Abstract
This paper presents novel frame-based semantic patterns, exploiting frame element and frame annotations, provided by FrameNet for relation extraction. The proposed frame-based patterns are evaluated against state-of-the-art dependency based syntactic patterns and lexico-syntactic patterns, on three independent datasets that differ in size and construction. The results show that the proposed frame-based patterns significantly improve performance, both in terms of scoring higher precision and higher recall for relation extraction, in comparison to dependency and lexico-syntactic patterns on all three datasets.
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Notes
- 1.
The online demo of semafor is available at http://demo.ark.cs.cmu.edu/parse.
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Mandya, A., Bollegala, D., Coenen, F., Atkinson, K. (2018). Frame-Based Semantic Patterns for Relation Extraction. In: Hasida, K., Pa, W. (eds) Computational Linguistics. PACLING 2017. Communications in Computer and Information Science, vol 781. Springer, Singapore. https://doi.org/10.1007/978-981-10-8438-6_5
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