Identifying Image Schemas: Towards Automatic Image Schema Extraction 1

One of the missing pieces before image schemas can be used in conceptual blending and artificial intelligence is a method to automatically identify image schemas in natural language. In order to investigate this problem, the PATHfollowing family introduced in Chapter 3 will be empirically investigated by using a natural language corpus to detect existing members of the family and detect possible additional candidates. The experiment relies on a method of syntactic pattern matching using words strongly associated with movement and processes. The experiment includes four different languages to strengthen the idea that PATH-following in abstract domains (here finance) is not only found in one language but universal as assumed through their embodied manifestation. The experiment found that approximately 1/3 of extracted words could be image-schematic and could not only provide linguistic support for the members of the PATH family but also provide additional candidates.