Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Computational Linguistics
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Information flow: the logic of distributed systems
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
ICCI '93 Proceedings of the Fifth International Conference on Computing and Information
Associative and Formal Concepts
ICCS '02 Proceedings of the 10th International Conference on Conceptual Structures: Integration and Interfaces
Corpus-Driven Unsupervised Learning of Verb Subcategorization Frames
AI*IA '97 Proceedings of the 5th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Knowledge-based multilingual document analysis
SEMANET '02 Proceedings of the 2002 workshop on Building and using semantic networks - Volume 11
An incremental algorithm to construct a lattice of set intersections
Science of Computer Programming
Concept Formation in Linguistic Ontologies
ICCS '09 Proceedings of the 17th International Conference on Conceptual Structures: Conceptual Structures: Leveraging Semantic Technologies
Using formal concept analysis to leverage ontology-based Acu-point knowledge system
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
Using formal concept analysis to leverage ontology-based Yoga knowledge system
WSEAS Transactions on Information Science and Applications
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Formal concept analysis as a methodology of data analysis and knowledge representation has potential to be applied to a variety of linguistic problems. First, linguistic applications often involve the identification and analysis of features, such as phonemes or syntactical or grammatical markers. Formal concept analysis can be used to record and analyze such features. The line diagrams of concept lattices can be used for communication among linguists about such features (see section 2). Second, modeling and storage of lexical information is becoming increasingly important for natural language processing tasks. This causes a growing need for detailed lexical databases, which should preferably be automatically constructed. Section 3 describes the role that formal concept analysis can play in the automated or semi-automated construction of lexical databases from corpora. Third, lexical databases usually contain hierarchical components, such as hyponymy or type hierarchies. Because formal concept lattices are a natural representation of hierarchies and classifications, lexical databases can often be represented or analyzed using formal concept analysis. This is described in section 4. It should be remarked that because this paper appears in a collection volume of papers on formal concept analysis, the underlying notions, such as formal concept, formal object and attribute, and lattice, are not further explained in this paper. The reader is referred to Ganter & Wille (1999) for detailed information on formal concept analysis.