Lexical Acquisition with Cross-Situational Learning and Bayesian Unsupervised Word Segmentation

Abstract

In this study, we develop a computational model in which an agent without a lexicon discovers words and their meanings by extending the model for cross-situational learning with unsupervised word segmentation. A computational model for cross-situational learning was proposed that learns the word's meaning by estimating its attributes and categories. However, this model did not include word segmentation and did not assume the ungrounded words, i.e., words that are not associated with sensory information. The proposed model simultaneously infers the words contained in sentences, the attributes and categories corresponding to those words, and ungrounded words or not. Experimental results show that our model, which considers sensory information, improves segmentation performance by 2.1\% and clustering performance by accounting for ungrounded words.