You can think of meaning in language as a kind of “blob” that is roughly partitioned by words. The partitions vary in clarity – “table” is clearly different from “flower” but less different from “countertop”. Different languages partition the space differently, but people have hypothesized that there are common elements in this partitioning. For example, there are countless ways to divide up the space of possible colours by words, but languages seem to converge on the same partitioning – a similar arrangement of colour terms.
This project is about using machine learning techniques, particularly deep reinforcement learning in an agent-based paradigm, to simulate how language communities might arrive at a common partitioning of the “meaning blob” into words, based on theories of cognition and communication. In cooperation with Prof. Devdatt Dubhashi at Chalmers.