With the ever increasing size of the web, relevant information extraction on the Internet with
a query formed by a few keywords has become a big challenge. Query Expansion (QE) …

The relative ineffectiveness of information retrieval systems is largely caused by the
inaccuracy with which a query formed by a few keywords models the actual user information …

An introduction to a broad range of topics in deep learning, covering mathematical and
conceptual background, deep learning techniques used in industry, and research …

Inventors have long dreamed of creating machines that think. Ancient Greek myths tell of
intelligent objects, such as animated statues of human beings and tables that arrive full of …

We present an automatic approach to the construction of BabelNet, a very large, wide-
coverage multilingual semantic network. Key to our approach is the integration of …

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context
in a computational manner. WSD is considered an AI-complete problem, that is, a task …

Supervised systems have nowadays become the standard recipe for Word Sense
Disambiguation (WSD), with Transformer-based language models as their primary …

Most word representation methods assume that each word owns a single semantic vector.
This is usually problematic because lexical ambiguity is ubiquitous, which is also the …

Traditional clustering algorithms do not consider the semantic relationships among words so
that cannot accurately represent the meaning of documents. To overcome this problem …

This paper introduces the use of Wikipedia as a resource for automatic keyword extraction
and word sense disambiguation, and shows how this online encyclopedia can be used to …