Towards Comparability of Linguistic Graph Banks for Semantic Parsing
We announce a new language resource for research on semantic parsing, a large, carefully curated collection of semantic dependency graphs representing multiple linguistic traditions. This resource is called SDP{\textasciitilde}2016 and provides an update and extension to previous versions used as Semantic Dependency Parsing target representations in the 2014 and 2015 Semantic Evaluation Exercises. For a common core of English text, this third edition comprises semantic dependency graphs from four distinct frameworks, packaged in a unified abstract format and aligned at the sentence and token levels. SDP 2016 is the first general release of this resource and available for licensing from the Linguistic Data Consortium in May 2016. The data is accompanied by an open-source SDP utility toolkit and system results from previous contrastive parsing evaluations against these target representations.
Code (0)
등록된 구현이 없습니다.
Tasks
Dependency ParsingSemantic Dependency ParsingSemantic ParsingSentenceSimilar Papers 제목 키워드 기반
Learning compositional structures for semantic graph parsing
AM dependency parsing is a method for neural semantic graph parsing that exploits the principle of compositionality. While AM dependency parsers have been shown to be fast and accurate across several graphbanks, they req…
Dependency ParsingFast semantic parsing with well-typedness guarantees
AM dependency parsing is a linguistically principled method for neural semantic parsing with high accuracy across multiple graphbanks. It relies on a type system that models semantic valency but makes existing parsers sl…
Dependency ParsingSemantic ParsingGraph-Based Meaning Representations: Design and Processing
This tutorial is on representing and processing sentence meaning in the form of labeled directed graphs. The tutorial will (a) briefly review relevant background in formal and linguistic semantics; (b) semi-formally defi…
SentenceSurveyCompositional Semantic Parsing Across Graphbanks
Most semantic parsers that map sentences to graph-based meaning representations are hand-designed for specific graphbanks. We present a compositional neural semantic parser which achieves, for the first time, competitive…
Multi-Task LearningSemantic ParsingNormalizing Compositional Structures Across Graphbanks
The emergence of a variety of graph-based meaning representations (MRs) has sparked an important conversation about how to adequately represent semantic structure. These MRs exhibit structural differences that reflect di…
Multi-Task LearningSemantic Parsing