6 |
XplaiNLI: Explainable Natural Language Inference through Visual Analytics ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
XplaiNLI : Explainable Natural Language Inference through Visual Analytics
|
|
|
|
In: Proceedings of the 28th International Conference on Computational Linguistics : System Demonstrations / Ptaszynski, Michal; Ziolko, Bartosz (Hrsg.). - Stroudsburg, PA : ACL, 2020. - S. 48-52. - ISBN 978-1-952148-28-6 (2020)
|
|
BASE
|
|
Show details
|
|
15 |
Universal Dependencies 2.0 – CoNLL 2017 Shared Task Development and Test Data
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Proceedings of the LexSem+Logics Workshop 2016 ...
|
|
|
|
Abstract:
Lexical semantics continues to play an important role in driving research directions in NLP, with the recognition and understanding of context becoming increasingly important in delivering successful outcomes in NLP tasks. Besides traditional processing areas such as word sense and named entity disambiguation, the creation and maintenance of dictionaries, annotated corpora and resources have become cornerstones of lexical semantics research and produced a wealth of contextual information that NLP processes can exploit. New efforts both to link and construct from scratch such information - as Linked Open Data or by way of formal tools coming from logic, ontologies and automated reasoning - have increased the interoperability and accessibility of resources for lexical and computational semantics, even in those languages for which they have previously been limited. LexSem+Logics 2016 combines the 1st Workshop on Lexical Semantics for Lesser-Resources Languages and the 3rd Workshop on Logics and Ontologies. The ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://arxiv.org/abs/1608.04767 https://dx.doi.org/10.48550/arxiv.1608.04767
|
|
BASE
|
|
Hide details
|
|
|
|