Recent years have seen important advances in the quality of state-of-the-art models, but this
has come at the expense of models becoming less interpretable. This survey presents an …

Contextualized word embeddings (CWE) such as provided by ELMo (Peters et al., 2018),
Flair NLP (Akbik et al., 2018), or BERT (Devlin et al., 2019) are a major recent innovation in …

Abstract Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word
in a given context. It lies at the base of Natural Language Processing as it provides semantic …

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 …

In this paper, we present Watasense, an unsupervised system for word sense
disambiguation. Given a sentence, the system chooses the most relevant sense of each …

The paper describes our participation in the first shared task on word sense induction and
disambiguation for the Russian language RUSSE'2018 (Panchenko et al., 2018). For each …

We present an approach to combining distributional semantic representations induced from
text corpora with manually constructed lexical semantic networks. While both kinds of …

This paper proposes an algorithm for unsupervised Word Sense Disambiguation to bypass
the knowledge bottleneck faced by supervised approaches. By simulating the semantic …

The lack of annotated data is one of the challenging issues in an ultra-fine entity typing,
which is the task to assign semantic types for a given entity mention. Hence, automatic type …

Creating a taxonomy of interests is expensive and human-effort intensive: not only do we
need to identify nodes and interconnect them, in order to use the taxonomy, we must also …