Word Tagging with Foundational Ontology Classes: Extending the WordNet-DOLCE Mapping to Verbs

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• 关键词：Linguistic resources ; Semantic annotation ; Foundational ontology
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：10024
• 期：1
• 页码：593-605
• 全文大小：316 KB
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• 作者单位：Vivian S. Silva (17)
  André Freitas (17)
  Siegfried Handschuh (17)
  
  17. Department of Computer Science and Mathematics, University of Passau, Innstraße 43, 94032, Passau, Germany
  
• 丛书名：Knowledge Engineering and Knowledge Management
• ISBN：978-3-319-49004-5
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：10024

文摘

Semantic annotation is fundamental to deal with large-scale lexical information, mapping the information to an enumerable set of categories over which rules and algorithms can be applied, and foundational ontology classes can be used as a formal set of categories for such tasks. A previous alignment between WordNet noun synsets and DOLCE provided a starting point for ontology-based annotation, but in NLP tasks verbs are also of substantial importance. This work presents an extension to the WordNet-DOLCE noun mapping, aligning verbs according to their links to nouns denoting perdurants, transferring to the verb the DOLCE class assigned to the noun that best represents that verb’s occurrence. To evaluate the usefulness of this resource, we implemented a foundational ontology-based semantic annotation framework, that assigns a high-level foundational category to each word or phrase in a text, and compared it to a similar annotation tool, obtaining an increase of 9.05 % in accuracy.