The syntactic process
Supertagging: an approach to almost parsing
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Multilingual deep lexical acquisition for HPSGs via supertagging
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StatMT '07 Proceedings of the Second Workshop on Statistical Machine Translation
Minimized models and grammar-informed initialization for supertagging with highly ambiguous lexicons
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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Much previous work has investigated weak supervision with HMMs and tag dictionaries for part-of-speech tagging, but there have been no similar investigations for the harder problem of supertagging. Here, I show that weak supervision for supertagging does work, but that it is subject to severe performance degradation when the tag dictionary is highly ambiguous. I show that lexical category complexity and information about how supertags may combine syntactically can be used to initialize the transition distributions of a first-order Hidden Markov Model for weakly supervised learning. This initialization proves more effective than starting with uniform transitions, especially when the tag dictionary is highly ambiguous.