Andrei Kutuzov

• Barnes, Jeremy; Oberlaender, Laura; Troiano, Enrica; Kutuzov, Andrei; Buchmann, Jan & Agerri, Rodrigo [Show all 8 contributors for this article] (2022). SemEval 2022 Task 10: Structured Sentiment Analysis. In Emerson, Guy; Schluter, Natalie; Stanovsky, Gabriel; Kumar, Ritesh; Palmer, Alexis; Schneider, Nathan; Singh, Siddarth & Ratan, Shyam (Ed.), Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022). Association for Computational Linguistics. ISSN 978-1-955917-80-3.
• Kutuzov, Andrei; Touileb, Samia; Mæhlum, Petter; Enstad, Tita & Wittemann, Alexandra (2022). NorDiaChange: Diachronic Semantic Change Dataset for Norwegian, Proceedings of the Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-72-6. p. 2563–2572.
• Giulianelli, Mario; Kutuzov, Andrei & Pivovarova, Lidia (2022). Do Not Fire the Linguist: Grammatical Profiles Help Language Models Detect Semantic Change, Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics. ISSN 978-1-955917-42-1. p. 54–67.
• Kutuzov, Andrei; Giulianelli, Mario & Pivovarova, Lidia (2021). Grammatical Profiling for Semantic Change Detection, Proceedings of the 25th Conference on Computational Natural Language Learning. Association for Computational Linguistics. ISSN 978-1-955917-05-6. p. 423–434.
• Iazykova, Tatyana; Kapelyushnik, Denis; Bystrova, Olga & Kutuzov, Andrei (2021). Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 20, p. 302–318. doi: 10.28995/2075-7182-2021-20-302-317.
• Kutuzov, Andrei & Pivovarova, Lidia (2021). Three-part diachronic semantic change dataset for Russian, Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics. ISSN 978-1-954085-60-2. p. 7–13.
• Ravishankar, Vinit; Kutuzov, Andrei; Øvrelid, Lilja & Velldal, Erik (2021). Multilingual ELMo and the Effects of Corpus Sampling. In Dobnik, Simon & Øvrelid, Lilja (Ed.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8. p. 378–383.
• Kutuzov, Andrei; Barnes, Jeremy; Velldal, Erik; Øvrelid, Lilja & Oepen, Stephan (2021). Large-Scale Contextualised Language Modelling for Norwegian. In Dobnik, Simon & Øvrelid, Lilja (Ed.), Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7929-614-8. p. 30–40.
• Rodina, Julia; Trofimova, Yuliya; Kutuzov, Andrei & Artemova, Ekaterina (2021). ELMo and BERT in Semantic Change Detection for Russian, Proceedings of AIST 2020: Analysis of Images, Social Networks and Texts. Springer. ISSN 978-3-030-72610-2. p. 175–186.
• Kutuzov, Andrei & Kuzmenko, Elizaveta (2021). Representing ELMo embeddings as two-dimensional text online, Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. Association for Computational Linguistics. ISSN 978-1-954085-05-3. p. 143–148.
• Kutuzov, Andrei; Fomin, V.; Mikhailov, V. Nikola & Rodina, Julia (2020). Shiftry: Web service for diachronic analysis of Russian news. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 2020-(19), p. 500–516. doi: 10.28995/2075-7182-2020-19-500-516.
• Katricheva, Nadezda; Yaskevich, Alyaxey; Lisitsina, Anastasiya; Zhordaniya, Tamara; Kutuzov, Andrei & Kuzmenko, Elizaveta (2020). Vec2graph: A Python Library for Visualizing Word Embeddings as Graphs. Communications in Computer and Information Science. ISSN 1865-0929. 1086, p. 190–198. doi: 10.1007/978-3-030-39575-9_20.
• Kutuzov, Andrei & Giulianelli, Mario (2020). UiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection, Proceedings of the Fourteenth Workshop on Semantic Evaluation. Association for Computational Linguistics. ISSN 978-1-952148-31-6. p. 126–134.
• Rodina, Julia & Kutuzov, Andrei (2020). RuSemShift: a dataset of historical lexical semantic change in Russian, Proceedings of the 28th International Conference on Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-952148-27-9. p. 1037–1047.
• Kunilovskaya, Maria; Kutuzov, Andrei & Plum, Alistair (2020). Taxonomy enrichment for Russian: synset classification outperforms linear hyponym-hypernym projections. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 19, p. 459–469. doi: 10.28995/2075-7182-2020-19-474-484.
• Kutuzov, Andrei; Fomin, Vadim; Rodina, Julia & Mikhailov, Vladislav (2020). ShiftRy: web service for diachronic analysis of Russian news. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 19, p. 485–501.
• Logacheva, Varvara; Teslenko, Denis; Shelmanov, Artem; Remus, S.; Ustalov, Dmitry & Kutuzov, Andrei [Show all 10 contributors for this article] (2020). Word Sense Disambiguation for 158 Languages using Word Embeddings Only. In Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Moreno, Asuncion; Odijk, Jan & Piperidis, Stelios (Ed.), Proceedings of The 12th Language Resources and Evaluation Conference. European Language Resources Association. ISSN 979-10-95546-34-4. p. 5945–5954.
