Document expansion for speech retrieval
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Perspectives on Information Retrieval and Speech
Information Retrieval Techniques for Speech Applications [this book is based on the workshop “Information Retrieval Techniques for Speech Applications”, held as part of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in New Orleans, USA, in September 2001].
Probabilistic structured query methods
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Building an information retrieval test collection for spontaneous conversational speech
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Spoken Document Retrieval Based on Approximated Sequence Alignment
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
Combining word and phonetic-code representations for spoken document retrieval
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part II
Expert Systems with Applications: An International Journal
CLEF-2006 CL-SR at Maryland: English and Czech
CLEF'06 Proceedings of the 7th international conference on Cross-Language Evaluation Forum: evaluation of multilingual and multi-modal information retrieval
Language independent semantic kernels for short-text classification
Expert Systems with Applications: An International Journal
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This paper reports results for the University of Maryland’s participation in the CLEF-2005 Cross-Language Speech Retrieval track. Techniques that were tried include: (1) document expansion with manually created metadata (thesaurus keywords and segment summaries) from a large side collection, (2) query refinement with pseudo-relevance feedback, (3) keyword expansion with thesaurus synonyms, and (4) cross-language speech retrieval using translation knowledge obtained from the statistics of a large parallel corpus. The results show that document expansion and query expansion using blind relevance feedback were effective, although optimal parameter choices differed somewhat between the training and evaluation sets. Document expansion in which manually assigned keywords were augmented with thesaurus synonyms yielded marginal gains on the training set, but no improvement on the evaluation set. Cross-language retrieval with French queries yielded 79% of monolingual mean average precision when searching manually assigned metadata despite a substantial domain mismatch between the parallel corpus and the retrieval task. Detailed failure analysis indicates that speech recognition errors for named entities were an important factor that substantially degraded retrieval effectiveness.