- (Jiang & Zhai, 2007a) ⇒ Jing Jiang, ChengXiang Zhai. (2007). “A Systematic Exploration of the Feature Space for Relation Extraction.” In: Proceedings of NAACL/HLT Conference (NAACL 2007).
- It suggests that the addition of bigram-based features is helpful. A simple analysis shows that the lift in F-measure from unigram to bigram on average is x ~1.09
- It suggests that the addition of trigram-based features is not significantly helpful. A simple anlaysis shows that the lift from the further addition of trigram is x ~1.02.
- ~35 http://scholar.google.com/scholar?q=%22A+Systematic+Exploration+of+the+Feature+Space+for+Relation+Extraction%22+2007
Relation extraction is the task of finding semantic relations between entities from text. The state-of-the-art methods for relation extraction are mostly based on statistical learning, and thus all have to deal with feature selection, which can significantly affect the classification performance. In this paper, we systematically explore a large space of features for relation extraction and evaluate the effectiveness of different feature subspaces. We present a general definition of feature spaces based on a graphic representation of relation instances, and explore three different representations of relation instances and features of different complexities within this framework. Our experiments show that using only basic unit features is generally sufficient to achieve state-of-the-art performance, while overinclusion of complex features may hurt the performance. A combination of features of different levels of complexity and from different sentence representations, coupled with task-oriented feature pruning, gives the best performance."
- (Bunescu and Mooney, 2005) ⇒ Razvan C. Bunescu and Raymond Mooney. (2005). “A Shortest Path Dependency Kernel for Relation Extraction."" In: Proceedings of HLT/EMNLP-2005.
- Razvan C. Bunescu and Raymond Mooney. 2005b. Subsequence kernels for relation extraction. In: Proceedings of NIPS.
- (Culotta and Sorensen, 2004) ⇒ Aron Culottaand J. S. Sorensen. (2004). “Dependency Tree Kernels for Relation Extraction.” In: Proceedings ofACL 2004.
- [[Chad Cumby] and Dan Roth. (2003). On kernel methods for relational learning. In: Proceedings of ICML.
- (HasegawaSG, 1994) ⇒ T. Hasegawa, Satoshi Sekine, and Ralph Grishman. (2004). “Discovering Relations among Named Entities from Large Corpora.”" In: Proceedings ofACL 2004.
- (Kambhatla, 2004) ⇒ Nanda Kambhatla. (2004). Combining lexical, syntactic, and semantic features with maximum entropy models for extracting relations. Poster In: Proceedings of [[ACL 2004]
- (Moschitti, 2004) ⇒ Alessandro Moschitti. (2004). “A study on Convolution Kernels for Shallow Semantic Parsing.” In: Proceedings of the 42-th Conference on Association for Computational Linguistic (ACL-2004).
- Jun Suzuki, Tsutomu Hirao, Yutaka Sasaki, and Eisaku Maeda. (2003). Hierarchical directed acyclic graph kernel: Methods for structured natural language data. In: Proceedings of ACL.
- Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella. (2003). Kernel methods for relation extraction. Journal of Machine Learning Research, 3:1083–1106.
- (ZhangZS, 2006) ⇒ M. Zhang, J. Zhang, and J. Su. (2006). “Exploring Syntactic Features for Relation Extraction using a Convolution Tree Kernel.” In: Proceedings of HLT-2006.
- (Zhang et al., 2006) ⇒ M. Zhang, J. Zhang, J. Su, and G. Zhou. (2006). “A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features.” In: Proceedings of COLING-ACL 2006.
- (Zhao and Grishman, 2005) ⇒ S. Zhao and Ralph Grishman. (2005). “Extracting Relations with Integrated Information Using Kernel Methods.” In: Proceedings of or ACL-2005.
- (Zhou et al., 2005) ⇒ G. Zhou, J. Su, J. Zhang, M. Zhang (2005). “Exploring Various Knowledge in Relation Extraction" In: Proceedings of ACL-2005.,
|2007 SystematicExplOrRelExtrFeatureSpace||Jing Jiang|
|A Systematic Exploration of the Feature Space for Relation Extraction||Proceedings of NAACL/HLT Conference||http://acl.ldc.upenn.edu/N/N07/N07-1015.pdf||2007|