Learning Morpho-Lexical Probabilities from an Untagged Corpus with an Application to Hebrew

By M. Levinger, U. Ornan, A. Itai. 1995

Subfields: Natural Language Processing, Computational Linguistics & Semantics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Noah A. Smith
2. Michael Elhadad
3. Roy W. Tromble
4. Meni Adler
5. David A. Smith
6. Yoav Goldberg
7. Yejin Choi
8. Dan Roth

In Computational Linguistics & Semantics

1. Noah A. Smith
2. Michael Elhadad
3. Roy W. Tromble
4. Meni Adler
5. David A. Smith

Citations in the corpus

• An Unsupervised Morpheme-Based HMM for Hebrew Morphological Disambiguation (2006, methodology): “Furthermore, as mentioned above, even the use of a sophisticated context-free tagger, based on (Levinger et al., 1995), gives low accuracy of 78.2%.”
• Context-Based Morphological Disambiguation with Random Fields (2005, methodology): “2 Probabilistic modeling of what we call the morphological channel was first carried out by Levinger et al. (1995), who used unlabeled data to estimate p ( ~y | x ) for Hebrew, with the support defined by a dictionary.”