Ricerc@Sapienza

Knowing the correct distribution of senses within a corpus can potentially boost the performance of Word Sense Disambiguation (WSD) systems by many points. We present two fully automatic and language-independent methods for computing the distribution of senses given a raw corpus of sentences. Intrinsic and extrinsic evaluations show that our methods outperform the current state of the art in sense distribution learning and the strongest baselines for the most frequent sense in multiple languages and on domain-specific test sets.

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 information for words. In the last decade, great strides have been made in this field and much effort has been devoted to mitigate the knowledge acquisition bottleneck problem, i.e., the problem of semantically annotating texts at a large scale and in different languages. This issue is ubiquitous in WSD as it hinders the creation of both multilingual knowledge bases and manually-curated training sets.

Knowing the Most Frequent Sense (MFS) of a word has been proved to help Word Sense Disambiguation (WSD) models significantly. However, the scarcity of sense-annotated data makes it difficult to induce a reliable and high-coverage distribution of the meanings in a language vocabulary. To address this issue, in this paper we present CluBERT, an automatic and multilingual approach for inducing the distributions of word senses from a corpus of raw sentences.