Unsupervised Induction of Linguistic Categories with Records of Reading, Speaking, and Writing
Maria Barrett, Ana Valeria Gonzalez-Garduño, Lea Frermann, Anders Søgaard
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) | Association for Computational Linguistics | Published : 2018
When learning POS taggers and syntactic chunkers for low-resource languages, different resources may be available, and often all we have is a small tag dictionary, motivating type-constrained unsupervised induction. Even small dictionaries can improve the performance of unsupervised induction algorithms. This paper shows that performance can be further improved by including data that is readily available or can be easily obtained for most languages, i.e., eye-tracking, speech, or keystroke logs (or any combination thereof). We project information from all these data sources into shared spaces, in which the union of words is represented. For English unsupervised POS induction, the additional ..View full abstract
Thanks to Desmond Elliott for valuable ideas. This research was partially funded by the ERC Starting Grant LOWLANDS No. 313695, as well as by Trygfonden.