The Institute for Logic, Language and Computation (ILLC) is a research institute at the University of Amsterdam, in which researchers from the Faculty of Science and the Faculty of Humanities collaborate. Its central research area is the study of fundamental principles of encoding, transmission and comprehension of information. Research at ILLC is interdisciplinary, and aims at bringing together insights from various disciplines concerned with information and information processing, such as computational linguistics, logic, mathematics, computer science, linguistics, cognitive science, artificial intelligence and philosophy.
This postdoc position is part of the larger Dutch research consortium ‘Language in Interaction’, sponsored by a large grant from the Netherlands Organisation for Scientific Research (NWO). This research consortium brings together researchers from many of the excellent research groups in the Netherlands, with a research programme on the foundations of language. The goal is to understand both the universality and the variability of the human language faculty from genes to behaviour. In addition to excellence in the domain of language and related relevant fields of cognition, the consortium provides state-of-the-art research facilities and a research team with ample experience in the complex research methods that will be invoked to address the scientific questions at the highest level of methodological sophistication. These include methods from genetics, neuroimaging, computational modelling, and patient-related research.
The consortium has identified five Big Questions (BQ) that are central to our understanding of the human language faculty. We are now looking for highly motivated candidates to join the team for:
BQ1: The nature of the mental lexicon: How to bridge neurobiology and psycholinguistic theory by computational modelling?
Project description
Big Question 1
The big question this project addresses is how to use computational modeling to link levels of description, from neurons to cognition and behavior, in understanding the language system. We focus on the mental lexicon and aim to characterize its structure in a way that is precise and meaningful in neurobiological and (psycho)linguistic terms. Our overarching goal is to devise causal/explanatory models of the mental lexicon that can explain neural and behavioral data. This will significantly deepen our understanding of the mental lexicon, lexical access, and lexical acquisition.
The key question addressed in this project is how to learn word vector representations that encode the combinatorial properties of words required to account for complex linguistic phenomena. Current distributional semantics models typically ignore the hierarchical structure of the sentences in which they occur. They yield word representations that capture semantic similarity among concrete nouns and verbs very well, but have less to say about more abstract semantic and syntactic properties. In contrast, in all major frameworks in theoretical linguistics, the lexicon contains rich information about each word: not only about its (referential) semantics, but also about its morphosyntactic category and other combinatorial properties. In this project we study, in machine learned vector-space models, the concrete linguistic phenomena that have motivated the properties of existing linguistic formalisms (such as CCG, TAG, HPSG, CxG, Simpler Syntax, GB, MG). The project will first identify among those linguistic phenomena the ones most useful to evaluate the vector-space models. We will then work on techniques to investigate how these existing neural models deal with the phenomena in question, and, importantly, how to quantify the performance of these models on these phenomena.


University of Amsterdam


The postdoctoral researcher should have:
a PhD degree (or equivalent) in (computational) linguistics, or another relevant field of study;
a strong background in syntax, formal semantics and/or parsing is required: you should be familiar with various formalisms for lexical and compositional semantics and syntax, and with a range of linguistic phenomena that motivate these formalisms;
knowledge of methods in distributional semantics and machine learning for Natural Language Processing is desirable, as is previous experience with vector space models of semantic compositionality;
an active interest in the cognitive science and neuroscience of language is essential;

Preference will be given to candidates whose profile stands out in comparison with international peers.

Language requirements:

excellent proficiency in written and spoken English.

Educational level:

Ph. D.

How to apply:

Please mention NLP People as a source when applying


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About University of Amsterdam

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