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Automatic topic detection for spoken input

: Soliman, Mohamed

Volltext urn:nbn:de:0011-n-4801006 (2.7 MByte PDF)
MD5 Fingerprint: 095a9a8f9b93fc7f24ac3761f20f0b06
Erstellt am: 11.1.2018

Aachen, 2017, 75 S.
Aachen, TH, Master Thesis, 2017
Master Thesis, Elektronische Publikation
Fraunhofer IAIS ()

In the Natural Language Understanding field, one of the important tasks is topic detection. Given the recent advancements in the field of deep learning, we explore the task of automatic topic detection for spoken input in the news domain. In this work, we study the effect of applying deep learning techniques such as Recurrent Neural Networks and Word Embeddings to the task of topic segmentation and topic detection given a spoken text stream. We also study the performance of the system given both written and spoken text using our novel approach for text segmentation.