Abstract
We compare three different approaches to parsing into syntactic, bi- lexical dependencies for English: a ‘direct’ data-driven dependency parser, a statistical phrase structure parser, and a hybrid, ‘deep’ grammar-driven parser. The analyses from the latter two are post- converted to bi-lexical dependencies. Through this ‘reduction’ of all three approaches to syntactic dependency parsers, we determine empirically what performance can be obtained for a common set of de- pendency types for English, across a broad variety of domains. In doing so, we observe what trade-offs apply along three dimensions, accuracy, efficiency, and resilience to domain variation. Our results suggest that the hand-built grammar in one of our parsers helps in both accuracy and cross-domain parsing performance, but these accuracy gains do not necessarily translate to improvements in the downstream task of negation resolution.
Highlights
IntroductionWe summarize data and software resources used in our experiments
Experimental SetupIn the following, we summarize data and software resources used in our experiments
As assignment of lexical categories is a core part of syntactic analysis, we complement LAS and UAS with tagging accuracy scores (TA), where appropriate
Summary
We summarize data and software resources used in our experiments. We give a brief introduction to the DT syntactic dependency scheme and a comparison to ‘mainstream’ representations. DeepBank HPSG analyses in DeepBank are manually selected from the set of parses licensed by the English Resource Grammar (ERG; Flickinger, 2000). Preterminals are labeled with fine-grained lexical categories, dubbed ERG lexical types, that augment common parts of speech with additional information, for example argument structure or the distinction between count, mass, and proper nouns. The ERG distinguishes about 250 construction types and 1000 lexical types
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