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Iliyan Angelov
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.. Copyright (C) 2001-2025 NLTK Project
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.. For license information, see LICENSE.TXT
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========
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FrameNet
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========
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The FrameNet corpus is a lexical database of English that is both human-
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and machine-readable, based on annotating examples of how words are used
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in actual texts. FrameNet is based on a theory of meaning called Frame
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Semantics, deriving from the work of Charles J. Fillmore and colleagues.
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The basic idea is straightforward: that the meanings of most words can
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best be understood on the basis of a semantic frame: a description of a
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type of event, relation, or entity and the participants in it. For
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example, the concept of cooking typically involves a person doing the
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cooking (Cook), the food that is to be cooked (Food), something to hold
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the food while cooking (Container) and a source of heat
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(Heating_instrument). In the FrameNet project, this is represented as a
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frame called Apply_heat, and the Cook, Food, Heating_instrument and
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Container are called frame elements (FEs). Words that evoke this frame,
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such as fry, bake, boil, and broil, are called lexical units (LUs) of
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the Apply_heat frame. The job of FrameNet is to define the frames
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and to annotate sentences to show how the FEs fit syntactically around
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the word that evokes the frame.
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------
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Frames
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------
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A Frame is a script-like conceptual structure that describes a
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particular type of situation, object, or event along with the
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participants and props that are needed for that Frame. For
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example, the "Apply_heat" frame describes a common situation
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involving a Cook, some Food, and a Heating_Instrument, and is
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evoked by words such as bake, blanch, boil, broil, brown,
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simmer, steam, etc.
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We call the roles of a Frame "frame elements" (FEs) and the
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frame-evoking words are called "lexical units" (LUs).
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FrameNet includes relations between Frames. Several types of
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relations are defined, of which the most important are:
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- Inheritance: An IS-A relation. The child frame is a subtype
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of the parent frame, and each FE in the parent is bound to
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a corresponding FE in the child. An example is the
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"Revenge" frame which inherits from the
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"Rewards_and_punishments" frame.
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- Using: The child frame presupposes the parent frame as
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background, e.g the "Speed" frame "uses" (or presupposes)
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the "Motion" frame; however, not all parent FEs need to be
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bound to child FEs.
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- Subframe: The child frame is a subevent of a complex event
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represented by the parent, e.g. the "Criminal_process" frame
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has subframes of "Arrest", "Arraignment", "Trial", and
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"Sentencing".
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- Perspective_on: The child frame provides a particular
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perspective on an un-perspectivized parent frame. A pair of
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examples consists of the "Hiring" and "Get_a_job" frames,
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which perspectivize the "Employment_start" frame from the
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Employer's and the Employee's point of view, respectively.
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To get a list of all of the Frames in FrameNet, you can use the
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`frames()` function. If you supply a regular expression pattern to the
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`frames()` function, you will get a list of all Frames whose names match
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that pattern:
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>>> from pprint import pprint
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>>> from operator import itemgetter
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>>> from nltk.corpus import framenet as fn
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>>> from nltk.corpus.reader.framenet import PrettyList
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>>> x = fn.frames(r'(?i)crim')
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>>> x.sort(key=itemgetter('ID'))
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>>> x
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[<frame ID=200 name=Criminal_process>, <frame ID=500 name=Criminal_investigation>, ...]
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>>> PrettyList(sorted(x, key=itemgetter('ID')))
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[<frame ID=200 name=Criminal_process>, <frame ID=500 name=Criminal_investigation>, ...]
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To get the details of a particular Frame, you can use the `frame()`
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function passing in the frame number:
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>>> from pprint import pprint
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>>> from nltk.corpus import framenet as fn
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>>> f = fn.frame(202)
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>>> f.ID
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202
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>>> f.name
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'Arrest'
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>>> f.definition
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"Authorities charge a Suspect, who is under suspicion of having committed a crime..."
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>>> len(f.lexUnit)
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11
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>>> pprint(sorted([x for x in f.FE]))
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['Authorities',
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'Charges',
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'Co-participant',
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'Manner',
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'Means',
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'Offense',
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'Place',
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'Purpose',
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'Source_of_legal_authority',
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'Suspect',
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'Time',
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'Type']
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>>> pprint(f.frameRelations)
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[<Parent=Intentionally_affect -- Inheritance -> Child=Arrest>, <Complex=Criminal_process -- Subframe -> Component=Arrest>, ...]
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The `frame()` function shown above returns a dict object containing
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detailed information about the Frame. See the documentation on the
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`frame()` function for the specifics.
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You can also search for Frames by their Lexical Units (LUs). The
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`frames_by_lemma()` function returns a list of all frames that contain
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LUs in which the 'name' attribute of the LU matches the given regular
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expression. Note that LU names are composed of "lemma.POS", where the
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"lemma" part can be made up of either a single lexeme (e.g. 'run') or
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multiple lexemes (e.g. 'a little') (see below).
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>>> PrettyList(sorted(fn.frames_by_lemma(r'(?i)a little'), key=itemgetter('ID')))
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[<frame ID=189 name=Quanti...>, <frame ID=2001 name=Degree>]
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-------------
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Lexical Units
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-------------
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A lexical unit (LU) is a pairing of a word with a meaning. For
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example, the "Apply_heat" Frame describes a common situation
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involving a Cook, some Food, and a Heating Instrument, and is
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_evoked_ by words such as bake, blanch, boil, broil, brown,
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simmer, steam, etc. These frame-evoking words are the LUs in the
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Apply_heat frame. Each sense of a polysemous word is a different
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LU.
