Linguistic applications of formal concept analysis.
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.
Priss, U. (2005). Linguistic applications of formal concept analysis. In B. Ganter, G. Stumme, & R. Wille (Eds.), Formal Concept Analysis, Foundations and Applications (149-160). Springer-Verlag. https://doi.org/10.1007/11528784_8
|Deposit Date||Jul 24, 2008|
|Peer Reviewed||Peer Reviewed|
|Book Title||Formal Concept Analysis, Foundations and Applications|
|Keywords||Epistemology; Linguistics; Computer programming;|
You might also like
Formalizing botanical taxonomies.
Modelling lexical databases with formal concept analysis.
Conceptual exploration of semantic mirrors.
FCA interpretation of relation algebra.
Bilingual word association networks.