Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
From frequency to meaning: vector space models of semantics
Journal of Artificial Intelligence Research
The Linear Ordering Problem: Exact and Heuristic Methods in Combinatorial Optimization
The Linear Ordering Problem: Exact and Heuristic Methods in Combinatorial Optimization
SemEval-2012 task 1: English Lexical Simplification
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
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Our system breaks down the problem of ranking a list of lexical substitutions according to how simple they are in a given context into a series of pairwise comparisons between candidates. For this we learn a binary classifier. As only very little training data is provided, we describe a procedure for generating artificial unlabeled data from Wordnet and a corpus and approach the classification task as a semi-supervised machine learning problem. We use a co-training procedure that lets each classifier increase the other classifier's training set with selected instances from an unlabeled data set. Our features include n-gram probabilities of candidate and context in a web corpus, distributional differences of candidate in a corpus of "easy" sentences and a corpus of normal sentences, syntactic complexity of documents that are similar to the given context, candidate length, and letter-wise recognizability of candidate as measured by a trigram character language model.