Search Results for author: Dmitry Ustalov

Found 23 papers, 11 papers with code

TextGraphs 2021 Shared Task on Multi-Hop Inference for Explanation Regeneration

1 code implementation NAACL (TextGraphs) 2021 Peter Jansen, Mokanarangan Thayaparan, Marco Valentino, Dmitry Ustalov

While previous editions of this shared task aimed to evaluate explanatory completeness – finding a set of facts that form a complete inference chain, without gaps, to arrive from question to correct answer, this 2021 instantiation concentrates on the subtask of determining relevance in large multi-hop explanations.

TextGraphs 2020 Shared Task on Multi-Hop Inference for Explanation Regeneration

no code implementations COLING (TextGraphs) 2020 Peter Jansen, Dmitry Ustalov

In this second iteration of the explanation regeneration shared task, participants are supplied with more than double the training and evaluation data of the first shared task, as well as a knowledge base nearly double in size, both of which expand into more challenging scientific topics that increase the difficulty of the task.

Eliminating Fuzzy Duplicates in Crowdsourced Lexical Resources

no code implementations GWC 2016 Yuri Kiselev, Dmitry Ustalov, Sergey Porshnev

Collaboratively created lexical resources is a trending approach to creating high quality thesauri in a short time span at a remarkably low price.

YARN: Spinning-in-Progress

no code implementations GWC 2016 Pavel Braslavski, Dmitry Ustalov, Mikhail Mukhin, Yuri Kiselev

YARN (Yet Another RussNet), a project started in 2013, aims at creating a large open WordNet-like thesaurus for Russian by means of crowdsourcing.

CrowdSpeech and VoxDIY: Benchmark Datasets for Crowdsourced Audio Transcription

1 code implementation2 Jul 2021 Nikita Pavlichenko, Ivan Stelmakh, Dmitry Ustalov

The main obstacle towards designing aggregation methods for more advanced applications is the absence of training data, and in this work, we focus on bridging this gap in speech recognition.

Crowdsourced Text Aggregation

Crowdsourcing Natural Language Data at Scale: A Hands-On Tutorial

no code implementations NAACL 2021 Alexey Drutsa, Dmitry Ustalov, Valentina Fedorova, Olga Megorskaya, Daria Baidakova

In this tutorial, we present a portion of unique industry experience in efficient natural language data annotation via crowdsourcing shared by both leading researchers and engineers from Yandex.

Word Sense Disambiguation for 158 Languages using Word Embeddings Only

no code implementations LREC 2020 Varvara Logacheva, Denis Teslenko, Artem Shelmanov, Steffen Remus, Dmitry Ustalov, Andrey Kutuzov, Ekaterina Artemova, Chris Biemann, Simone Paolo Ponzetto, Alexander Panchenko

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.

Word Embeddings Word Sense Disambiguation

TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration

no code implementations WS 2019 Peter Jansen, Dmitry Ustalov

While automated question answering systems are increasingly able to retrieve answers to natural language questions, their ability to generate detailed human-readable explanations for their answers is still quite limited.

Information Retrieval Question Answering

HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings

1 code implementation SEMEVAL 2019 Saba Anwar, Dmitry Ustalov, Nikolay Arefyev, Simone Paolo Ponzetto, Chris Biemann, Alexander Panchenko

We present our system for semantic frame induction that showed the best performance in Subtask B. 1 and finished as the runner-up in Subtask A of the SemEval 2019 Task 2 on unsupervised semantic frame induction (QasemiZadeh et al., 2019).

Word Embeddings

Unsupervised Sense-Aware Hypernymy Extraction

1 code implementation17 Sep 2018 Dmitry Ustalov, Alexander Panchenko, Chris Biemann, Simone Paolo Ponzetto

In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction.

Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction

2 code implementations CL 2019 Dmitry Ustalov, Alexander Panchenko, Chris Biemann, Simone Paolo Ponzetto

We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains.

Graph Clustering

Unsupervised Semantic Frame Induction using Triclustering

1 code implementation ACL 2018 Dmitry Ustalov, Alexander Panchenko, Andrei Kutuzov, Chris Biemann, Simone Paolo Ponzetto

We use dependency triples automatically extracted from a Web-scale corpus to perform unsupervised semantic frame induction.

RUSSE: The First Workshop on Russian Semantic Similarity

no code implementations15 Mar 2018 Alexander Panchenko, Natalia Loukachevitch, Dmitry Ustalov, Denis Paperno, Christian Meyer, Natalia Konstantinova

The paper gives an overview of the Russian Semantic Similarity Evaluation (RUSSE) shared task held in conjunction with the Dialogue 2015 conference.

Semantic Similarity Semantic Textual Similarity

Fighting with the Sparsity of Synonymy Dictionaries

no code implementations30 Aug 2017 Dmitry Ustalov, Mikhail Chernoskutov, Chris Biemann, Alexander Panchenko

Graph-based synset induction methods, such as MaxMax and Watset, induce synsets by performing a global clustering of a synonymy graph.

Unsupervised, Knowledge-Free, and Interpretable Word Sense Disambiguation

1 code implementation EMNLP 2017 Alexander Panchenko, Fide Marten, Eugen Ruppert, Stefano Faralli, Dmitry Ustalov, Simone Paolo Ponzetto, Chris Biemann

In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images.

Word Sense Disambiguation

Watset: Automatic Induction of Synsets from a Graph of Synonyms

1 code implementation ACL 2017 Dmitry Ustalov, Alexander Panchenko, Chris Biemann

This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings.

Word Embeddings Word Sense Induction

Towards crowdsourcing and cooperation in linguistic resources

no code implementations19 Aug 2014 Dmitry Ustalov

Linguistic resources can be populated with data through the use of such approaches as crowdsourcing and gamification when motivated people are involved.

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