Frame Semantics, Constructions, and the FrameNet Lexical Database

Frame Semantics,
Constructions, and the
FrameNet Lexical Database
Collin F.Baker
International Computer Science Institute
Berkeley, California

Outline of Course
• Background
• What is a semantic frame?
• Representing FS Concepts
• The Lexicographic Process
• Full text annotation
• Automation
• Related projects

Background

Fillmore on Case Grammar
• 1968. The Case for Case
• 1969. Towards a Modern Theory of
Case
• 1977. The Case for Case Reopened
• S is underlyingly V + {N case1 , N case2 , …}
• Saliency hierarchy ⇒ foregrounding
• Case hierarchy ⇒ grammatical function

Fillmore on Frame
Semantics
• 1976 Frame semantics and the nature
of language
• 1977 Scenes-and-frames semantics
• 1983 Frame semantics
• 1985 Frames and the Semantics of
Understanding

Construction Grammar
• Only 1 type of entity: constructions
• Construction is pairing of form and meaning
(Saussure’s sign)
• Words, MWEs are lexical constructions
• Non-lexical constructions: Subject-predicate,
left isolation (“extraction”), modification
• mono-stratal, no deep vs. surface
• examples from What’s X doing Y? (Kay and
Fillmore 1999)

A Non-lexical Construction

A Lexical Construction

Embodied Conx Grammar
• http://www.icsi.berkeley.edu/NTL
• Outgrowth of Neural Theory of Language
group (neé L-zero)
• John Bryant, et al. (forthcoming) Cognitive
Linguistics-- linguistic side
• Jerome Feldman From Molecule to Metaphor
-- CogSci side
• How can a brain, made up of neurons and
connections between them, give rise to
thought and language?

The FrameNet Project
• Creating a highly detailed lexicon of
English predicators based on Frame
Semantics
• Documenting their valences by
manually annotating corpus examples
• Human- and machine-readable output
• Full data at
http://framenet.icsi.berkeley.edu

What is a semantic frame?
What do you annotate?

Semantic Frames
• Frame: Semantic frames are schematic
representations of situations involving various
participants, props, and other conceptual
roles, each of which is called a frame
element (FE)
• These include events, states, and relations
• What were called in earlier work on Frame
Semantics “scenes” and “scenarios” are all
represented in FrameNet by one data type,
the frame.
• Frames are connected to each other via
frame-to-frame relations

Frame Elements (FEs)
• Frame Element (FE): The participants, props
and roles of a frame. These can include
agents, inanimate objects, and elements of
the setting.
• The syntactic dependents (broadly construed)
of a predicating word correspond to the frame
elements of the frame (or frames) associated
with that word.
• Each FE is defined relative to a single frame.
Any connections between FEs have to be
made explicitly.

Lexical Unit (LU)
• The pairing of a lemma with a meaning; a
word sense. The meaning is partially
expressed by the relation between the lemma
and a FN frame, i.e. between lexical form(s)
and the semantic frame they evoke.
• Includes inflected forms see, saw seen
• Includes multi-word expressions (MWEs):
pick out, lose sleep, spitting image, family
practitioner
• May be any part of speech: verbs, nouns,
adjectives, prepositions, etc.

LU Definitions
• In addition to the connection to the
frame, the FN database also includes a
definition of each LU.
• The LU definition is human-readable
(we hope), similar to a dictionary
definition, and represents aspects of
meaning finer than the frame
distinctions.

Placing Frame: Definition
• Generally without overall (translational)
motion, an Agent places a Theme at a
location, the Goal, which is profiled. In this
frame, the Theme is under the control of the
Agent/Cause at the time of its arrival at the
Goal.
• This frame differs from Filling in that it
focuses on the Theme rather than the effect
on the Goal entity. It differs from Removing in
focusing on the Goal rather than the Source
of motion for the Theme.

Placing Frame: FEs
• Agent/Cause
• Goal
• Theme
• Area
• Cotheme
• Degree
• Distance
• Manner
• Means
• Place
• Purpose
• Time...

