Learning Complex Output Representations in Connectionist Parsing of Spoken Language

Abstract

Due to robustness, learnability and ease of integration of different information sources, connectionist parsing systems have proven to be applicable for parsing spoken language, However, most proposed connectionist parsers do not compute and represent complex structures. These parsers assign only a very limited structure to a given input string. For spoken language translation and data base access, more detailed syntactic and semantic representation is needed. In the present paper, the authors show that arbitrary linguistic features and arbitrary complex tree structures can indeed also be learned by a connectionist parsing system.