Applications
FrameNet has proven useful in a number of computational applications, because computers need additional knowledge in order to recognize that "John sold a car to Mary" and "Mary bought a car from John" describe essentially same situation, despite using two very different verbs, different prepositions and a different word order. FrameNet has been used in applications like question answering, paraphrasing, recognizing textual entailment, and information extraction, either directly or by means of Semantic Role Labeling tools. The first automatic system for Semantic Role Labeling (SRL, sometimes also referred to as "shallow semantic parsing") was developed by Daniel Gildea and Daniel Jurafsky based on FrameNet in 2002, and Semantic Role Labelling has since become one of the standard tasks in natural language processing.
Since frames are essentially semantic descriptions, they are similar across languages, and several projects have arisen over the years that have relied on the original FrameNet as the basis for additional non-English FrameNets, for Spanish, Japanese, and German, among others.
Read more about this topic: Frame Net