Andrey Kutuzov


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Large-Scale Contextualised Language Modelling for NorwegianNorwegian
Andrey Kutuzov | Jeremy Barnes | Erik Velldal | Lilja Øvrelid | Stephan Oepen
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

We present the ongoing NorLM initiative to support the creation and use of very large contextualised language models for Norwegian (and in principle other Nordic languages), including a ready-to-use software environment, as well as an experience report for data preparation and training. This paper introduces the first large-scale monolingual language models for Norwegian, based on both the ELMo and BERT frameworks. In addition to detailing the training process, we present contrastive benchmark results on a suite of NLP tasks for Norwegian. For additional background and access to the data, models, and software, please see :

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Multilingual ELMo and the Effects of Corpus SamplingELMo and the Effects of Corpus Sampling
Vinit Ravishankar | Andrey Kutuzov | Lilja Øvrelid | Erik Velldal
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

Multilingual pretrained language models are rapidly gaining popularity in NLP systems for non-English languages. Most of these models feature an important corpus sampling step in the process of accumulating training data in different languages, to ensure that the signal from better resourced languages does not drown out poorly resourced ones. In this study, we train multiple multilingual recurrent language models, based on the ELMo architecture, and analyse both the effect of varying corpus size ratios on downstream performance, as well as the performance difference between monolingual models for each language, and broader multilingual language models. As part of this effort, we also make these trained models available for public use.

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Three-part diachronic semantic change dataset for RussianRussian
Andrey Kutuzov | Lidia Pivovarova
Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021

We present a manually annotated lexical semantic change dataset for Russian : RuShiftEval. Its novelty is ensured by a single set of target words annotated for their diachronic semantic shifts across three time periods, while the previous work either used only two time periods, or different sets of target words. The paper describes the composition and annotation procedure for the dataset. In addition, it is shown how the ternary nature of RuShiftEval allows to trace specific diachronic trajectories : ‘changed at a particular time period and stable afterwards’ or ‘was changing throughout all time periods’. Based on the analysis of the submissions to the recent shared task on semantic change detection for Russian, we argue that correctly identifying such trajectories can be an interesting sub-task itself.


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UiO-UvA at SemEval-2020 Task 1 : Contextualised Embeddings for Lexical Semantic Change DetectionUiO-UvA at SemEval-2020 Task 1: Contextualised Embeddings for Lexical Semantic Change Detection
Andrey Kutuzov | Mario Giulianelli
Proceedings of the Fourteenth Workshop on Semantic Evaluation

We apply contextualised word embeddings to lexical semantic change detection in the SemEval-2020 Shared Task 1. This paper focuses on Subtask 2, ranking words by the degree of their semantic drift over time. We analyse the performance of two contextualising architectures (BERT and ELMo) and three change detection algorithms. We find that the most effective algorithms rely on the cosine similarity between averaged token embeddings and the pairwise distances between token embeddings. They outperform strong baselines by a large margin (in the post-evaluation phase, we have the best Subtask 2 submission for SemEval-2020 Task 1), but interestingly, the choice of a particular algorithm depends on the distribution of gold scores in the test set.

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Word Sense Disambiguation for 158 Languages using Word Embeddings Only
Varvara Logacheva | Denis Teslenko | Artem Shelmanov | Steffen Remus | Dmitry Ustalov | Andrey Kutuzov | Ekaterina Artemova | Chris Biemann | Simone Paolo Ponzetto | Alexander Panchenko
Proceedings of the 12th Language Resources and Evaluation Conference

Disambiguation of word senses in context is easy for humans, but is a major challenge for automatic approaches. Sophisticated supervised and knowledge-based models were developed to solve this task. However, (i) the inherent Zipfian distribution of supervised training instances for a given word and/or (ii) the quality of linguistic knowledge representations motivate the development of completely unsupervised and knowledge-free approaches to word sense disambiguation (WSD). They are particularly useful for under-resourced languages which do not have any resources for building either supervised and/or knowledge-based models. In this paper, we present a method that takes as input a standard pre-trained word embedding model and induces a fully-fledged word sense inventory, which can be used for disambiguation in context. We use this method to induce a collection of sense inventories for 158 languages on the basis of the original pre-trained fastText word embeddings by Grave et al., (2018), enabling WSD in these languages. Models and system are available online.


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One-to-X Analogical Reasoning on Word Embeddings : a Case for Diachronic Armed Conflict Prediction from News TextsX Analogical Reasoning on Word Embeddings: a Case for Diachronic Armed Conflict Prediction from News Texts
Andrey Kutuzov | Erik Velldal | Lilja Øvrelid
Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change

We extend the well-known word analogy task to a one-to-X formulation, including one-to-none cases, when no correct answer exists. The task is cast as a relation discovery problem and applied to historical armed conflicts datasets, attempting to predict new relations of type ‘location : armed-group’ based on data about past events. As the source of semantic information, we use diachronic word embedding models trained on English news texts. A simple technique to improve diachronic performance in such task is demonstrated, using a threshold based on a function of cosine distance to decrease the number of false positives ; this approach is shown to be beneficial on two different corpora. Finally, we publish a ready-to-use test set for one-to-X analogy evaluation on historical armed conflicts data.