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We have used the word "word" in talking about LUs. The reality
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is actually rather complex. When we say that the word "bake" is
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polysemous, we mean that the lemma "bake.v" (which has the
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word-forms "bake", "bakes", "baked", and "baking") is linked to
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three different frames:
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- Apply_heat: "Michelle baked the potatoes for 45 minutes."
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- Cooking_creation: "Michelle baked her mother a cake for her birthday."
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- Absorb_heat: "The potatoes have to bake for more than 30 minutes."
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These constitute three different LUs, with different
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definitions.
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Multiword expressions such as "given name" and hyphenated words
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like "shut-eye" can also be LUs. Idiomatic phrases such as
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"middle of nowhere" and "give the slip (to)" are also defined as
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LUs in the appropriate frames ("Isolated_places" and "Evading",
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respectively), and their internal structure is not analyzed.
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Framenet provides multiple annotated examples of each sense of a
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word (i.e. each LU). Moreover, the set of examples
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(approximately 20 per LU) illustrates all of the combinatorial
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possibilities of the lexical unit.
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Each LU is linked to a Frame, and hence to the other words which
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evoke that Frame. This makes the FrameNet database similar to a
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thesaurus, grouping together semantically similar words.
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In the simplest case, frame-evoking words are verbs such as
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"fried" in:
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"Matilde fried the catfish in a heavy iron skillet."
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Sometimes event nouns may evoke a Frame. For example,
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"reduction" evokes "Cause_change_of_scalar_position" in:
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"...the reduction of debt levels to $665 million from $2.6 billion."
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Adjectives may also evoke a Frame. For example, "asleep" may
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evoke the "Sleep" frame as in:
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"They were asleep for hours."
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Many common nouns, such as artifacts like "hat" or "tower",
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typically serve as dependents rather than clearly evoking their
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own frames.
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Details for a specific lexical unit can be obtained using this class's
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`lus()` function, which takes an optional regular expression
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pattern that will be matched against the name of the lexical unit:
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>>> from pprint import pprint
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>>> PrettyList(sorted(fn.lus(r'(?i)a little'), key=itemgetter('ID')))
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[<lu ID=14733 name=a little.n>, <lu ID=14743 name=a little.adv>, ...]
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You can obtain detailed information on a particular LU by calling the
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`lu()` function and passing in an LU's 'ID' number:
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>>> from pprint import pprint
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>>> from nltk.corpus import framenet as fn
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>>> fn.lu(256).name
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'foresee.v'
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>>> fn.lu(256).definition
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'COD: be aware of beforehand; predict.'
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>>> fn.lu(256).frame.name
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'Expectation'
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>>> fn.lu(256).lexemes[0].name
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'foresee'
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Note that LU names take the form of a dotted string (e.g. "run.v" or "a
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little.adv") in which a lemma precedes the "." and a part of speech
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(POS) follows the dot. The lemma may be composed of a single lexeme
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(e.g. "run") or of multiple lexemes (e.g. "a little"). The list of
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POSs used in the LUs is:
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v - verb
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n - noun
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a - adjective
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adv - adverb
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prep - preposition
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num - numbers
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intj - interjection
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art - article
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c - conjunction
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scon - subordinating conjunction
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For more detailed information about the info that is contained in the
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dict that is returned by the `lu()` function, see the documentation on
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the `lu()` function.
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-------------------
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Annotated Documents
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-------------------
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The FrameNet corpus contains a small set of annotated documents. A list
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of these documents can be obtained by calling the `docs()` function:
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>>> from pprint import pprint
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>>> from nltk.corpus import framenet as fn
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>>> d = fn.docs('BellRinging')[0]
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>>> d.corpname
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'PropBank'
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>>> d.sentence[49]
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full-text sentence (...) in BellRinging:
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<BLANKLINE>
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<BLANKLINE>
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[POS] 17 tags
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<BLANKLINE>
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[POS_tagset] PENN
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<BLANKLINE>
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[text] + [annotationSet]
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<BLANKLINE>
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`` I live in hopes that the ringers themselves will be drawn into
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***** ******* *****
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Desir Cause_t Cause
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[1] [3] [2]
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<BLANKLINE>
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that fuller life .
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******
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Comple
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[4]
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(Desir=Desiring, Cause_t=Cause_to_make_noise, Cause=Cause_motion, Comple=Completeness)
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<BLANKLINE>
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>>> d.sentence[49].annotationSet[1]
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annotation set (...):
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<BLANKLINE>
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[status] MANUAL
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<BLANKLINE>
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[LU] (6605) hope.n in Desiring
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<BLANKLINE>
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[frame] (366) Desiring
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<BLANKLINE>
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[GF] 2 relations
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<BLANKLINE>
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[PT] 2 phrases
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<BLANKLINE>
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[text] + [Target] + [FE] + [Noun]
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<BLANKLINE>
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`` I live in hopes that the ringers themselves will be drawn into
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- ^^^^ ^^ ***** ----------------------------------------------
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E supp su Event
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<BLANKLINE>
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that fuller life .
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-----------------
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<BLANKLINE>
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(E=Experiencer, su=supp)
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<BLANKLINE>
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<BLANKLINE>
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