• Kutuzov, Andrei & Nikishina, Irina (2019). Double-Blind Peer-Reviewing and Inclusiveness in Russian NLP Conferences, Analysis of Images, Social Networks and Texts (Revised Selected Papers). Springer Publishing Company. ISSN 978-3-030-37334-4. p. 3–8. doi: 10.1007/978-3-030-37334-4_1.
• Droganova, Kira; Kutuzov, Andrei; Mediankin, Nikita & Zeman, Daniel (2019). ÚFAL-Oslo at MRP 2019: Garage Sale Semantic Parsing. In Oepen, Stephan; Abend, Omri; Hajic, Jan; Hershcovich, Daniel; Kuhlmann, Marco; O’Gorman, Tim & Nianwen, Xue (Ed.), Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning. Association for Computational Linguistics. ISSN 978-1-950737-60-4. p. 158–165. doi: 10.18653/v1/K19-2015.
• Kutuzov, Andrei & Kuzmenko, Elizaveta (2019). To Lemmatize or Not to Lemmatize: How Word Normalisation Affects ELMo Performance in Word Sense Disambiguation. In Nivre, Joakim; Derczynski, Leon; Ginter, Filip; Lindi, Bjørn; Oepen, Stephan; Søgaard, Anders & Tidemann, Jorg (Ed.), Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing. Linköping University Electronic Press. ISSN 978-91-7929-999-6. p. 22–28.
• Kutuzov, Andrei; Dorgham, Mohammad; Oliynyk, Oleksiy; Biemann, Chris & Panchenko, Alexander (2019). Making Fast Graph-based Algorithms with Graph Metric Embeddings. In Korhonen, Anna; Traum, David & Màrquez, Lluís (Ed.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-950737-48-2. p. 3349–3355. doi: 10.18653/v1/P19-1325.
• Rodina, Julia; Bakshandaeva, Daria; Fomin, Vadim; Kutuzov, Andrei; Touileb, Samia & Velldal, Erik (2019). Measuring Diachronic Evolution of Evaluative Adjectives with Word Embeddings: the Case for English, Norwegian, and Russian. In Tahmasebi, Nina; Borin, Lars; Jatowt, Adam & Xu, Yang (Ed.), Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics. ISSN 978-1-950737-31-4. p. 202–209. doi: 10.18653/v1/W19-4725. Full text in Research Archive
• Kutuzov, Andrei; Velldal, Erik & Øvrelid, Lilja (2019). One-to-X Analogical Reasoning on Word Embeddings: a Case for Diachronic Armed Conflict Prediction from News Texts. In Tahmasebi, Nina; Borin, Lars; Jatowt, Adam & Xu, Yang (Ed.), Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics. ISSN 978-1-950737-31-4. p. 196–201. doi: 10.18653/v1/W19-4724. Full text in Research Archive
• Kutuzov, Andrei; Dorgham, Mohammad; Oliynyk, Oleksiy; Biemann, Chris & Panchenko, Alexander (2019). Learning Graph Embeddings from WordNet-based Similarity Measures. In Mihalcea, Rada; Shutova, Ekaterina; Ku, Lun-Wei; Evang, Kilian & Poria, Soujanya (Ed.), Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019). Association for Computational Linguistics. ISSN 978-1-948087-93-3. p. 125–135. doi: 10.18653/v1/S19-1014.
• Fomin, Vadim; Bakshandaeva, Daria; Rodina, Julia & Kutuzov, Andrei (2019). Tracing Cultural Diachronic Semantic Shifts in Russian Using Word Embeddings: Test Sets and Baselines. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 2019-May(18), p. 213–227.
• Nikishina, Irina; Bakarov, Amir & Kutuzov, Andrei (2018). RusNLP: Semantic search engine for Russian NLP conference papers. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11179 LNCS, p. 111–120. doi: 10.1007/978-3-030-11027-7_11.
• Bakarov, A; Kutuzov, Andrei & Nikishina, I (2018). Russian computational linguistics: Topical structure in 2007-2017 conference papers. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 2018-May(17).
• Sadov, Mikhail A. & Kutuzov, Andrei (2018). Use of morphology in distributional word embedding models: Russian language case. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 2018-May(17), p. 1–12.
• Kutuzov, Andrei; Øvrelid, Lilja; Szymanski, Terrence & Velldal, Erik (2018). Diachronic word embeddings and semantic shifts: a survey, Proceedings of the 27th International Conference on Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-948087-50-6. p. 1384–1397. Full text in Research Archive
• Ustalov, Dmitry; Panchenko, Alexander; Kutuzov, Andrei; Biemann, Chris & Ponzetto, Simone (2018). Unsupervised Semantic Frame Induction using Triclustering, Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics. ISSN 978-1-948087-34-6. p. 55–62. doi: 10.18653/v1/p18-2010. Full text in Research Archive
• Kutuzov, Andrei (2018). Russian Word Sense Induction by Clustering Averaged Word Embeddings. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 2018-May(17), p. 391–403. Full text in Research Archive Show summary

