Relational Pattern Recognition Task
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A Relational Pattern Recognition Task is a Pattern Recognition Task that can find linguistic patterns.
- AKA: Relational Pattern Mining Task, Relational Pattern Learning Task.
- Context:
- Context:
- It can be solved by a Relational Pattern Recognition System by implementing Relational Pattern Recognition Algorithms.
- It ranges from being a Supervised Relational Pattern Recognition Task to being an Unsupervised Relational Pattern Task.
- Example(s):
- Counter-Example(s):
- See: Pattern Recognition System, Knowledge Discovery, Correlation, Exploratory Data Analysis, Graph Pattern Mining Task, Structured Pattern Mining Task.
References
2016a
- (Takase et al., 2016a) ⇒ Sho Takase, Naoaki Okazaki, and Kentaro Inui. (2016). “Modeling Semantic Compositionality of Relational Patterns.” In: Engineering Applications of Artificial Intelligence Journal, 50(C). doi:10.1016/j.engappai.2016.01.027
2016b
- (Takase et al., 2016b) ⇒ Sho Takase, Naoaki Okazaki, and Kentaro Inui. (2016). “Composing Distributed Representations of Relational Patterns.” In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL 2016). doi:DOI:10.18653/v1/p16-1215 arXiv:1707.07265
2011a
- (Zilles, 2011) ⇒ Michael Geilke, and Sandra Zilles. (2011). “Learning Relational Patterns.” In: Proceedings of International Conference on Algorithmic Learning Theory (ALT 2011). Lecture Notes in Computer Science. ISBN:978-3-642-24411-7, 978-3-642-24412-4, doi:10.1007/978-3-642-24412-4_10
2011b
- (Giacometti et al., 2011) ⇒ Arnaud Giacometti, Patrick Marcel, and Arnaud Soulet. (2011). “A Relational View of Pattern Discovery.” In: Proceedings of the 16th International Conference on Database systems for advanced applications - Volume Part I. ISBN:978-3-642-20148-6 DOI:10.1007/978-3-642-20149-3_13
2006
- (Bishop, 2006) ⇒ Christopher M. Bishop. (2006). "Pattern Recognition and Machine Learning". New York, NY : Springer, 2006. DOI:10.1117/1.2819119 ISBN:0-387-31073-8, 1-493-93843-6, 978-0387-31073-2, 978-1493-93843-8.