Global Features for Shallow Discourse Parsing

A coherently related group of sentences may be referred to as a discourse. In this paper we address the problem of parsing coherence relations as defined in the Penn Discourse Tree Bank (PDTB). A good model for discourse structure analysis needs to account both for local dependencies at the token-level and for global dependencies and statistics. We present techniques on using inter-sentential or sentence-level (global), data-driven, non-grammatical features in the task of parsing discourse. The parser model follows up previous approach based on using token-level (local) features with conditional random fields for shallow discourse parsing, which is lacking in structural knowledge of discourse. The parser adopts a two-stage approach where first the local constraints are applied and then global constraints are used on a reduced weighted search space (n-best). In the latter stage we experiment with different rerankers trained on the first stage n-best parses, which are generated using lexico-syntactic local features. The two-stage parser yields significant improvements over the best performing model of discourse parser on the PDTB corpus.

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