  Understanding the meaning of words is crucial for many tasks that involve
human-machine interaction. This has been tackled by research in Word Sense
Disambiguation (WSD) in the Natural Language Processing (NLP) field. Recently,
WSD and many other NLP tasks have taken advantage of embeddings-based
representation of words, sentences, and documents. However, when it comes to
WSD, most embeddings models suffer from ambiguity as they do not capture the
different possible meanings of the words. Even when they do, the list of
possible meanings for a word (sense inventory) has to be known in advance at
training time to be included in the embeddings space. Unfortunately, there are
situations in which such a sense inventory is not known in advance (e.g., an
ontology selected at run-time), or it evolves with time and its status diverges
from the one at training time. This hampers the use of embeddings models for
WSD. Furthermore, traditional WSD techniques do not perform well in situations
in which the available linguistic information is very scarce, such as the case
of keyword-based queries. In this paper, we propose an approach to keyword
disambiguation which grounds on a semantic relatedness between words and senses
provided by an external inventory (ontology) that is not known at training
time. Building on previous works, we present a semantic relatedness measure
that uses word embeddings, and explore different disambiguation algorithms to
also exploit both word and sentence representations. Experimental results show
that this approach achieves results comparable with the state of the art when
applied for WSD, without training for a particular domain.
