Sebastian Beschke


Exploring Graph-Algebraic CCG Combinators for Syntactic-Semantic AMR Parsing
Sebastian Beschke
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

We describe a new approach to semantic parsing based on Combinatory Categorial Grammar (CCG). The grammar’s semantic construction operators are defined in terms of a graph algebra, which allows our system to induce a compact CCG lexicon. We introduce an expectation maximisation algorithm which we use to filter our lexicon down to 2500 lexical templates. Our system achieves a semantic triple (Smatch) precision that is competitive with other CCG-based AMR parsing approaches.


Graph Algebraic Combinatory Categorial Grammar
Sebastian Beschke | Wolfgang Menzel
Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics

This paper describes CCG/AMR, a novel grammar for semantic parsing of Abstract Meaning Representations. CCG/AMR equips Combinatory Categorial Grammar derivations with graph semantics by assigning each CCG combinator an interpretation in terms of a graph algebra. We provide an algorithm that induces a CCG/AMR from a corpus and show that it creates a compact lexicon with low ambiguity and achieves a robust coverage of 78% of the examined sentences under ideal conditions. We also identify several phenomena that affect any approach relying either on CCG or graph algebraic approaches for AMR parsing. This includes differences of representation between CCG and AMR, as well as non-compositional constructions that are not expressible through a monotonous construction process. To our knowledge, this paper provides the first analysis of these corpus issues.


Large-scale CCG Induction from the Groningen Meaning Bank
Sebastian Beschke | Yang Liu | Wolfgang Menzel
Proceedings of the ACL 2014 Workshop on Semantic Parsing