Continuous word representations, trained on large unlabeled corpora are useful for many
natural language processing tasks. Popular models that learn such representations ignore …

Transformer language models (LMs) are fundamental to NLP research methodologies and
applications in various languages. However, developing such models specifically for the …

Semantic relatedness between words is a core concept in natural language processing.
While countless approaches have been proposed, measuring which one works best is still a …

Bigrams (two-word sequences) hold a special place in semantic composition research since
they are the smallest unit formed by composing words. A semantic relatedness dataset that …

The paper describes the results of the first shared task on word sense induction (WSI) for the
Russian language. While similar shared tasks were conducted in the past for some …

This paper introduces a novel collection of word embeddings, numerical representations of
lexical semantics, in 55 languages, trained on a large corpus of pseudo-conversational …

This paper presents a new graph-based approach that induces synsets using synonymy
dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted …

A traditional view on sentence comprehension holds that the listener parses linguistic input
using hierarchical syntactic rules. Recently, physiological evidence for such a claim has …

Distributed representations of words play a major role in the field of natural language
processing by encoding semantic and syntactic information of words. However, most …

This paper describes the results of the first shared task on taxonomy enrichment for the
Russian language. The participants were asked to extend an existing taxonomy with …