ABSTRACT
This paper focuses on the automatic acquisition of semantic relationships from Chinese corpus, motivated by improving the performances of our QA systems named NL-WAS. Linguistic patterns designed for Chinese sentences are applied to a collection of texts to extract synonymy relationship, hyponymy relationship, and meronymy relationship. Patterns are broken down into unambiguous and ambiguous, and different strategies are adopted to refine the candidates extracted using this two kinds of patterns. Compared to other previous works, we apply not only strict unambiguous patterns but also loose unambiguous patterns to extract relationships and proposed efficient approach to refine the outputs of these patterns for the sake of high recall and high precision. The experimental result shows that the proposed method can delete most noisy pairs of terms and improve accuracy and efficiency of NL-WAS. At the same time, our method is complementary to statistically based approaches that find semantic relationships between terms.
- Marti A. Hearst. Automatic Acquisition of Hyponyms from Large Text Corpora. In Actes, 14th International Conference on Computational Linguistics, pages 539--545, Nantes, France, 1992. Google ScholarDigital Library
- Matthew Berland and Eugene Charniak. Finding Parts in Very Large Corpora. In Proceedings of the 37th Annual Meeting of the Association for the Computational Linguistics (ACL-99), pages 57--64, College Park, MD, 1999. Google ScholarDigital Library
- Finkelstein-Landau Michal and Morin Emmanuel. Extracting Semantic Relationships between Terms: Supervised vs. Unsupervised Methods. In proceedings of International Workshop on Ontological Engineering on the Global Information Infrastructure, pages 71--80, Dagstuhl Castle, Germany, 1999Google Scholar
- Feiyu Xu, Daniela Kurz, Jakub Piskorski and Sven Schmeier. A Domain Adaptive Approach to Automatic Acquisition of Domain Relevant Terms and their Relations with Bootstrapping. In Proceedings of the 3rd International Conference on Language Resources an Evaluation (LREC'02), May 29-31, Las Palmas, Canary Islands, Spain, 2002.Google Scholar
- Pantel. P and Ravichandran. D. Automatically Labeling Semantic Classes. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004.Google Scholar
- Hong Yu. et al. Automatic Extraction of Gene and Protein Synonyms from MEDLINE and Journal Articles. In Proceedings of the American Medical Informatics Association 2002 Symposium (AMIA'2002), November 9-13, 2002.Google Scholar
- Sunjuan Zhang. A Novel Algorithm of Eliminating the Chinese Word Segmentation Ambiguities for Web Answer. Computer Engineering and Applications. 2004Google Scholar
- Zhaojing Wang. An Approach of POS Tagging for Web Answer. Computer Engineering and Applications. 2004Google Scholar
- Xia Sun and Qinghua Zheng. A Method of Special Domain Lexicon Construction Based on Raw Materials. Mini-Micro Systems. (forthcoming)Google Scholar
- Xia Sun and Qinghua Zheng. Semantics-based Answers Selection in Question Answering System. In Proceedings of the 3rd International Conference on Web-Based Learning (ICWL2004), August 8-11, Tsinghua University, Beijing, China, 2004Google Scholar
- Zhaojing Wang. An Approach to Generate Semantic Network of Concept Based on Structural Corpus. Journal of Computer Research and Development. 2004Google Scholar
- Roxana Girju, Adriana Badulescu and Dan Moldovan. Learning Semantic Constraints for the Automatic Discovery of Part-Whole Relations. In proceedings of HLT-NAACL. 2003 Google ScholarDigital Library
Recommendations
Extraction of terms and semantic relationships from Arabic texts for automatic construction of an ontology
The task of building an ontology from a textual corpus starts with the conceptualization phase, which extracts ontology concepts. These concepts are linked by semantic relationships. In this paper, we describe an approach to the construction of an ...
Automatic generation of probabilistic relationships for improving schema matching
Schema matching is the problem of finding relationships among concepts across data sources that are heterogeneous in format and in structure. Starting from the ''hidden meaning'' associated with schema labels (i.e. class/attribute names), it is possible ...
An approach to acquire semantic relationships between words from web document
ICWL'05: Proceedings of the 4th international conference on Advances in Web-Based LearningIn this paper, we focus on the semantic relationships acquisition from Chinese web documents motivated by the large requirement of web question answering system in e-Learning. With our scheme, we dwindle in numbers of text to be analyzed and obtain ...
Comments