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Making Fast Graph-based Algorithms with Graph Metric Embeddings
Andrey Kutuzov | Mohammad Dorgham | Oleksiy Oliynyk | Chris Biemann | Alexander Panchenko
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Graph measures, such as node distances, are inefficient to compute. We explore dense vector representations as an effective way to approximate the same information. We introduce a simple yet efficient and effective approach for learning graph embeddings. Instead of directly operating on the graph structure, our method takes structural measures of pairwise node similarities into account and learns dense node representations reflecting user-defined graph distance measures, such as e.g. the shortest path distance or distance measures that take information beyond the graph structure into account. We demonstrate a speed-up of several orders of magnitude when predicting word similarity by vector operations on our embeddings as opposed to directly computing the respective path-based measures, while outperforming various other graph embeddings on semantic similarity and word sense disambiguation tasks.

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FAL-Oslo at MRP 2019 : Garage Sale Semantic ParsingÚFAL-Oslo at MRP 2019: Garage Sale Semantic Parsing
Kira Droganova | Andrey Kutuzov | Nikita Mediankin | Daniel Zeman
Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning

This paper describes the FALOslo system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP, Oepen et al. The submission is based on several third-party parsers. Within the official shared task results, the submission ranked 11th out of 13 participating systems.


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Diachronic word embeddings and semantic shifts : a survey
Andrey Kutuzov | Lilja Øvrelid | Terrence Szymanski | Erik Velldal
Proceedings of the 27th International Conference on Computational Linguistics

Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models. However, this vein of research lacks the cohesion, common terminology and shared practices of more established areas of natural language processing. In this paper, we survey the current state of academic research related to diachronic word embeddings and semantic shifts detection. We start with discussing the notion of semantic shifts, and then continue with an overview of the existing methods for tracing such time-related shifts with word embedding models. We propose several axes along which these methods can be compared, and outline the main challenges before this emerging subfield of NLP, as well as prospects and possible applications.

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Unsupervised Semantic Frame Induction using Triclustering
Dmitry Ustalov | Alexander Panchenko | Andrey Kutuzov | Chris Biemann | Simone Paolo Ponzetto
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We use dependency triples automatically extracted from a Web-scale corpus to perform unsupervised semantic frame induction. We cast the frame induction problem as a triclustering problem that is a generalization of clustering for triadic data. Our replicable benchmarks demonstrate that the proposed graph-based approach, Triframes, shows state-of-the art results on this task on a FrameNet-derived dataset and performing on par with competitive methods on a verb class clustering task.


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Clustering of Russian Adjective-Noun Constructions using Word EmbeddingsRussian Adjective-Noun Constructions using Word Embeddings
Andrey Kutuzov | Elizaveta Kuzmenko | Lidia Pivovarova
Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing

This paper presents a method of automatic construction extraction from a large corpus of Russian. The term ‘construction’ here means a multi-word expression in which a variable can be replaced with another word from the same semantic class, for example, ‘a glass of [ water / juice / milk ]’. We deal with constructions that consist of a noun and its adjective modifier. We propose a method of grouping such constructions into semantic classes via 2-step clustering of word vectors in distributional models. We compare it with other clustering techniques and evaluate it against A Russian-English Collocational Dictionary of the Human Body that contains manually annotated groups of constructions with nouns meaning human body parts. The best performing method is used to cluster all adjective-noun bigrams in the Russian National Corpus. Results of this procedure are publicly available and can be used for building Russian construction dictionary as well as to accelerate theoretical studies of constructions.

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Temporal dynamics of semantic relations in word embeddings : an application to predicting armed conflict participants
Andrey Kutuzov | Erik Velldal | Lilja Øvrelid
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

This paper deals with using word embedding models to trace the temporal dynamics of semantic relations between pairs of words. The set-up is similar to the well-known analogies task, but expanded with a time dimension. To this end, we apply incremental updating of the models with new training texts, including incremental vocabulary expansion, coupled with learned transformation matrices that let us map between members of the relation. The proposed approach is evaluated on the task of predicting insurgent armed groups based on geographical locations. The gold standard data for the time span 19942010 is extracted from the UCDP Armed Conflicts dataset. The results show that the method is feasible and outperforms the baselines, but also that important work still remains to be done.

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Building Web-Interfaces for Vector Semantic Models with the WebVectors ToolkitWebVectors Toolkit
Andrey Kutuzov | Elizaveta Kuzmenko
Proceedings of the Software Demonstrations of the 15th Conference of the European Chapter of the Association for Computational Linguistics

In this demo we present WebVectors, a free and open-source toolkit helping to deploy web services which demonstrate and visualize distributional semantic models (widely known as word embeddings). WebVectors can be useful in a very common situation when one has trained a distributional semantics model for one’s particular corpus or language (tools for this are now widespread and simple to use), but then there is a need to demonstrate the results to general public over the Web. We show its abilities on the example of the living web services featuring distributional models for English, Norwegian and Russian.