This is the annotation manual for Universal Conceptual Cognitive Annotation (UCCA; Abend and Rappoport, 2013), specifically the Foundational Layer.
Building on recent advances in semantic parsing and text simplification, we investigate the use of semantic splitting of the source sentence as preprocessing for machine translation.
Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens.
Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate.
We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results.
Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects.
Here we present a simple and efficient splitting algorithm based on an automatic semantic parser.
Ranked #19 on Text Simplification on TurkCorpus
Given the success of recent semantic parsing shared tasks (on SDP and AMR), we expect the task to have a significant contribution to the advancement of UCCA parsing in particular, and semantic parsing in general.
The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others.
Ranked #3 on UCCA Parsing on SemEval 2019 Task 1
We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation.
Ranked #4 on UCCA Parsing on SemEval 2019 Task 1
With our selected context configurations, we train on only 14% (A), 26. 2% (V), and 33. 6% (N) of all dependency-based contexts, resulting in a reduced training time.
The run time complexity of state-of-the-art inference algorithms in graph-based dependency parsing is super-linear in the number of input words (n).