Abstract
The paper presents a module for topic identification that is embedded into a complex system for acquisition and storing large volumes of text data from the Web. The module processes each of the acquired data items and assigns keywords to them from a defined topic hierarchy that was developed for this purposes and is also described in the paper. The quality of the topic identification is evaluated in two ways - using classic precision-recall measures and also indirectly, by measuring the ASR performance of the topic-specific language models that are built using the automatically filtered data.
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Skorkovská, L., Ircing, P., Pražák, A., Lehečka, J. (2011). Automatic Topic Identification for Large Scale Language Modeling Data Filtering. In: Habernal, I., Matoušek, V. (eds) Text, Speech and Dialogue. TSD 2011. Lecture Notes in Computer Science(), vol 6836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23538-2_9
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DOI: https://doi.org/10.1007/978-3-642-23538-2_9
Publisher Name: Springer, Berlin, Heidelberg
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