RUSSE: The First Workshop on Russian Semantic Similarity

TL;DR

This paper introduces the RUSSE shared task, providing benchmark datasets for Russian semantic similarity, and analyzes various approaches, showing that methods successful in English are also effective for Russian.

Contribution

It presents the first evaluation methodology and benchmark datasets for Russian semantic similarity, enabling systematic comparison of different computational approaches.

Findings

Supervised models combining multiple sources achieved top results.

Unsupervised skip-gram models performed competitively.

Distributional models are effective for Russian semantic similarity.

Abstract

The paper gives an overview of the Russian Semantic Similarity Evaluation (RUSSE) shared task held in conjunction with the Dialogue 2015 conference. There exist a lot of comparative studies on semantic similarity, yet no analysis of such measures was ever performed for the Russian language. Exploring this problem for the Russian language is even more interesting, because this language has features, such as rich morphology and free word order, which make it significantly different from English, German, and other well-studied languages. We attempt to bridge this gap by proposing a shared task on the semantic similarity of Russian nouns. Our key contribution is an evaluation methodology based on four novel benchmark datasets for the Russian language. Our analysis of the 105 submissions from 19 teams reveals that successful approaches for English, such as distributional and skip-gram models, are directly applicable to Russian as well. On the one hand, the best results in the contest were obtained by sophisticated supervised models that combine evidence from different sources. On the other hand, completely unsupervised approaches, such as a skip-gram model estimated on a large-scale corpus, were able score among the top 5 systems.