NER Predictor Feature
An NER Predictor Feature is a Predictor Feature that can be used by a Supervised NER Algorithm.
- AKA: NER Feature.
- See: POS Predictor Feature, Concept Mention Predictor Feature.
- (Nadeau & Sekine, 2007) ⇒ David Nadeau, and Satoshi Sekine. (2007). “A Survey of Named Entity Recognition and Classification.” In: Lingvisticae Investigationes, 30(1).
- (Tasi et al., 2006) ⇒ Tzong-han Tsai, Wen-Chi Chou, Shih-Hung Wu, Ting-Yi Sung, Jieh Hsiang, and Wen-Lian Hsu. (2006). “Integrating Linguistic Knowledge into a Conditional Random Field Framework to Identify Biomedical Named Entities.” In: Expert Systems with Applications: An International Journal, 30(1). doi:10.1016/j.eswa.2005.09.072
- In the NER problem, we regard each word in a sentence as a token. Each token is associated with a tag that indicates the category of the NE and the location of the token within the NE, for example, B_c, I_c where [math]c[/math] is a category. These two tags denote respectively the beginning token and the following token of an NE in category c. In addition, we use the tag $O$ to indicate that a token is not part of an NE. The NER problem can then be phrased as the problem of assigning one of 2n + 1 tags to each token, where n is the number of NE categories. In the JNLPBA 2004 task, there are 5 named entity categories and 11 tags. For example, one way to tag the phrase IL-2 gene expression, CD28, and NFkappa B in a paper is “B-DNA, I-DNA, O, O, B-protein, O, O, B-protein, I-protein”.
- (Minkov et al., 2005) ⇒ Einat Minkov, Richard C. Wang, and William W. Cohen. (2005). “Extracting personal names from email: applying named entity recognition to informal text.” In: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing. doi:10.3115/1220575.1220631
- (McCallum & Li, 2003) ⇒ Andrew McCallum, and Wei Li. (2003). “Early Results for Named Entity Recognition with Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons.” In: Proceedings of Seventh Conference on Natural Language Learning (CoNLL 2003).