5-year European Research Council (ERC) Starting Grant (nr. 715154; February 2017 - January 2022) funded within the H2020 Programme
Interested in joining? Write to Gemma Boleda (email@example.com).
The project thus seeks to understand the phenomenon of reference in natural language via computational modeling experiments, and we are particularly interested in the interaction of language with conceptual knowledge, on the one hand, and the extralinguistic context, on the other.
This interdisciplinary project builds on two complementary semantic traditions: 1) Formal semantics, a symbolic approach that can delimit and track linguistic referents, but does not adequately match them with the descriptive content of linguistic expressions; 2) Continuous approaches to language such as deep learning models and distributional semantics, which can handle descriptive content but do not associate it to individuated referents. AMORE synthesizes the two approaches into a unified, scalable model of reference that operates with individuated referents and links them to referential expressions characterized by rich descriptive content. The model is a distributed (neural network) version of a formal semantic framework that is furthermore able to integrate perceptual (visual) and linguistic information about entities. We test it extensively in referential tasks that require matching noun phrases (the Medicine student, the white cat) with entity representations extracted from text and images. AMORE advances our scientific understanding of language and its computational modeling, and contributes to the far-reaching debate between symbolic and continuous approaches to cognition with a proposal that falls clearly on the continuous camp, but integrates key insights from the symbolic camp.
PI: Gemma Boleda
Senior researcher: Louise McNally
Write to gemma DOT boleda AT upf DOT edu.
(Image credits: Hagerty Ryan, USFWS)
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 715154).