Abstract
In the interaction between humans and computers as well as in the interaction among humans, topical units (TUs) have an important role. This motivated our investigation of topical unit recognition. To lay foundations for this, we first create a classifier for topical units using Deep Neural Nets with rectifier units (DRNs) and the probabilistic sampling method. Evaluating the resulting models on the HuComTech corpus using the Unweighted Average Recall (UAR) measure, we find that this method produces significantly higher classification scores than those that can be achieved using Support Vector Machines, and what DRNs can produce in the absence of probabilistic sampling. We also examine experimentally the number of topical unit labels to be used. We demonstrate that not having to discriminate between variations of topic change leads to better classification scores. However, there can be applications where this distinction is necessary, for which case we introduce a hierarchical classification method. Results show that this method increases the UAR scores by more than 7%.
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Notes
- 1.
The current study is an extended version of this conference paper.
- 2.
The difference between the two is due to the different length in the occurrences of various topical units in the annotation. A 3.2 s long topic elaboration and a 32 s long topic elaboration both count as one occurrence in the annotation, but the number of frames associated with the former will be 10, while the number of frames associated with the latter will be 100.
- 3.
It should be mentioned here that before applying our machine learning methods, we normalized all non-binary features so as to have a zero mean and unit variance.
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Acknowledgements
The research reported in the paper was conducted with the support of the Hungarian Scientific Research Fund (OTKA) grant # K116938. Tamás Grósz was supported by the ÚNKP-16-3 new national excellence programme of the Ministry of Human Capacities.
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Kovács, G., Grósz, T., Váradi, T. (2019). Using Deep Rectifier Neural Nets and Probabilistic Sampling for Topical Unit Classification. In: Klempous, R., Nikodem, J., Baranyi, P. (eds) Cognitive Infocommunications, Theory and Applications. Topics in Intelligent Engineering and Informatics, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-95996-2_1
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