Probing Lexical Ambiguity: Word Vectors Encode Number and Relatedness of Senses

Calculating the Number of Senses: Implications for Ambiguity Advantage Effect During Lexical Access

Using WordNet Lexical Database and Internet to Disambiguate Word Senses

The term “knowledge acquisition bottleneck” has been used in Word Sense Disambiguation Tasks (WSDTs) to illustrate/express the problem of the lack of large tagged corpora. In this paper, an automated WSDT is based on text corpora extracted / collected from Internet web pages. First, the disambiguation for the sense of a word, in a context, is based on the use of its definition and the definitio...

Limits of Lexical Semantic Relatedness with Ontology-based Conceptual Vectors

Conceptual vectors can be used to represent thematic aspects of text segments, which allow for the computation of semantic relatedness. We study the behavior of conceptual vectors based on an ontology by comparing the results to the Miller-Charles benchmark. We discuss the limits to such an approach due to explicit mapping, as well as the viability of the Miller-Charles dataset as a benchmark f...

Discovering word senses from a network of lexical cooccurrences

Lexico-semantic networks such as WordNet have been criticized about the nature of the senses they distinguish as well as on the way they define these senses. In this article, we present a possible solution to overcome these limits by defining the sense of words from the way they are used. More precisely, we propose to differentiate the senses of a word from a network of lexical cooccurrences bu...

Specialising Word Vectors for Lexical Entailment

We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymy...