Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
378711 | Data & Knowledge Engineering | 2014 | 21 Pages |
Extracting semantic associations from text corpora is an important problem with several applications. It is well understood that semantic associations from text can be discerned by observing patterns of co-occurrences of terms. However, much of the work in this direction has been piecemeal, addressing specific kinds of semantic associations. In this work, we propose a generic framework, using which several kinds of semantic associations can be mined. The framework comprises a co-occurrence graph of terms, along with a set of graph operators. A methodology for using this framework is also proposed, where the properties of a given semantic association can be hypothesized and tested over the framework. To show the generic nature of the proposed model, four different semantic associations are mined over a corpus comprising of Wikipedia articles. The design of the proposed framework is inspired from cognitive science — specifically the interplay between semantic and episodic memory in humans.