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Interset is a means of converting among various tag sets in natural language processing. The core idea is similar to interlingua-based machine translation. Interset defines a set of features that are encoded by the various tag sets. The set of features should be as universal as possible. It does not need to encode everything that is encoded by any tag set but it should encode all information that people may want to access and/or port from one tag set to another. (The features and their values are very similar to those defined in Universal Dependencies. UD features are based on Interset. However, Interset still uses a slightly different notation, e.g. all features and values are lowercased.)

New tag sets are attached by writing a driver for them. Once the driver is ready, you can easily convert tags between the new set and any other set for which you also have a driver. This reusability is an obvious advantage over writing a targeted conversion procedure each time you need to convert between a particular pair of tag sets.


This research has been supported by the grant MSM 0021620838 of the Ministry of Education of the Czech Republic.

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