In this work, we revisit Shared Task 1 from the 2012 *SEM Conference: the automated analysis of negation. Unlike the vast majority of participating systems in 2012, our approach works over explicit and formal representations of propositional semantics, i.e. derives the notion of negation scope assumed in this task from the structure of logical-form meaning representations. We relate the task-specific interpretation of (negation) scope to the concept of (quantifier and operator) scope in mainstream underspecified semantics. With reference to an explicit encoding of semantic predicate-argument structure, we can operationalize the annotation decisions made for the 2012 *SEM task, and demonstrate how a comparatively simple system for negation scope resolution can be built from an off-the-shelf deep parsing system. In a system combination setting, our approach improves over the best published results on this task to date.
Keywords: syntactic dependency parsing, domain variationWe compare three different approaches to parsing into syntactic, bilexical 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 postconverted 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 dependency types for English; in-and out-of-domain experimentation ranges over diverse text types. 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. When evaluated extrinsically in two downstream tasks -negation resolution and semantic dependency parsing -these accuracy gains do sometimes but not always translate into improved end-to-end performance.
We design and test a sentence comparison method using the framework of Robust Minimal Recursion Semantics which allows us to utilise the deep parse information produced by Jacy, a Japanese HPSG based parser and the lexical information available in our ontology. Our method was used for both paraphrase detection and also for answer sentence selection for question answering. In both tasks, results showed an improvement over Bag-of-Words, as well as providing extra information useful to the applications.
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