The Journal of Machine Learning Research
Clustering by committee
Corpus-based Learning of Analogies and Semantic Relations
Machine Learning
Espresso: leveraging generic patterns for automatically harvesting semantic relations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Classification-Based Filtering of Semantic Relatedness in Hypernymy Extraction
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Derivational relations in Czech WordNet
ACL '07 Proceedings of the Workshop on Balto-Slavonic Natural Language Processing: Information Extraction and Enabling Technologies
SemEval-2010 task 8: multi-way classification of semantic relations between pairs of nominals
DEW '09 Proceedings of the Workshop on Semantic Evaluations: Recent Achievements and Future Directions
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
From frequency to meaning: vector space models of semantics
Journal of Artificial Intelligence Research
WCCL: a morpho-syntactic feature toolkit
TSD'11 Proceedings of the 14th international conference on Text, speech and dialogue
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Derivational relations are an important part of the lexical semantics system in many languages, especially those of rich inflection. They represent wide variety of semantic oppositions. Analysis of morphological word forms in terms of prefixes and suffixes provides limited information about their semantics. We propose a method of semantic classification of the potential derivational pairs. The method is based on supervised learning, but requires only a list of word pairs assigned to the derivational relations. The classification was based on a combination of features describing distribution of a derivative and derivational base in a large corpus together with their morphological and morpho-syntactic properties. The method does not use patterns based on close co-occurrence of a derivative and its base. Two classification schemes were evaluated: a multiclass and a cascade of binary classifiers, both expressed good performance in experiments on the selected nominal derivational relations.