NAACL-ANLP 2000 Workshop: Syntactic and Semantic Complexity in Natural Language Processing Systems

The last decade has seen an explosion in the work done in the development of robust natural language processing systems. A common methodology used in building these systems has been to analyze a sample of the data available (either manually, or automatically for training statistical systems), build statistical/heuristical schemas based upon the analysis, and test the system on a blind sample of the data. Due to this commonly used paradigm, an important area of research that has not been given the attention it deserves is the estimation of syntactic and semantic complexity faced by these systems in the tasks they perform. The Workshop on Syntactic and Semantic Complexity in Natural Language Processing Systems, held on April 30th, 2000 at the Language Technology Joint Conference on Applied Natural Language Processing and the North American Chapter of the Association of Computational Linguistics (ANLP-NAACL2000) was organized around the goals of discussing, promoting, and presenting new research results regarding the question of complexity as it pertains to the syntax and semantics of natural language. In particular, the goal of the workshop was to focus on: •estimation of the syntactic and semantic complexity of specific NLP tasks •semantic complexity and world knowledge •role of syntactic and semantic complexity in system design and testing •syntactic and semantic complexity and its role in the evaluation of NLP systems •use of syntactic and semantic complexity as a performance predictor •relationship between syntactic and semantic complexity