Developing a large semantically annotated corpus

By Valerio Basile, Johan Bos, Kilian Evang, Noortje J. Venhuizen. 2012

141 citations recorded by the source corpus.

What would be a good method to provide a large collection of semantically annotated texts with formal, deep semantics rather than shallow? We argue that a bootstrapping approach comprising state-of-the-art NLP tools for parsing and semantic interpretation, in combination with a wiki-like interface for collaborative annotation of experts, and a game with a purpose for crowdsourcing, are the starting ingredients for fulfilling this enterprise. The result is a semantic resource that anyone can edit and that integrates various phenomena, including predicate-argument structure, scope, tense, thematic roles, rhetorical relations and presuppositions, into a single semantic formalism: Discourse Representation Theory. Taking texts rather than sentences as the units of annotation results in deep semantic representations that incorporate discourse structure and dependencies. To manage the various (possibly conflicting) annotations provided by experts and non-experts, we introduce a method that stores ``Bits of Wisdom'' in a database as stand-off annotations.

Subfields: Artificial Intelligence

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Citations in the corpus

Continual Few-Shot Learning for Text Classification (2021, background): “Previous NLU projects also beneﬁted from expert annotations (Basile et al., 2012; Bos et al., 2017; Warstadt et al., 2019; Williams et al., 2020).”
Question-Answer Driven Semantic Role Labeling: Using Natural Language to Annotate Natural Language (2015, methodology): “The Groningen Meaning Bank (Basile et al., 2012) project annotates text by manually correcting the output of existing semantic parsers.”
Analysing Data-To-Text Generation Benchmarks (2017, neutral): “0, (Basile et al., 2012)) and covers different genres (e.”
Large-Scale QA-SRL Parsing (2018, background): “Some more recent semantic resources require less annotator training, or can be crowdsourced (Abend and Rappoport, 2013; Reisinger et al., 2015; Basile et al., 2012; Michael et al., 2018).”
The Sketch Engine: ten years on (2014, methodology): “annotation, leading examples being the UAM tool (O'Donnell 2008) and the Groningen Meaning Bank tool (Basile et al. 2012).”
ANLIzing the Adversarial Natural Language Inference Dataset (2020, background): “This suggests that one ideally wants expert annotators for difficult annotation tasks like this one (see also Basile et al. 2012; Bos et al. 2017 for other NLU annotation projects that benefit from experts).”
Discourse Representation Structure Parsing (2018, background): “Le and Zuidema (2012) were the ﬁrst to train a semantic parser on an early release of the GMB (2,000 documents; Basile et al. 2012), however, they abandon lambda calculus in favor of a graph based representation.”
Abstract Meaning Representation for Sembanking (2013, methodology): “The need for larger, broad-coverage sem-banks has sparked several projects, including the Groningen Meaning Bank (GMB) (Basile et al., 2012a), UCCA (Abend and Rappoport, 2013), the Semantic Treebank (ST) (Butler and Yoshimoto, 2012), the Prague Dependency Treebank (B¨ohmov´a et al., 2003), and UNL…”