Anton Alekseev


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Ad Lingua: Text Classification Improves Symbolism Prediction in Image Advertisements
Andrey Savchenko | Anton Alekseev | Sejeong Kwon | Elena Tutubalina | Evgeny Myasnikov | Sergey Nikolenko
Proceedings of the 28th International Conference on Computational Linguistics

Understanding image advertisements is a challenging task, often requiring non-literal interpretation. We argue that standard image-based predictions are insufficient for symbolism prediction. Following the intuition that texts and images are complementary in advertising, we introduce a multimodal ensemble of a state of the art image-based classifier, a classifier based on an object detection architecture, and a fine-tuned language model applied to texts extracted from ads by OCR. The resulting system establishes a new state of the art in symbolism prediction.


AspeRa: Aspect-Based Rating Prediction Based on User Reviews
Elena Tutubalina | Valentin Malykh | Sergey Nikolenko | Anton Alekseev | Ilya Shenbin
Proceedings of the 2019 Workshop on Widening NLP

We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items. It is based on aspect extraction with neural networks and combines the advantages of deep learning and topic modeling. It is mainly designed for recommendations, but an important secondary goal of AspeRa is to discover coherent aspects of reviews that can be used to explain predictions or for user profiling. We conduct a comprehensive empirical study of AspeRa, showing that it outperforms state-of-the-art models in terms of recommendation quality and produces interpretable aspects. This paper is an abridged version of our work (Nikolenko et al., 2019)