Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Class-Based Construction of a Verb Lexicon
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Similarity of Semantic Relations
Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Adding predicate argument structure to the Penn TreeBank
HLT '02 Proceedings of the second international conference on Human Language Technology Research
A general feature space for automatic verb classification
Natural Language Engineering
Methods for domain-independent information extraction from the web: an experimental comparison
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Modeling reciprocity in social interactions with probabilistic latent space models
Natural Language Engineering
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In this paper we address the problem of identifying reciprocal relationships in English. In particular we introduce an algorithm that semi-automatically discovers patterns encoding reciprocity based on a set of simple but effective pronoun templates. Using a set of most frequently occurring patterns, we extract pairs of reciprocal pattern instances by searching the web. Then we apply two unsupervised clustering procedures to form meaningful clusters of such reciprocal instances. The pattern discovery procedure yields an accuracy of 97%, while the clustering procedures indicate accuracies of 91% and 82%. Moreover, the resulting set of 10,882 reciprocal instances represent a broad-coverage resource.