Towards Robust Linguistic Analysis using OntoNotes

Summary, in English

Large-scale linguistically annotated corpora have played a crucial role in advancing the state of the art of key natural language technologies such as syntactic, semantic and discourse analyzers, and they serve as training data as well as evaluation benchmarks. Up till now, however, most of the evaluation has been done on monolithic corpora such as the Penn Treebank, the Proposition Bank. As a result, it is still unclear how the state-of-the-art analyzers perform in general on data from a variety of genres or domains. The completion of the OntoNotes corpus, a large-scale, multi-genre, multilingual corpus manually annotated with syntactic, semantic and discourse information, makes it possible to perform such an evaluation. This paper presents an analysis of the performance of publicly available, state-of-the-art tools on all layers and languages in the OntoNotes v5.0 corpus. This should set the benchmark for future development of various NLP components in syntax and semantics, and possibly encourage research towards an integrated system that makes use of the various layers jointly to improve overall performance

• Author: Sameer Pradhan; Alessandro Moschitti; Nianwen Xue; Hwee Tou Ng; Anders Björkelund; Olga Uryupina; Yuchen Zhang; Zhi Zhong
• Publishing year: 2013-08-01
• Language: English
• Pages: 143-152
• Publication/Series: Proceedings of the Seventeenth Conference on Computational Natural Language Learning
• Links: https://www.aclweb.org/anthology/W13-3516
• Document type: Conference paper
• Publisher: Association for Computational Linguistics
• Topic: Language Technology (Computational Linguistics)
• Status: Published
• ISBN/ISSN/Other: ISBN: 978-1-937284-70-1