  The paper reports our participation in the shared task on word sense induction and disambiguation for the Russian language (RUSSE’2018). Our team was ranked 2nd for the wiki-wiki dataset (containing mostly homonyms) and 5th for the bts-rnc and active-dict datasets (containing mostly polysemous words) among all 19 participants. The method we employed was extremely naive. It implied representing contexts of ambiguous words as averaged word embedding vectors, using off-the-shelf pre-trained distributional models. Then, these vector representations were clustered with mainstream clustering techniques, thus producing the groups corresponding to the ambiguous word’ senses. As a side result, we show that word embedding models trained on small but balanced corpora can be superior to those trained on large but noisy data—not only in intrinsic evaluation, but also in downstream tasks like word sense induction.

• Kutuzov, Andrei & Kunilovskaya, Maria (2018). Size vs. Structure in Training Corpora for Word Embedding Models: Araneum Russicum Maximum and Russian National Corpus. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10716 LNCS, p. 47–58. doi: 10.1007/978-3-319-73013-4_5. Show summary

  In this paper, we present a distributional word embedding model trained on one of the largest available Russian corpora: Araneum Russicum Maximum (over 10 billion words crawled from the web). We compare this model to the model trained on the Russian National Corpus (RNC). The two corpora are much different in their size and compilation procedures. We test these differences by evaluating the trained models against the Russian part of the Multilingual SimLex999 semantic similarity dataset. We detect and describe numerous issues in this dataset and publish a new corrected version. Aside from the already known fact that the RNC is generally a better training corpus than web corpora, we enumerate and explain fine differences in how the models process semantic similarity task, what parts of the evaluation set are difficult for particular models and why. Additionally, the learning curves for both models are described, showing that the RNC is generally more robust as training material for this task.

