Broad-Coverage Parsing with Neural Networks

Abstract

Subsymbolic systems have been successfully used to model several aspects of human language processing. Such parsers are appealing because they allow revising the interpretation as words are incrementally processed. Yet, it has been very hard to scale them up to realistic language due to training time, limited memory, and the difficulty of representing linguistic structure. In this study, we show that it is possible to keep track of long-distance dependencies and to parse into deeper structures than before based on two techniques: a localist encoding of the input sequence and a dynamic unrolling of the network according to the parse tree. With these techniques, the system can nonmonotonically parse a corpus of realistic sentences into parse trees labelled with grammatical tags from a broad-coverage Head-driven Phrase Structure Grammar of English.