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We participated in the SENSEVAL-3 English lexical sample task and multilingual lexical sample task. We adopted a supervised learning approach with Support Vector Machines, using only the official training data provided. No other external resources were used. The knowledge sources used were part-of-speech of neighboring words, single words in the surrounding(More)
Recent research efforts on spoken document retrieval have tried to overcome the low quality of 1-best automatic speech recognition transcripts, especially in the case of conversational speech, by using statistics derived from speech lattices containing multiple transcription hypotheses as output by a speech recognizer. We present a method for lattice-based(More)
Recent efforts on the task of spoken document retrieval (SDR) have made use of speech lattices: speech lattices contain information about alternative speech transcription hypotheses other than the 1-best transcripts, and this information can improve retrieval accuracy by overcoming recognition errors present in the 1-best transcription. In this paper, we(More)
Speech recognition transcripts are far from perfect; they are not of sufficient quality to be useful on their own for spoken document retrieval. This is especially the case for conversational speech. Recent efforts have tried to overcome this issue by using statistics from speech lattices instead of only the 1-best transcripts; however, these efforts have(More)
the equations for shape parameters decouple and the numerical procedure for the inversion problem becomes very efficient, thus resulting in a very satisfactory shape recovery for exact data. Due to the ill-posed nature of the inverse problems, shape recovery and its resolution were found to be sensitive to the data noise. However , shape recovery of the(More)
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