Domain Adaptation for Dependency Parsing via Self-Training

Juntao Yu, Mohab Elkaref, Bernd Bohnet
2015 Proceedings of the 14th International Conference on Parsing Technologies  
This paper presents a successful approach for domain adaptation of a dependency parser via self-training. We improve parsing accuracy for out-of-domain texts with a self-training approach that uses confidence-based methods to select additional training samples. We compare two confidence-based methods: The first method uses the parse score of the employed parser to measure the confidence into a parse tree. The second method calculates the score differences between the best tree and alternative
more » ... ees. With these methods, we were able to improve the labeled accuracy score by 1.6 percentage points on texts from a chemical domain and by 0.6 on average on texts of three web domains. Our improvements on the chemical texts of 1.5% UAS is substantially higher than improvements reported in previous work of 0.5% UAS. For the three web domains, no positive results for self-training have been reported before.
doi:10.18653/v1/w15-2201 dblp:conf/iwpt/YuEB15 fatcat:poy6w2srhrcmdh3peosn2m4oly