Reading between the lines: overcoming data sparsity for accurate classification of lexical relationships

Abstract

Comunicació presentada a: 4th Joint Conference on Lexical and Computational Semantics, celebrada a Denver, Colorado, Estats Units d'Amèrica, del 4 al 5 de juny de 2015.

The lexical semantic relationships between word pairs are key features for many NLP tasks. Most approaches for automatically classifying related word pairs are hindered by data sparsity because of their need to observe two words co-occurring in order to detect the lexical relation holding between them. Even when mining very large corpora, not every related word pair co-occurs. Using novel representations based on graphs and word embeddings, we present two systems that are able to predict relations between words, even when these are never found in the same sentence in a given corpus. In two experiments, we demonstrate superior performance of both approaches over the state of the art, achieving significant gains in recall.

The authors gratefully acknowledge the support of the CLARA project (EU-7FP-ITN-238405), the SKATER project (Ministerio de Economia y Competitividad, TIN2012-38584-C06-05) and of the MultiJEDI ERC Starting Grant no. 259234.