Yarowsky, D. (1994) Decision Lists for Lexical Ambiguity Resolution: Application to Accent Restoration in Spanish and French. Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, NM, 88-95.
http://dx.doi.org/10.3115/981732.981745

has been cited by the following article:

• TITLE: Improving the Collocation Extraction Method Using an Untagged Corpus for Persian Word Sense Disambiguation

  AUTHORS: Noushin Riahi, Fatemeh Sedghi

  KEYWORDS: Collocation Extraction, Word Sense Disambiguation, Untagged Corpus, Decision List

  JOURNAL NAME: Journal of Computer and Communications, Vol.4 No.4, April 22, 2016

  ABSTRACT: Word sense disambiguation is used in many natural language processing fields. One of the ways of disambiguation is the use of decision list algorithm which is a supervised method. Supervised methods are considered as the most accurate machine learning algorithms but they are strongly influenced by knowledge acquisition bottleneck which means that their efficiency depends on the size of the tagged training set, in which their preparation is difficult, time-consuming and costly. The proposed method in this article improves the efficiency of this algorithm where there is a small tagged training set. This method uses a statistical method for collocation extraction from a big untagged corpus. Thus, the more important collocations which are the features used for creation of learning hypotheses will be identified. Weighting the features improves the efficiency and accuracy of a decision list algorithm which has been trained with a small training corpus.