Word meaning extension in humans and machines

Words in natural language often take on new meanings, so that language users can express an infinite set of intended meanings with a finite vocabulary. Such processes of word meaning extension (WME) are highly productive, diverse, but also non-arbitrary. However, it remains unclear what common cognitive mechanisms and knowledge are driving various types of word meaning extension. Modeling WME is also relevant for natural language processing (NLP), since novel lexical expressions are constantly emerging through time, and NLP systems should effectively interpret and generate such novel word usages in a human-like way. In this dissertation, I develop a computational framework that not only accounts for word meaning extension in historical language development, but also supports NLP systems to flexibly interpret and generate novel word usages. My dissertation is organized into two main parts. In the first part, I study word meaning extension from a computational cognitive science perspective. I first propose a probabilistic generative model of word meaning extension that relies on multimodal semantic knowledge, and show that the model, when incorporated with the cognitive processes of chaining, can accurately predict historical emergence of novel verb-noun syntactic compositions. Next, I present a novel and general account of semantic chaining through the lens of cognitive efficiency. This account explains different chaining mechanisms as a tradeoff between representation and complexity. I show that the efficiency-based framework can be formulated as an infinite mixture model from Bayesian non-parametric statistics, which adaptively constructs word meaning through time under limited computational resources. In the second part of my dissertation, I study how modeling human-like word meaning extension can enhance natural language processing. Specifically, I propose the problem of word sense extension, where a neural language model trained on limited linguistic data is asked to generate novel usages based on previously unseen word senses. I develop a generative framework that combines deep few-shot learning with semantic chaining to capture incremental word sense extension, and I show that this framework can be leveraged to fine-tune and improve word sense disambiguation on rare word senses. Furthermore, I investigate how modeling the systematicity in word meaning extension in combination with language models helps the construction of non-literal word usages like metaphors. I show that learning the analogical similarity between word meanings effectively improves language model systematicity in making both incremental and irregular types of word meaning extension. I therefore suggest that learning systematic word meaning extension benefits language models on multiple tasks pertaining to figurative language understanding. In summary, my dissertation contributes a principled paradigm for modeling the generative processes of word meaning extension, and it opens up future opportunities for human-like automated processing of creative language use.

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