• Kunilovskaya, Maria & Kutuzov, Andrei (2017). Universal Dependencies-based syntactic features in detecting human translation varieties. In Hajič, Jan (Eds.), Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories. Association for Computational Linguistics. ISSN 978-80-88132-04-2. p. 27–36.
• Smirnov, Ivan V.; Kuznetsova, Rita; Kopotev, Mikhail; Khazov, Andrey; Lyashevskaya, Olga & Ivanova, L. [Show all 7 contributors for this article] (2017). Evaluation tracks on plagiarism detection algorithms for the Russian language. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 1(16), p. 271–283.
• Kutuzov, Andrei & Kuzmenko, Elizaveta (2017). Two centuries in two thousand words: Neural embedding models in detecting diachronic lexical changes, Quantitative Approaches to the Russian Language. Routledge. ISSN 9781138097155. doi: 10.4324/9781315105048-5.
• Kutuzov, Andrei; Velldal, Erik & Øvrelid, Lilja (2017). Temporal dynamics of semantic relations in word embeddings: an application to predicting armed conflict participants, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. ISSN 978-1-945626-83-8. p. 1825–1830. doi: 10.18653/v1/D17-1194. Full text in Research Archive
• Kutuzov, Andrei; Velldal, Erik & Øvrelid, Lilja (2017). Tracing armed conflicts with diachronic word embedding models. In Caselli, Tommaso (Eds.), Proceedings of the Events and Stories in the News Workshop. Association for Computational Linguistics. ISSN 978-1-945626-63-0. p. 31–36. doi: 10.18653/v1/W17-2705. Full text in Research Archive
• Kunilovskaya, Maria & Kutuzov, Andrei (2017). Testing target text fluency: A machine learning approach to detecting syntactic translationese in English-Russian translation, New perspectives on cohesion and coherence: Implications for translation. Language Science Press. ISSN 978-3-946234-72-2. p. 75–103. doi: 10.5281/zenodo.814452.
• Kutuzov, Andrei & Kuzmenko, Elizaveta (2017). WebVectors: A toolkit for building web interfaces for vector semantic models. Communications in Computer and Information Science. ISSN 1865-0929. 661, p. 155–161. doi: 10.1007/978-3-319-52920-2_15. Show summary

  The paper presents a free and open source toolkit which aim is to quickly deploy web services handling distributed vector models of semantics. It fills in the gap between training such models (many tools are already available for this) and dissemination of the results to general public. Our toolkit, WebVectors, provides all the necessary routines for organizing online access to querying trained models via modern web interface. We also describe two demo installations of the toolkit, featuring several efficient models for English, Russian and Norwegian.

• Kutuzov, Andrei (2017). Arbitrariness of Linguistic Sign Questioned: Correlation between Word Form and Meaning in Russian. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 1(16), p. 109–120. Full text in Research Archive Show summary

  In this paper, we present the results of preliminary experiments on finding the link between the surface forms of Russian nouns (as represented by their graphic forms) and their meanings (as represented by vectors in a distributional model trained on the Russian National Corpus). We show that there is a strongly significant correlation between these two sides of a linguistic sign (in our case, word). This correlation coefficient is equal to 0.03 as calculated on a set of 1 729 mono-syllabic nouns, and in some subsets of words starting with particular two-letter sequences the correlation raises as high as 0.57. The overall correlation value is higher than the one reported in similar experiments for English (0.016). Additionally, we report correlation values for the noun subsets related to different phonaesthemes, supposedly represented by the initial characters of these nouns.

