using Farsi text from the CLEF 2008 shared-task, which we translated to English automatically. We report precision/recall/F1, accuracy and time-on-task. We found that different summarization methods perform optimally for different evaluation metrics, but overall query biased word clouds are the best summarization strategy. In our analysis, we demonstrate that using the ROUGE metric on our sentence-based summaries cannot make the same kinds of distinctions as our evaluation framework does. Finally, we present our recommendations for creating much-needed evaluation standards and databases.
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Summary

In this paper we present our task-based evaluation of query biased summarization for cross-language information retrieval (CLIR) using relevance prediction. We describe our 13 summarization methods each from one of four summarization strategies. We show how well our methods perform using Farsi text from the CLEF 2008 shared-task, which we...

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Characterizing phonetic transformations and acoustic differences across English dialects

Published in:
IEEE Trans. Audio, Speech, and Lang. Process., Vol. 22, No. 1, January 2014, pp. 110-24.

Summary

In this work, we propose a framework that automatically discovers dialect-specific phonetic rules. These rules characterize when certain phonetic or acoustic transformations occur across dialects. To explicitly characterize these dialect-specific rules, we adapt the conventional hidden Markov model to handle insertion and deletion transformations. The proposed framework is able to convert pronunciation of one dialect to another using learned rules, recognize dialects using learned rules, retrieve dialect-specific regions, and refine linguistic rules. Potential applications of our proposed framework include computer-assisted language learning, sociolinguistics, and diagnosis tools for phonological disorders.
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Summary

In this work, we propose a framework that automatically discovers dialect-specific phonetic rules. These rules characterize when certain phonetic or acoustic transformations occur across dialects. To explicitly characterize these dialect-specific rules, we adapt the conventional hidden Markov model to handle insertion and deletion transformations. The proposed framework is able to...

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