Search Results for author: Anton Alekseev

Found 16 papers, 8 papers with code

Syntactic Transfer to Kyrgyz Using the Treebank Translation Method

1 code implementation17 Dec 2024 Anton Alekseev, Alina Tillabaeva, Gulnara Dzh. Kabaeva, Sergey I. Nikolenko

The Kyrgyz language, as a low-resource language, requires significant effort to create high-quality syntactic corpora.

Translation

HJ-Ky-0.1: an Evaluation Dataset for Kyrgyz Word Embeddings

2 code implementations16 Nov 2024 Anton Alekseev, Gulnara Kabaeva

One of the key tasks in modern applied computational linguistics is constructing word vector representations (word embeddings), which are widely used to address natural language processing tasks such as sentiment analysis, information extraction, and more.

Sentiment Analysis Word Embeddings

KyrgyzNLP: Challenges, Progress, and Future

no code implementations8 Nov 2024 Anton Alekseev, Timur Turatali

Despite interest and support from both business and government sectors in the Kyrgyz Republic, the situation for Kyrgyz language resources remains challenging.

Neural Click Models for Recommender Systems

1 code implementation30 Sep 2024 Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Anna Volodkevich, Alexey Vasilev, Andrey V. Savchenko, Sergey Nikolenko

We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models.

Recommendation Systems

Benchmarking Multilabel Topic Classification in the Kyrgyz Language

1 code implementation30 Aug 2023 Anton Alekseev, Sergey I. Nikolenko, Gulnara Kabaeva

Kyrgyz is a very underrepresented language in terms of modern natural language processing resources.

Benchmarking Classification +2

Machine Learning for SAT: Restricted Heuristics and New Graph Representations

no code implementations18 Jul 2023 Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Sergey Nikolenko

Boolean satisfiability (SAT) is a fundamental NP-complete problem with many applications, including automated planning and scheduling.

Scheduling

DetIE: Multilingual Open Information Extraction Inspired by Object Detection

1 code implementation24 Jun 2022 Michael Vasilkovsky, Anton Alekseev, Valentin Malykh, Ilya Shenbin, Elena Tutubalina, Dmitriy Salikhov, Mikhail Stepnov, Andrey Chertok, Sergey Nikolenko

Our model sets the new state of the art performance of 67. 7% F1 on CaRB evaluated as OIE2016 while being 3. 35x faster at inference than previous state of the art.

Multilingual NLP Object +2

Near-Zero-Shot Suggestion Mining with a Little Help from WordNet

no code implementations25 Nov 2021 Anton Alekseev, Elena Tutubalina, Sejeong Kwon, Sergey Nikolenko

In this work, we explore the constructive side of online reviews: advice, tips, requests, and suggestions that users provide about goods, venues, services, and other items of interest.

Suggestion mining

Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification

no code implementations17 Jun 2020 Anton Alekseev, Elena Tutubalina, Valentin Malykh, Sergey Nikolenko

Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling.

Aspect Extraction domain classification +2

AspeRa: Aspect-Based Rating Prediction Based on User Reviews

no code implementations WS 2019 Elena Tutubalina, Valentin Malykh, Sergey Nikolenko, Anton Alekseev, Ilya Shenbin

We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items.

Aspect Extraction Prediction

AspeRa: Aspect-based Rating Prediction Model

no code implementations23 Jan 2019 Sergey I. Nikolenko, Elena Tutubalina, Valentin Malykh, Ilya Shenbin, Anton Alekseev

We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users.

model Prediction +1

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