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TextGraphs 2019 Shared Task on Multi-Hop Inference for Explanation Regeneration
The Shared Task on Multi-Hop Inference for Explanation Regeneration tasks participants with regenerating detailed gold explanations for standardized elementary science exam questions by selecting facts from a knowledge base of semi-structured tables.
RUSSE: The First Workshop on Russian Semantic Similarity
This work proposes a shared task on the semantic similarity of Russian nouns and proposes an evaluation methodology based on four novel benchmark datasets for the Russian language, which reveals that successful approaches for English, such as distributional and skip-gram models, are directly applicable to Russian.
Human and Machine Judgements for Russian Semantic Relatedness
This work uses one of the best approaches identified in this competition to generate the fifth high-coverage resource, the first open distributional thesaurus of Russian, which multiple evaluations indicate its high accuracy.
Watset: Automatic Induction of Synsets from a Graph of Synonyms
This paper builds a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary, and applies word sense induction to deal with ambiguous words, and clusters the disambiguated version of the ambiguous input graph into synsets.
Unsupervised Semantic Frame Induction using Triclustering
These 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.
RUSSE'2018: A Shared Task on Word Sense Induction for the Russian Language
The first shared task on word sense induction (WSI) for the Russian language explores the performance of sense induction and disambiguation methods for a Slavic language that shares many features with other Slavic languages, such as rich morphology and virtually free word order.
Negative Sampling Improves Hypernymy Extraction Based on Projection Learning
It is shown that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach of Fu et al. (2014) on three datasets from different languages.
A text-to-picture system for russian language
Motivation and design of the general purpose text-to-picture synthesis system for Russian language processing is presented and the basic design ideas of the system architecture have been highlighted.
YARN: Spinning-in-Progress
The paper describes the linguistic, technical, and organizational principles of the project, as well as the evaluation results, lessons learned, and the future plans of YARN.
HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings
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