• Lison, Pierre & Kutuzov, Andrei (2017). Redefining Context Windows for Word Embedding Models: An Experimental Study. In Tiedemann, Jörg (Eds.), Proceedings of the 21st Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7685-601-7. p. 284–288. Full text in Research Archive
• Kutuzov, Andrei; Fares, Murhaf; Oepen, Stephan & Velldal, Erik (2017). Word vectors, reuse, and replicability: Towards a community repository of large-text resources. In Tiedemann, Jörg (Eds.), Proceedings of the 21st Nordic Conference on Computational Linguistics (NoDaLiDa). Linköping University Electronic Press. ISSN 978-91-7685-601-7. p. 271–276. Full text in Research Archive
• Kutuzov, Andrei; Kuzmenko, Elizaveta & Pivovarova, Lidia (2017). Clustering of Russian Adjective-Noun Constructions using Word Embeddings. In Pivovarova, Lidia; Piskorski, Jakub & Erjavec, Tomaž (Ed.), Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing. Association for Computational Linguistics. ISSN 978-1-945626-45-6. p. 3–13. doi: 10.18653/v1/W17-1402. Full text in Research Archive
• Koslowa, Olga & Kutuzov, Andrei (2016). Improving Distributional Semantic Models Using Anaphora Resolution during Linguistic Preprocessing. Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii. ISSN 2221-7932. 15, p. 288–299.
• Kutuzov, Andrei; Kuzmenko, Elizaveta & Marakasova, Anna (2016). Exploration of register-dependent lexical semantics using word embeddings, Proceedings of the Workshop on Language Technology Resources and Tools for Digital Humanities (LT4DH). Association for Computational Linguistics. ISSN 978-4-87974-708-2. p. 26–34.
• Kutuzov, Andrei; Velldal, Erik & Øvrelid, Lilja (2016). Redefining part-of-speech classes with distributional semantic models, Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics. ISSN 978-1-945626-19-7. p. 115–125. doi: 10.18653/v1/K16-1012. Full text in Research Archive
• Kutuzov, Andrei; Kopotev, Mikhail; Sviridenko, Tatyana & Ivanova, Lyubov (2016). Clustering Comparable Corpora of Russian and Ukrainian Academic Texts: Word Embeddings and Semantic Fingerprints, Proceedings of the Ninth Workshop on Building and Using Comparable Corpora, held at LREC-2016. European Language Resources Association. ISSN 978-2-9517408-9-1. p. 3–10.
• Kutuzov, Andrei & Kuzmenko, Elizaveta (2016). Neural Embedding Language Models in Semantic Clustering of Web Search Results, Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). European Language Resources Association. ISSN 978-2-9517408-9-1. p. 3044–3048.
• Kutuzov, Andrei & Kuzmenko, Elizaveta (2016). Cross-lingual Trends Detection for Named Entities in News Texts with Dynamic Neural Embedding Models, Proceedings of the First International Workshop on Recent Trends in News Information Retrieval co-located with 38th European Conference on Information Retrieval (ECIR 2016). Technical University of Aachen. ISSN 978-3-319-30671-1. p. 27–32.
• Kutuzov, Andrei & Kuzmenko, Elizaveta (2015). Semi-automated typical error annotation for learner English essays: integrating frameworks, Proceedings of the 4th workshop on NLP for Computer Assisted Language Learning at NODALIDA 2015. Linköping University Electronic Press. ISSN 978-91-7519-036-5. p. 35–41.
• Kutuzov, Andrei & Kuzmenko, Elizaveta (2015). Comparing Neural Lexical Models of a Classic National Corpus and a Web Corpus: The Case for Russian. In Gelbukh, Alexander (Eds.), Computational Linguistics and Intelligent Text Processing. Springer Publishing Company. ISSN 978-3-319-18111-0. p. 47–58. doi: 10.1007/978-3-319-18111-0_4.
• Kutuzov, Andrei (2015). Semantic Clustering of Russian Web Search Results: Possibilities and Problems, Information retrieval. Springer Publishing Company. ISSN 978-3-319-25485-2. p. 320–331. doi: 10.1007/978-3-319-25485-2_12.

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• Tahmasebi, Nina; Montariol, Syrielle; Kutuzov, Andrei; Hengchen, Simon; Dubossarsky, Haim & Borin, Lars (2022). Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change. Association for Computational Linguistics. ISBN 978-1-955917-42-1. 219 p.
• Kutuzov, Andrei; van der Aalst, Wil M. P.; Batagelj, Vladimir & Ignatov, Dmitry (2021). Proceedings of AIST 2020: Analysis of Images, Social Networks and Texts. Springer. ISBN 978-3-030-72610-2. 480 p.

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• van der Aalst, Wil M. P.; Batagelj, Vladimir; Buzmakov, Alexey; Ignatov, Dmitry I.; Kalenkova, Anna & Khachay, Michael [Show all 18 contributors for this article] (2021). Preface. Communications in Computer and Information Science. ISSN 1865-0929. 1357, p. vii–x.

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