Recent years have seen a growing interest within the natural language processing (NLP) community in evaluating the ability of semantic models to capture human meaning representation in the brain. Existing research has mainly focused on applying semantic models to decode brain activity patterns associated with the meaning of individual words, and, more recently, this approach has been extended to sentences and larger text fragments. Our work is the first to investigate metaphor processing in the brain in this context. We evaluate a range of semantic models (word embeddings, compositional, and visual models) in their ability to decode brain activity associated with reading of both literal and metaphoric sentences. Our results suggest that compositional models and word embeddings are able to capture differences in the processing of literal and metaphoric sentences, providing support for the idea that the literal meaning is not fully accessible during familiar metaphor comprehension.

Recent work shows that distributional semantic models can be used to decode patterns of brain activity associated with individual words and sentence meanings.However, it is yet unclear to what extent such models can be used to study and decode fMRI patterns associated with specific aspects of semantic composition such as the negation function.In this paper, we apply lexical and compositional semantic models to decode fMRI patterns associated with negated and affirmative sentences containing hand-action verbs.Our results show reduced decoding (correlation) of sentences where the verb is in the negated context, as compared to the affirmative one, within brain regions implicated in action-semantic processing.This supports behavioral and brain imaging studies, suggesting that negation involves reduced access to aspects of the affirmative mental representation.The results pave the way for testing alternate semantic models of negation against human semantic processing in the brain.

Research in computational semantics is increasingly guided by our understanding of human semantic processing.However, semantic models are typically studied in the context of natural language processing system performance.In this paper, we present a systematic evaluation and comparison of a range of widely-used, stateof-the-art semantic models in their ability to predict patterns of conceptual representation in the human brain.Our results provide new insights both for the design of computational semantic models and for further research in cognitive neuroscience.

The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding.Yet, the majority of metaphor processing systems to date rely on handengineered features and there is still no consensus in the field as to which features are optimal for this task.In this paper, we present the first deep learning architecture designed to capture metaphorical composition.Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.

Property norms have the potential to aid a wide range of semantic tasks, provided that they can be obtained for large numbers of concepts. Recent work has focused on text as the main source of information for automatic property extraction. In this paper we examine property norm prediction from visual, rather than textual, data, using cross-modal maps learnt between property norm and visual spaces. We also investigate the importance of having a complete feature norm dataset, for both training and testing. Finally, we evaluate how these datasets and cross-modal maps can be used in an image retrieval task.