Towards Domain Adaptation for Parsing Web Data
Mohammad Abid Khan, Markus Dickinson, Sandra Kübler · Recent Advances in Natural Language Processing · 2013
We improve upon a previous line of work for parsing web data, by exploring the impact of different decisions regarding the training data. First, we compare training on automatically POS-tagged data vs. gold POS data. Secondly, we compare the effect of training and testing within sub-genres, i.e., whether a close match of the genre is more important than training set size. Finally, we examine different ways to select out-of-domain parsed data to add to training, attempting to match the in-domain data in different shallow ways (sentence length, perplexity). In general, we find that approximating the in-domain data has a positive impact on parsing.