Maximum-Entropy Tagger
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A Maximum-Entropy Tagger is a String Tagging Model that is also a Maximum-Entropy Model.
- AKA: Max-Ent Tagger.
- Context:
- It can (typically) be a Maximum Entropy Markov_Model.
- See: Maximum Entropy Principle, CRF Tagger.
References
2002
- (Collins, 2002a) ⇒ Michael Collins. (2002). “Ranking Algorithms for Named–Entity Extraction: Boosting and the voted perceptron.” In: Proceedings of the ACL Conference (ACL 2002).
- QUOTE: As a baseline model we used a maximum entropy tagger, very similar to the ones described in (Ratnaparkhi 1996; Borthwick et. al 1998; McCallum et al. 2000). Max-ent taggers have been shown to be highly competitive on a number of tagging tasks, such as part-of-speech tagging (Ratnaparkhi 1996), named-entity recognition (Borthwick et. al 1998), and information extraction tasks (McCallum et al. 2000). Thus the maximum-entropy tagger we used represents a serious baseline for the task.