Less Grammar, More Features

We present a parser that relies primar-ily on extracting information directly from surface spans rather than on propagat-ing information through enriched gram-mar structure. For example, instead of cre-ating 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 fea-tures 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 X-bar 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 con-stituency 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 addi-tion, despite being designed for syntactic analysis, our system also achieves state-of-the-art numbers on the structural senti-ment 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.