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Towards Robust Linguistic Analysis Using OntoNotes
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@MISC{Pradhan_towardsrobust,
author = {Sameer Pradhan and Ro Moschitti and Nianwen Xue and Hwee Tou Ng and Anders Björkelund and Olga Uryupina and Yuchen Zhang and Zhi Zhong},
title = {Towards Robust Linguistic Analysis Using OntoNotes},
year = {}
}
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Abstract
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. 1
Keyphrases
crucial role training data various nlp component proposition bank various layer discourse information monolithic corpus integrated system state-of-the-art analyzer discourse analyzer penn treebank key natural language technology ontonotes v5 future development state-of-the-art tool overall performance ontonotes corpus multilingual corpus evaluation benchmark
