Multimodal Distributional Semantics

Multimodal Distributional Semantics

Distributional semantic models derive computational representations of word meaning from the patterns of co-occurrence of words in text. Such models have been a success story of computational linguistics, being able to provide reliable estimates of semantic relatedness for the many semantic tasks requiring them. However, distributional models extract meaning information exclusively from text, w...

Functional Distributional Semantics

Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semantics and recent advances in machine learning. In particular, we separate predicates from the entities they refer to, allowing us to perform Bayesian inference based on logical for...

Distributional Semantics in Technicolor

Our research aims at building computational models of word meaning that are perceptually grounded. Using computer vision techniques, we build visual and multimodal distributional models and compare them to standard textual models. Our results show that, while visual models with state-of-the-art computer vision techniques perform worse than textual models in general tasks (accounting for semanti...

Compositionality in distributional semantics

Distributional Semantics in Use

In this position paper we argue that an adequate semantic model must account for language in use, taking into account how discourse context affects the meaning of words and larger linguistic units. Distributional semantic models are very attractive models of meaning mainly because they capture conceptual aspects and are automatically induced from natural language data. However, they need to be ...