Effects of Developmental Heuristics for Natural Language Learning
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Machine learning in natural language has been a widely pursued area of research. However, few learning techniques model themselves after human learning, despite the nature of the task being closely connected to human cognition. In particular, the idea of learning language in stages is a common approach for human learning, as can be seen in practice in the education system and in research on language acquisition. However, staged learning for natural language is an area largely overlooked by machine learning researchers. This thesis proposes a developmental learning heuristic for natural language models, to evaluate its performance on natural language tasks. The heuristic simulates human learning stages by training on child, teenage and adult text, provided by the British National Corpus. The three staged learning techniques that are proposed take advantage of these stages to create a single developed Hidden Markov Model. This model is then applied to the task of part-of-speech tagging to observe the effects of development on language learning.