Kilian Evang

TuLiPA: Towards a Multi-Formalism Parsing Environment for Grammar Engineering

In this paper, we present an open-source parsing environment (Tubingen Linguistic Parsing Architecture, TuLiPA) which uses Range Concatenation Grammar (RCG) as a pivot formalism, thus opening the way to the parsing of several mildly context-sensitive formalisms. This environment currently supports tree-based grammars (namely Tree-Adjoining Grammars (TAG) and Multi-Component Tree-Adjoining Grammars with Tree Tuples (TT-MCTAG)) and allows computation not only of syntactic structures, but also of the corresponding semantic representations. It is used for the development of a tree-based grammar for German.

The Groningen Meaning Bank

The goal of the Groningen Meaning Bank (GMB) is to obtain a large corpus of English texts annotated with formal meaning representations. Since manually annotating a comprehensive corpus with deep semantic representations is a hard and time-consuming task, we employ a sophisticated bootstrapping approach. This method employs existing language technology tools (for segmentation, part-of-speech tagging, named entity tagging, animacy labelling, syntactic parsing, and semantic processing) to get a reasonable approximation of the target annotations as a starting point. The machine-generated annotations are then refined by information obtained from both expert linguists (using a wiki-like platform) and crowd-sourcing methods (in the form of a ‘Game with a Purpose’) which help us in deciding how to resolve syntactic and semantic ambiguities. The result is a semantic resource that integrates various linguistic phenomena, including predicate-argument structure, scope, tense, thematic roles, rhetorical relations and presuppositions. The semantic formalism that brings all levels of annotation together in one meaning representation is Discourse Representation Theory, which supports meaning representations that can be translated to first-order logic. In contrast to ordinary treebanks, the units of annotation in the GMB are texts, rather than isolated sentences. The current version of the GMB contains more than 10,000 public domain texts aligned with Discourse Representation Structures, and is freely available for research purposes.

RoBox: CCG with Structured Perceptron for Supervised Semantic Parsing of Robotic Spatial Commands

We use a Combinatory Categorial Grammar (CCG) parser with a structured perceptron learner to address Shared Task 6 of SemEval-2014, Supervised Semantic Parsing of Robotic Spatial Commands. Our system reaches an accuracy of 79% ignoring spatial context and 87% using the spatial planner, showing that CCG can successfully be applied to the task.

Sharing Is Caring: The Future of Shared Tasks

Shared tasks are indisputably drivers of progress and interest for problems in NLP. This is reflected by their increasing popularity, as well as by the fact that new shared tasks regularly emerge for under-researched and under-resourced topics, especially at workshops and smaller conferences. The general procedures and conventions for organizing a shared task have arisen organically over time (Paroubek, Chaudiron, and Hirschman 2007, Section 7). There is no consistent framework that describes how shared tasks should be organized. This is not a harmful thing per se, but we believe that shared tasks, and by extension the field in general, would benefit from some reflection on the existing conventions. This, in turn, could lead to the future harmonization of shared task procedures. Shared tasks revolve around two aspects: research advancement and competition. We see research advancement as the driving force and main goal behind organizing them. Competition is an instrument to encourage and promote participation. However,