Placing Frame: LUs
• archive.v, arrange.v, bag.v, bestow.v, billet.v,
bin.v, bottle.v, box.v, brush.v, cage.v, cram.v,
crate.v, dab.v, daub.v, deposit.v, drape.v,
drizzle.v, dust.v, embed.v, file.v, garage.v,
hang.v, heap.v, immerse.v, implant.v, inject.v,
insert.v, ... put.v, rest.v, rub.v, set.v,
sheathe.v, shelve.v, shoulder.v, shower.v,
sit.v, situate.v, smear.v, sow.v, stable.v,
stand.v, stash.v, station.v, stick.v, stow.v,
stuff.v, tuck.v, warehouse.v
• Many of these incorporate the Goal FE

Placing Frame: Annotation
• [European heads of government ...
AGENT ] showered [telegrams of
congratulation THEME ] [on Clinton GOAL ],
saying...
• [I AGENT ] plunged [my hands THEME ]
[wrist deep DISTANCE ] [in the fragrant
herbs GOAL ]...

The Revenge Frame

Revenge Frame: Definition
• This frame concerns the infliction of punishment in
return for a wrong suffered. An Avenger performs a
Punishment on a Offender as a consequence of an
earlier action by the Offender, the Injury.
• The Avenger inflicting the Punishment need not be
the same as the Injured_Party who suffered the
Injury, but the Avenger does have to share the
judgment that the Offender's action was wrong.
• The judgment that the Offender had inflicted an Injury
is made without regard to the law.

Revenge Frame: FEs
• Core:
– Avenger
– Injured_party
– Injury
– Offender
– Punishment
• Non-Core:
– Place
– Time
– Degree
– Manner
– Purpose, ...

Core FEs
• Inherent in the concept of the frame
itself
• The most frame-specific FEs; core FEs
differences serve to differentiate frames
• Must be annotated for every instance of
the frame

Peripheral FEs
• Ontologically necessary, but not really
frame-specific
• Often presupposed, deprofiled
• Bindings to high-level frames
– Event ->Place, Time
– Intentionally_act -> Reason

Extra-thematic FEs
• Not actually part of the frame in
question;
• Beneficiary, description, etc.
• Really amount to evocation of a
separate frame
• But appear frequently in sentences,
should be annotated at the same time

Revenge Frame: LUs
• avenge.v, get_back.v, get_even.v,
payback.n, retaliate.v, retaliation.n,
retribution.n, retributive.a, retributory.a,
revenge.n, revenge.v, revengeful.a,
sanction.n, vengeance.n, vengeful.a,
vindictive.a
• AND avenger.n, revenger.n

Revenge Frame: Annotation
• 1. [They AVENGER ] took revenge [for the
deaths of two loyalist prisoners INJURY ]
• 2. The next day, [the Roman forces AVENGER ]
took revenge [on their enemies OFFENDER ]...]
• 3. [The ban PUNISHMENT ] [is Cop ] [Prince
Charles's AVENGER ] revenge [for her refusal to
spend Christmas with the rest of the royals...
INJURY ]

Text Frame: Definition
• A Text is an entity that contains
linguistic, symbolic information on a
Topic, created by an Author at the
Time_of_creation. It may be a physical
entity that is made of a certain Material
etc. It may be constructed for an
Honoree.

Text frame: FEs
• Core
– Text
• Non-core
– Author
– Components
– Containing_text
– Genre
– Honoree
– Material
– Medium
– Time_of_creation
– Topic
– Use

Text Frame: LUs
• account.n, article.n, autobiography.n, ballad.n,
benediction.n, biography.n, book.n, booklet.n,
bulletin.n, chronicle.n, comedy.n, diary.n,
drama.n, edition.n, editorial.n, elegy.n, epic.n,
epigram.n, epilogue.n, epistle.n, essay.n,
eulogy.n, exemplum.n, fable.n, fanzine.n,
festschrift.n, fiction.n, ... sermon.n, song.n,
sonnet.n, speech.n, spellbook.n, tetralogy.n,
thriller.n, tome.n, tract.n, tractate.n, tragedy.n,
treatise.n, trilogy.n, volume.n, whodunit.n,
writings.n

Text Frame: Annotation
• [Shusaku Endo's AUTHOR ] novel [of ideas TOPIC ]
can be read as symbolism or old-fashioned
realism...
• [Frodo's AUTHOR ] elegy [for Gandalf HONOREE ]
ends on the word “died”...
• Has he seen the excellent [Glasgow Evening
Times CONTAINING TEXT ] article [of 20 November
TIME OF CREATION ] which...
• [Its AUTHOR ] bulletin, [Christian Meditation
Newsletter TITLE ], containing ...

Apply heat Frame: Definition
• A Cook applies heat to Food, where the
Temperature_setting of the heat and
Duration of application may be specified. A
Heating_instrument, generally indicated by
a locative phrase, may also be expressed.
Some cooking methods involve the use of a
Medium (e.g. milk or water) by which heat is
transferred to the Food. A less semantically
prominent Food or Cook is marked
Co_participant.

