New FCG Editor available!

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Great news: writing constructions has never been easier than with our new FCG Editor! The FCG editor, which is available for Microsoft Windows, macOS and Linux, allows you to quickly install and test Fluid Construction Grammar without having to set up a complete Lisp programming environment or the Babel software architecture. It includes all of the essential functionalities of FCG for rapidly defining and visualizing constructions in your web browser, and to develop grammar fragments that you can use for language comprehension and production. You can download the FCG Editor for free at this page.

The FCG editor is based on a first design by Dr Remi van Trijp, and has since then been co-developed by the Sony Computer Science Laboratories Paris and the VUB Artificial Intelligence Laboratory.

Bringing computational construction grammar into your classes and research

Katrien Beuls

Paul Van Eecke and Katrien Beuls are organising a tutorial at the 11th International Conference on Construction Grammar in Antwerp on 17 August 2021. During the tutorial, we will walk you through the basics of computational construction grammar (CCxG), with a special focus on how the main ideas underlying construction grammar can be implemented using FCG. The tutorial will alternate between theory, hands-on exercises from the textbook and demonstrations of more advanced case studies. It targets in particular lecturers in construction grammar who wish to include CCxG into their courses, as well as scholars who would like to learn CCxG and use it in their research. For more information, please check the tutorial’s webpage.

A computational construction grammar approach to semantic frame extraction

Katrien Beuls

We are happy to announce that our latest FCG paper “A Computational Construction Grammar Approach to Semantic Frame extraction” has now been published in the Linguistics Vanguard.

The paper describes a novel approach to extracting semantic frames from texts, with a case study on extracting frames of causation from newspaper articles. The computational construction grammar approach yields a word-level F1 score of 78.5%, outperforming a commonly used approach based on conditional random fields by 4.5 percentage points.

Visual question answering

Katrien Beuls

Have you always wondered what the role of construction grammar could be in solving AI benchmark tasks, such as for example visual question answering? Our latest paper “Computational Construction Grammar for Visual Question Answering” published in the Linguistics Vanguard shows how we were able to write a construction grammar that could parse natural language questions into directly executable queries that can be used to retrieve information in images. Find out more about how we did it in the paper or check out the web demonstration.

Two open PhD positions in computational construction grammar

Katrien Beuls
There are two open PhD positions in the field of computational construction grammar and artificial intelligence. Apply now!
1) Acquiring domain knowledge through natural language dialogue (VUB AI Lab)
 
The selected candidate will work on a project that investigates how gaps in domain knowledge of either a human or an intelligent system can be identified, and filled through natural language dialogue. For doing so, he or she will need to combine symbolic techniques from computational construction grammar and dialogue modelling, with the goal of building a conversational agent that (i) can interact naturally on both the grammatical and discourse level, (ii) reason about the knowledge that needs to be acquired either by the human or by the agent and (iii) integrate the acquired knowledge into its knowledge base for later reuse.  
2) Learning computational construction grammars (University of Leuven & imec)
As a doctoral researcher, you will investigate how construction grammars can be automatically learned by a computational entity (e.g. an autonomous agent), allowing it to communicate in its native environment. You will use a variety of machine learning techniques, ranging from deep neural networks to inductive logic programming. You will set up multi-agent simulations in which a population of autonomous agents makes use of these techniques to establish an effective and efficient communication system.