Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Learning linear, sparse, factorial codes
Learning linear, sparse, factorial codes
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This paper introduces an unsupervised algorithm that collects senses contained in WordNet to explain words, whose meaning is unknown, but plenty of documents are available that contain the word in that unknown sense. Based on the widely accepted idea that the meaning of a word is characterized by its context, a neural network architecture was designed to reconstruct the meaning of the unknown word. The connections of the network were derived from word co-occurrences and word-sense statistics. The method was tested on 80 TOEFL synonym questions, from which 63 questions were answered correctly. This is comparable to other methods tested on the same questions, but using a larger corpus or richer lexical database. The approach was found robust against details of the architecture.