The Groningen Meaning Bank

Abstract

What would be a good method to provide a large collection of semantically annotated texts with formal, deep semantics rather than shallow? In this talk I will argue that (i) a bootstrapping approach comprising state-of-the-art NLP tools for semantic parsing, in combination with (ii) a wiki-like interface for collaborative annotation of experts, and (iii) a game with a purpose for crowdsourcing, are the starting ingredients for fulfilling this enterprise. The result, known as the Groningen Meaning Bank, is a semantic resource that anyone can edit and that integrates various semantic phenomena, including predicate-argument structure, scope, tense, thematic roles, animacy, pronouns, and rhetorical relations. A single semantic formalism, Discourse Representation Theory, embraces all these phenonema by taking meaning representations of texts rather than sentences as the units of annotation.

Expectation-based Comprehension: Modeling the Interaction of World Knowledge and Linguistic Experience

“…It must be noted, however, that none of the presented machinery or results hinges upon this choice-it primarily serves the controlled investigation of the model's performance. In future work, we aim to investigate different ways of deriving a DSS from empirical data, for example, by making use of semantically annotated corpora (e.g., the Groningen Meaning Bank; Bos, Basile, Evang, Venhuizen, & Bjerva, 2017), or crowd-sourced data describing world knowledge (see, e.g., Elman & McRae, 2017).…”

The Alexa Meaning Representation Language

“…The Alexa ontology expands schema.org to cover types, properties and roles used in spoken language. Semantic parsing has been investigated in the content of small domain-specific datasets such as GeoQuery (Wong and Mooney, 2006) and in the context of larger broad-coverage representations such as the Groningen Meaning Bank (GMB) (Bos et al, 2017), the Abstract Meaning Representation (AMR) (Banarescu et al, 2013), UCCA (Abend and Rappoport, 2013), PropBank (Kingsbury and Palmer, 2002), Raiment (Baker et al, 1998) and lambda-DCS (Kingsbury and Palmer, 2002). OntoNotes (Hovy et al, 2006), lambda-DCS s (Liang, 2013) (Baker et al, 1998), FrameNet (Baker et al, 1998), combinatory categorial grammars (CCG) (Steedman and Baldridge, 2011) (Hockenmaier and Steedman, 2007), universal dependencies (Nivre et al, 2016) are all related representations.…”

Context-Sensitive Recognition for Emerging and Rare Entities

“…2.1 and Tab. 1) we utilize 1) all components of the W-NUT 2016 Twitter NER shared task (Strauss et al, 2016), 2) all components of the 2003 CONLL NER shared task (Tjong Kim Sang and De Meulder, 2003), 3) the WikiNER annotations (Nothman et al, 2008(Nothman et al, , 2012, and 4) the Groningen Meaning Bank (Bos et al, 2017). Each corpus required mapping its entity types to the six 2017 shared task types, and for data sets (2), (3), and (4), only mappings for the location and person types were deemed appropriate (geo-loc, facility, and loc to location, and per to person).…”