FrameNet Project
FrameNet Project is a corpus annotation project to create a FrameNet database.
- …
- Counter-Example(s):
- a Open Mind Common Sense Project, for a ConceptNet KB.
- See: PropBank.
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.
- 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
2012
- http://framenet.icsi.berkeley.edu/fndrupal/about
- QUOTE: The FrameNet project is building a lexical database of English that is both human- and machine-readable, based on annotating examples of how words are used in actual texts. From the student's point of view, it is a dictionary of more than 10,000 word senses, most of them with annotated examples that show the meaning and usage. For the researcher in Natural Language Processing, the more than 170,000 manually annotated sentences provide a unique training dataset for semantic role labeling, used in applications such as information extraction, machine translation, event recognition, sentiment analysis, etc. For students and teachers of linguistics it serves as a valence dictionary, with uniquely detailed evidence for the combinatorial properties of a core set of the English vocabulary. The project has been in operation at the International Computer Science Institute in Berkeley since 1997, supported primarily by the National Science Foundation, and the data is freely available for download; it has been downloaded and used by researchers around the world for a wide variety of purposes (See FrameNet users).
FrameNet is based on a theory of meaning called Frame Semantics, deriving from the work of Charles J. Fillmore and colleagues (Fillmore 1976, 1977, 1982, 1985, Fillmore and Baker 2001, 2010). The basic idea is straightforward: that the meanings of most words can best be understood on the basis of a semantic frame: a description of a type of event, relation, or entity and the participants in it. For example, the concept of cooking typically involves a person doing the cooking (Cook), the food that is to be cooked (Food), something to hold the food while cooking (Container) and a source of heat (Heating_instrument). In the FrameNet project, this is represented as a frame called Apply_heat, and the Cook, Food, Heating_instrument and Container are called frame elements (FEs). Words that evoke this frame, such as fry, bake, boil, and broil, are called lexical units (LUs) of the Apply_heat frame. Other frames are more complex, such as Revenge, which involves more FEs (Offender, Injury, Injured_Party, Avenger, and Punishment) and others are simpler, such as Placing, with only an Agent (or Cause), a thing that is placed (called a Theme) and the location in which it is placed (Goal). The job of FrameNet is to define the frames and to annotate sentences to show how the FEs fit syntactically around the word that evokes the frame, as in the following examples of Apply_heat and Revenge:
- … [Cook the boys] … GRILL [Food their catches] [Heating_instrument on an open fire].
- [Avenger I] 'll GET EVEN [Offender with you] [Injury for this]!
- In the simplest case, the frame-evoking word is a verb and the FEs are its syntactic dependents, as in the example above where boys is the subject of the verb grill, their catches is the direct object, and on an open fire is a prepositional phrase modifying grill, but LUs can also be event nouns such as retaliation, also in the Revenge frame:
- [Punishment This attack was conducted] [Support in] RETALIATION [Injury for the U.S. bombing raid on Tripoli...
- or adjectives such as asleep in the Sleep frame:
- [Sleeper They] [Copula were] ASLEEP [Duration for hours]
- The lexical entry for each LU is derived from such annotations, and specifies the ways in which FEs are realized in syntactic structures headed by the word.
Many common nouns, such as tree, hat or tower, usually serve as dependents which head FEs, rather than clearly evoking their own frames, so we have devoted less effort to annotating them, since information about them is available from other lexicons, such as WordNet (Miller et al. 1990).
We do, however, recognize that such nouns also have a minimal frame structure of their own, and in fact, the FrameNet database contains slightly more nouns than verbs.
Formally, FrameNet annotations are sets of triples that represent the FE realizations for each annotated sentence, each consisting of a frame element name (for example, Food), a grammatical function (say, Object) and a phrase type (say, noun phrase (NP)). We can think of these three types of annotation on each FE as " layers ", but the grammatical function and phrase-type layers are not displayed in the web-based report system, to avoid visual clutter. The downloadable XML version of the data includes these three layers (and several more not discussed here) for all of the annotated sentences, along with complete frame and FE descriptions, frame-frame relations, and lexical entries for each annotated LU. Most of the annotations are of separate sentences annotated for only one LU, but there are also a collection of texts in which all the frame-evoking words have been annotated; the overlapping frames provide a rich representation of much of the meaning of the entire text. The FrameNet team have defined more than 1,000 semantic frames and have linked them together by a system of frame relations, which relate more general frames to more specific ones and provide a basis for reasoning about events and intentional actions.
Because the frames are basically semantic, they are often similar across languages; for example, frames about buying and selling involve the FEs Buyer, Seller, Goods, and Money, regardless of the language in which they are expressed. Several projects are underway to build FrameNets parallel to the English FrameNet project for languages around the the world, including Spanish, German, Chinese, and Japanese, and frame semantic analysisand annotation has been carried out in specialized areas from legal terminology to soccer to tourism.
- QUOTE: The FrameNet project is building a lexical database of English that is both human- and machine-readable, based on annotating examples of how words are used in actual texts. From the student's point of view, it is a dictionary of more than 10,000 word senses, most of them with annotated examples that show the meaning and usage. For the researcher in Natural Language Processing, the more than 170,000 manually annotated sentences provide a unique training dataset for semantic role labeling, used in applications such as information extraction, machine translation, event recognition, sentiment analysis, etc. For students and teachers of linguistics it serves as a valence dictionary, with uniquely detailed evidence for the combinatorial properties of a core set of the English vocabulary. The project has been in operation at the International Computer Science Institute in Berkeley since 1997, supported primarily by the National Science Foundation, and the data is freely available for download; it has been downloaded and used by researchers around the world for a wide variety of purposes (See FrameNet users).
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
- (Gildea and Jurafsky, 2002) ⇒ D. Gildea and Daniel Jurafsky. (2002). “Automatic Labeling of Semantic Roles. Computational Linguistics Vol. 28:3, 245-288. (paper.pdf)
2000
- (Gildea and Jurafsky, 2000) ⇒ D. Gildea and Daniel Jurafsky. (2000). “Automatic labeling of semantic roles.” In: Proceedings of ACL-2000. (paper.pdf)
1998
- (Baker et al., 1998) ⇒ Collin F. Baker, Charles J. Fillmore, and John B. Lowe. (1998). “The Berkeley FrameNet Project.” In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics - Volume 1. doi:10.3115/980845.980860
- QUOTE: FrameNet is a three-year NSF-supported project in corpus-based computational lexicography, now in its second year (NSF IRI-9618838, "Tools for Lexicon Building"). The project's key features are (a) a commitment to corpus evidence for semantic and syntactic generalizations, and (b) the representation of the valences of its target words (mostly nouns, adjectives, and verbs) in which the semantic portion makes use of frame semantics. The resulting database will contain (a) descriptions of the semantic frames underlying the meanings of the words described, and (b) the valence representation (semantic and syntactic) of several thousand words and phrases, each accompanied by (c) a representative collection of annotated corpus attestations, which jointly exemplify the observed linkings between “frame elements” and their syntactic realizations (e.g. grammatical function, phrase type, and other syntactic traits). This report will present the project's goals and workflow, and information about the computational tools that have been adapted or created in-house for this work.
1977
- (Fillmore, 1977) ⇒ Charles J. Fillmore. (1977). “The Case for Case Reopened." Syntax and Semantics, 8.
1976
- (Fillmore, 1976) ⇒ Charles J. Fillmore. (1976). “Frame Semantics and the Nature of Language *." Annals of the New York Academy of Sciences, 280(1).