Apply heat frame: FEs
• Core
– Container
– Cook
– Food
– Heating instrument
– Temperature setting
• Non-core
– Co-participant
– Degree
– Duration
– Manner
– Means
– Medium
– Place
– Purpose
– Time

Apply heat frame: LUs
• bake.v, barbecue.v, blanch.v, boil.v,
braise.v, broil.v, brown.v, char.v,
coddle.v, cook.v, deep fry.v, fry.v, grill.v,
microwave.v, parboil.v, poach.v, roast.v,
saute.v, scald.v, sear.v, simmer.v,
singe.v, steam.v, steep.v, stew.v,
toast.v

Apply heat frame: Annotation
• 1. Bake [the souffles FOOD ] [for 12
minutes DURATION ]. [CNI COOK ] [INI HEATING
INSTRUMENT ]
• [They COOK ] boil [them FOOD ] [in an iron
saucepan CONTAINER ].

Null Instantiation
• CNI (“constructional null instantiation”)
grammatically licensed
– e.g.,missing subject of imperative
Please leave the room.
• INI (“indefinite null instantiation”)
existential; lexically licensed
– e.g., missing objects of some activity verbs
I’ve been baking all afternoon.
• DNI (“definite null instantiation”)
anaphoric; lexically licensed
– e.g., omitted complements of some cognitive
verbs She already knows.

The art of frame definition
• Not a science, but not haphazard, either
• Try to make useful generalizations
across reasonable-sized groups
• FN proceeds frame by frame rather than
lemma by lemma
• All LUs in a frame have the same FEs,
with the same profiling / coreness
• Respect stative / inchoative / causative
distinction

The Lexicographic Process
• Vanguarding
• Subcorporation
• Annotation
• Reports and data distribution

“Vanguarding”
• Frames, FEs, and LUs defined–corpus, other
resources
• Rules written to extract examples of all
syntactic patterns, some collocations
• Rules can include commands to pre-mark
FEs on chunks
• “Other” subcorpus for unforeseen patterns

Subcorporation
• Non-interactive, command-line process
• Down-sample when necessary for very
common lemmas
• Subcorpora produced by matching
chunked sentences against rules
– Slow
– Parse-specific
– Complex, cascaded filter

Annotation
• Annotator chooses “good_ examples of
each lexicographically relevant
syntactic pattern (“alternations”)
• All core FEs found are annotated for
each sentence
• If the predicator is a verb, core FEs not
expressed in the sentence are
annotated as “null instantiated_

Annotation
• Each annotation set contains labels on
multiple layers: FE, GF, PT, etc.
• GF and PT added semi-automatically
• Process allows multiple targets
(annotation sets) per sentence (not
needed for lexicographic work)
• Goal is ~15-20 clear examples / LU

Retaliate: Annotation report

Retaliate: Lexical entry report

Representing Frame
Semantic Concepts

Frame-to-frame relations
1. Inheritance
2. Perspective on
3. Using
4. Subframe
5. Precedes
6. Causative of
7. Inchoative of
8. See also

FE-to-FE relations across
frames
• Every frame relation (except See also)
is accompanied by one or more FE-to-
FE relations.
• At the moment, there is only one type of
FE-FE relation, which is “subtype of”
(possibly improper).
• This may change.

FrameGrapher

Inheritance
• All FEs of parent are bound to FEs of
child
• Child FEs need not have same name
• Child can have more FEs
• Child semantics is a subtype of parent
semantics
• Any within-frame FE-FE relations of
parent are duplicated in child

Perspective on
• Some parent FEs missing or deprofiled
in child
• Commercial Transaction -- Buy, Sell
• Employment -- Hiring, Getting a job

Using

Subframe and Precedes

Causative of and Inchoative of
Causative of
Inchoative of
1. …others BOUND the
forelegs, and the hind legs,
together with rope.
2. Recombinant Oct-11 protein
BINDS specifically to an
octamer sequence…
3. …the BOUND protein can
detach from the protected
DNA fragment and
reassociate to other DNA
fragments…

See also
• This is not a profoundly ontological
relationship, it’s more like “see also” in
a print dictionary, alerting the reader to
another frame that seems similar
• Such links allow us to describe
differences between a set of similar
frames in just one place.

