Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Measuring Similarity between Ontologies
EKAW '02 Proceedings of the 13th International Conference on Knowledge Engineering and Knowledge Management. Ontologies and the Semantic Web
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
A taxonomy for English nouns and verbs
ACL '81 Proceedings of the 19th annual meeting on Association for Computational Linguistics
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Automatic construction of a hypernym-labeled noun hierarchy from text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Lexical chains for question answering
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites
Computational Linguistics
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Word Sense Induction Using Graphs of Collocations
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Semeval-2007 task 02: evaluating word sense induction and discrimination systems
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
Word sense induction & disambiguation using hierarchical random graphs
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
MENTA: inducing multilingual taxonomies from wikipedia
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Measuring the impact of sense similarity on word sense induction
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
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Taxonomies are an important resource for a variety of Natural Language Processing (NLP) applications. Despite this, the current state-of-the-art methods in taxonomy learning have disregarded word polysemy, in effect, developing taxonomies that conflate word senses. In this paper, we present an unsupervised method that builds a taxonomy of senses learned automatically from an unlabelled corpus. Our evaluation on two WordNet-derived taxonomies shows that the learned taxonomies capture a higher number of correct taxonomic relations compared to those produced by traditional distributional similarity approaches that merge senses by grouping the features of each word into a single vector.