Combining Dictionary- and Corpus-Based Concept Extraction

Concept extraction is an increasingly popular topic in deep text analysis. Concepts are individual content elements. Their extraction offers thus an overview of the content of the material from which they were extracted. In the case of domain-specific material, concept extraction boils down to term identification. The most straightforward strategy for term identification is a look up in existing terminological resources. In recent research, this strategy has a poor reputation because it is prone to scaling limitations due to neologisms, lexical variation, synonymy, etc., which make the terminology to be submitted to a constant change. For this reason, many works developed statistical techniques to extract concepts. But the existence of a crowdsourced resource such as Wikipedia is changing the landscape. We present a hybrid approach that combines state-of-the-art statistical techniques with the use of the large scale term acquisition tool BabelFy to perform concept extraction. The combination of both allows us to boost the performance, compared to approaches that use these techniques separately.