Titlebook: Artificial Intelligence and Natural Language; 6th Conference, AINL Andrey Filchenkov,Lidia Pivovarova,Jan ?i?ka Conference proceedings 2018

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Andrey Filchenkov,Lidia Pivovarova,Jan ?i?kaIncludes supplementary material:

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Communications in Computer and Information Sciencehttp://image.papertrans.cn/b/image/162256.jpg

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978-3-319-71745-6Springer International Publishing AG 2018

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Semantic Feature Aggregation for Gender Identification in Russian Facebook Russian. We collect Facebook posts of Russian-speaking users and apply them as a dataset for two topic modelling techniques and a distributional clustering approach. The output of the algorithms is applied as a feature aggregation method in a task of gender classification based on a smaller Faceboo

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Using Linguistic Activity in Social Networks to Predict and Interpret Dark Psychological Traitsanging from psychology to marketing, but there are very few works of this kind on Russian-speaking samples. We use Latent Dirichlet Allocation on the Facebook status updates to extract interpretable features that we then use to identify Facebook users with certain negative psychological traits (the

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APNEA

Deep Learning for Acoustic Addressee Detection in Spoken Dialogue Systemsspeech addressed to real humans. In this work, several modalities were analyzed, and acoustic data has been chosen as the main modality by reason of the most flexible usability in modern SDSs. To resolve the problem of addressee detection, deep learning methods such as fully-connected neural network

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Deep Neural Networks in Russian Speech Recognitionts. We propose applying various DNNs in automatic recognition of Russian continuous speech. We used different neural network models such as Convolutional Neural Networks (CNNs), modifications of Long short-term memory?(LSTM), Residual Networks and Recurrent Convolutional Networks (RCNNs). The presen