FrameNet Project

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FrameNet Project is a corpus annotation project to create a FrameNet database.



References

2015

  • (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/FrameNet Retrieved:2015-3-28.
    • In computational linguistics, FrameNet is a project housed at the International Computer Science Institute in Berkeley, California which produces an electronic resource based on a theory of meaning called

       frame semantics. FrameNet reveals for example that the sentence "John sold a car to Mary" essentially describes the same basic situation (semantic frame) as "Mary bought a car from John", just from a different perspective. A semantic frame can be thought of as a conceptual structure describing an event, relation, or object and the participants in it. The FrameNet lexical database contains around 1,200 semantic frames, 13,000 lexical units (a pairing of a word with a meaning; polysemous words are represented by several lexical units) and over 190,000 example sentences. FrameNet is largely the creation of Charles J. Fillmore, who developed the theory of frame semantics that the project is based on, and was initially the project leader when the project began in 1997. Collin Baker became the project manager in 2000. The FrameNet project has been influential in both linguistics and natural language processing, where it led to the task of automatic Semantic Role Labeling.

2012

2003

  • (Fleischman et al., 2003) ⇒ M. Fleischman and N. Kwon and Eduard Hovy. (2003). “Maximum entropy models for FrameNet classification. In: Proceedings of HLT/NAACL-2003. (paper.pdf)
    • QUOTE: The FrameNet project seeks to annotate a large subset of the British National Corpus with seman-tic information. Annotations are based on Frame Semantics (Fillmore, 1976), in which frames are defined as schematic representations of situations involving various frame elements such as participants, props, and other conceptual roles. In each FrameNet sentence, a single target predicate is identified and all of its relevant frame elements are tagged with their semantic role (e.g., Agent, Judge), their syntactic phrase type (e.g., NP, PP), and their grammatical function (e.g., ex-ternal argument, object argument). Figure 1 shows an example of an annotated sentence and its appro-priate semantic frame. (Figure-1: She clapped her hands in inspiration. Frame: Body-Movement. Frame Elements: Agent Body Part Cause. Figure heading: Frame for lemma “clap” shown with three core frame elements and a sentence annotated with element type, phrase type, and grammatical function.) As of its first release in June 2002, FrameNet has made available 49,000 annotated sentences. The release contains 99,000 annotated frame ele-ments for 1462 distinct lexical predicates (927 verbs, 339 nouns, and 175 adjectives). While considerable in scale, the FrameNet database does not yet approach the magnitude of re-sources available for other NLP tasks. Each target predicate, for example, has on average only 30 sentences tagged. This data sparsity makes the task of learning a semantic classifier formidable, and in-creases the importance of the modeling framework that is employed.

2002

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1977

1976