AMORE: A distributional MOdel of Reference to Entities

5-year European Research Council (ERC) Starting Grant (nr. 715154; February 2017 - January 2022) funded within the H2020 Programme


  • May 2017: PhD positions awarded to Laura Aina and Ionut-Teodor Sorodoc, joining in the fall. We're very lucky to have gotten such great students!
  • March 2017: AMORE is featured in the Spanish newspaper El Periódico (Spanish, Catalan).
  • March 2017: Post-doc positions awarded to Carina Silberer and Matthijs Westera, who will join in the summer. We're very happy and proud to have them onboard!
  • February 2017: The project has started!


When I asked my seven-year-old daughter Who is the boy in your class who was also new in school last year, like you? she instantly replied Daniel, using the descriptive content in my utterance to identify an entity in the real world and refer to it. The ability to use language to refer to reality is crucial for humans, and yet it is very difficult to model. AMORE breaks new ground in Computational Linguistics, Linguistics, and Artificial Intelligence by developing a model of linguistic reference to entities implemented as a computational system that can learn its own representations from data. 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.

More background:


Senior researcher: Louise McNally
Post-docs: Carina Silberer, Matthijs Westera (starting summer 2017)
PhD students: Laura Aina, Ionut-Teodor Sorodoc (starting fall 2017)

Plus 1 more researcher to be hired with project funds. 

Advisory Board


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).