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Semantic Frames: FrameNet

Semantic Frames: FrameNet. What is FrameNet?. FrameNet is an ongoing project at the International Computer Science Institute located in Berkeley California The primary goal is to convert semantic knowledge into something that is machine-readable, for use in NLP systems etc.

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Semantic Frames: FrameNet

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  1. Semantic Frames: FrameNet

  2. What is FrameNet? • FrameNet is an ongoing project at the International Computer Science Institute located in Berkeley California • The primary goal is to convert semantic knowledge into something that is machine-readable, for use in NLP systems etc. • This converted knowledge is then made available for free online to other research organizations.

  3. FrameNet • A semantic frame is a structure used to define the semantic meaning of a word. • The frame is a generalizable concept with recurring Frame Elements or themes that are recognized intuitively. • Frame elements are simply the separate elements which make up a frame.

  4. Frames • There are different types of frames to describe different situations. • For example, there are abstract frames (such as the “Replacement” frame), as well as specific frames (such as the “Apply_heat” frame).

  5. Abstract Frame: Replacement Frame • Pat replaced [Oldthe curtains][Newwith wooden blinds] • The FrameNet lexicon contains one lexical unit for the association of the verb replace with the Replacement Frame. • The lexicon is made up of similar associations of words and frames, then stored in the FrameNet Lexical Database

  6. Specific Frame: Apply_heat Frame • Boil[Foodthe rice][Durationfor 3 minutes][Mediumin water] then drain. • Here Boil is associated with the Apply_heat Frame to form another lexical unit. • Some additional possible associations with Apply_heat include: char, fry, grill, microwave, etc. • Frame Elements that Apply_heat can use include: cook, food, medium, duration, etc.

  7. Lexical Units and Frame Elements • These are the basics of FrameNet. • The Lexical Units represent words associated with frames,… • …and the Frame Elements label the different parts of the frame itself, such as Food, or Duration in the Apply_heat example, which can be used to describe how to cook food (which is done by applying heat).

  8. Inherited Frames • Frames can also be inherited from other frames, a good example is the Transportation frame. • Here the Driving and Riding_1 frames are inheriting the Transportation Frame.

  9. Adding Frames • It’s a fairly involved process, I’ll just touch on the basics. • FrameNet is added to and maintained by many volunteers. • The volunteer tasks are separated into 3 roles: Vanguard, Annotator, and Rearguard.

  10. Vanguard: Preparation • This is where the initial frame description and list of frame elements is developed. • The Vanguard also selects target words for the frame and syntactic patterns for each word and enters them into the Lexical Database

  11. Subcorpus Extraction: Automatic • Based on the Vanguard’s work, the system produces a collection of sentences representative of the frame with the target words. • Basically the system filters these sentences until there is a good number of appropriate example sentences to describe the frame. • If the automatic system fails to provide good example sentences, then better ones are chosen by hand.

  12. Annotator: Annotation… • Annotators use the FrameNet software and the Frame Database updated by the Vanguard. • They take the extracted example sentences and find canonical examples, patterns and problem sentences.

  13. Rearguard: Entry Writing • They review the lexical record produced by the Vanguard, the example sentences, and the Frame Element Groups taken from these sentences. • Using these, the Rearguard builds lemma entries for the lexical Database, as well as frame descriptions for the Frame Database.

  14. Using FrameNet • You can find it by going online and playing around with at least part of the system at: http://www.icsi.berkeley.edu/~framenet/ • In general, when using the parser, when it finds a word it links to the corresponding frame to suggest semantics about how that word can/should be used. • FrameNet tries to capture human insight into how that word can be used.

  15. FrameNet Future • FrameNet is very concerned about the quality of their frames, so they monitor for quality constantly. • The current goal is to have 7000 lexical units, meaning more than 250,000 annotated sentences, and more than half a million frame elements! • There is a long way to go, but there will be no stopping there, FrameNet will be constantly growing and evolving.

  16. References • Baker, Collin F., Fillmore, Charles J., and Lowe, John B. (1998): The Berkeley FrameNet Project. In Proceedings of the COLING-ACL, Montreal, Canada. • Baker, Collin F. and Hiroaki Sato (2003): The FrameNet Data and Software. Poster and Demonstration at Association for Computational Linguistics, Sapporo, Japan. • Ruggieri, A. and Pakhomov, S. (2004): A Corpus Driven Approach Applying the “Frame Semantic” Method for Modeling Functional Status Terminology. In Proc. MedInfo 2004.

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