We present a parser that relies primarily on extracting information directly from surface spans rather than on propagating information through enriched grammar structure. For example, instead of creating separate grammar symbols to mark the definiteness of an NP, our parser might instead capture the same information from the first word of the NP. Moving context out of the grammar and onto surface features can greatly simplify the structural component of the parser: because so many deep syntactic cues have surface reflexes, our system can still parse accurately with context-free backbones as minimal as Xbar grammars. Keeping the structural backbone simple and moving features to the surface also allows easy adaptation to new languages and even to new tasks. On the SPMRL 2013 multilingual constituency parsing shared task (Seddah et al., 2013) , our system outperforms the top single parser system of Björkelund et al. (2013) on a range of languages. In addition, despite being designed for syntactic analysis, our system also achieves stateof-the-art numbers on the structural sentiment task of Socher et al. (2013) . Finally, we show that, in both syntactic parsing and sentiment analysis, many broad linguistic trends can be captured via surface features.
REF introduced an approach where, instead of propagating contextual information from the leaves of the tree to internal nodes in order to refine the grammar, the structural complexity of the grammar is minimized.
Less Grammar, More Features
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Computer Science