Maximum Entropy OOV Word Detection System
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A Maximum Entropy OOV Word Detection System is an Out-Of-Vocabulary (OOV) Word Detection System that is based on the maximum entropy model.
- AKA: MaxEnt OOV Detection System.
- Context:
- It can solve a Maximum Entropy OOV Word Detection Task by implementing Maximum Entropy OOV Word Detection Algorithm.
- Example(s):
- Counter-Example(s):
- See: Word Embedding System, Text Generation System, Text Translation System, Natural Language Processing System.
References
2010
- (Parada et al., 2010) ⇒ Carolina Parada, Mark Dredze, Denis Filimonov, and Frederick Jelinek. (2010). “Contextual Information Improves OOV Detection in Speech.” In: Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL 2010).
- QUOTE: Our baseline system is the Maximum Entropy model with features from filler and confidence estimation models proposed by Rastrow et al. (2009a). Based on filler models, this approach models OOVs by constructing a hybrid system which combines words and sub-word units. Sub-word units, or fragments, are variable length phone sequences selected using statistical methods (Siohan and Bacchiani, 2005). The vocabulary contains a word and a fragment lexicon; fragments are used to represent OOVs in the language model text. Language model training text is obtained by replacing low frequency words (assumed OOVs) by their fragment representation. Pronunciations for OOVs are obtained using grapheme to phoneme models (Chen, 2003) (...).
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