What happened to thematic
roles?
• They correspond fairly well to the FEs of
top-level frames:
– Event:
– Act
– Intentionally act:
• Some also correspond fairly well to
semantic types on FEs

Traditional thematic roles
don't fit some frames
• Similarity-- NB two ways of expressing
this:
– reciprocally (The children are very similar
(to each other)) or
– unequally (John resembles his father.
– NB: ??His father resembles john.
• Not an event but a state; event-type
roles don’t apply

Traditional roles don’t fit (2)
• Causative replacing
– The coach replaced [Smith OLD] with
[Jones NEW]
• Inchoative replacing
– [France NEW] replaced [Brazil OLD] as
world champions.

FE-to-FE relations within
frames
• Excludes: 1 FE excludes another
• Requires: 1 FE requires the presence of
another.
• [The children PARTICIPANTS] are very
similar.
• [John FIGURE] is similar to [his father
GROUND].

FE-to-FE relations within
frames (2): Coresets
• Any one of a set of FEs is sufficient for
a pragmatically "complete” sentence
• She ran [here GOAL] [from the bus-stop
SOURCE]
• She ran [for one kilometer DISTANCE]
• She ran [along the canal PATH]
• ?She ran. (OK in answer to the question
How did she get here?...)

*LU-to-LU relations
• Implied relation of inter-substitutability
for all LUs within a frame
• But more true of some frames than
others
• Positive and negative words in same
frame, marked with semantic types
• Use WordNet for most such relations

FN Semantic Types
• Ontological types--
– relate to ontologies, WN hierarchy but
data-driven
– mostly on FEs
• Lexical types -- on LUs (pos/ neg)
• Framal types-- theory-internal e.g.
“non-lexical frame”

Framal Semantic Types

Full text annotation

Deeper text understanding
• Although the main work of FN has been
lexicographic, the ultimate goal has
always been deep understanding of full
texts (Fillmore and Baker 2001)
• One annotation set for each frameevoking
expression

In October 2002,
the U.S. State Department
informed
North Korea
that
the U.S.
was aware
of this program,
and
regards
it
as a violation
of Pyongyang's
nonproliferation
commitments.
Telling.inform

Telling.inform
Time
Speaker
Target
Addressee
Message
In 2002,
the U.S. State Department
INFORMED
North Korea
that the U.S. was aware of this program ,
and regards it as a violation of Pyongyang's
nonproliferation commitments

Inform
• The meaning of inform that we wish to describe
belongs to a Telling frame; here the emphasis is on
getting information to an addressee, and is thus
distinct from Saying.
– The Telling frame is shared by inform, tell, notify, etc.,
Saying is shared by say, announce, state, whisper, etc.
• The meaning of inform in the Telling frame is distinct
from the sense it has as a member of the Reporting
frame, where it occurs as part of a phrasal verb,
inform on. Other members of this frame are report
(they reported me to the authorities), tell on, rat on,
fink on.

Annotations with
Telling.inform

Other patterns with
Telling.inform?
• Those examples showed just one of the syntactic
patterns available to inform in the Telling frame:
simple active sentences with a that-clause
expressing the Message. If the Message is
expressed with a NP, the preposition of can be
selected.
– Passive with that: Were [you] informed [that
Shelly has left home]?
– Active with of: [I] already informed [you] [of my
decision].
– Passive with of: Had [you] been informed [of the
details]?
• Other possibilities include on, about, and as to.

In October 2002,
the U.S. State Department
informed
North Korea
that
the U.S.
was aware
of this program,
and
regards
it
as a violation
of Pyongyang's
nonproliferation
commitments.
Awareness.aware

Awareness.aware
Cognizer
TARGET
Content
the U.S.
was AWARE
of this program

Aware
• The adjective aware is assigned to the Awareness
frame, which it shares with cognizant, conscious and
a number of verbs (know, realize, ...) and nouns
(awareness) that signal a relation between a
Cognizer and a Content. As with Telling.inform, the
Content (corresponding to Message) can be
expressed as a clause or as a NP:
– Are [you] aware [of our crisis]?
– Are [you] aware [that we are having a crisis]?

In October 2002,
the U.S. State Department
informed
North Korea
that
the U.S.
was aware
of this program,
and
regards
it
as a violation
of Pyongyang's
nonproliferation
commitments.
Categorization.regard

Categorization.regard
Cognizer
TARGET
Item
Category
the U.S.
REGARDS
it
as a violation of Pyongyang's
nonproliferation commitments

Regard
• Regard, in the Categorization frame, is
used to express the idea of assigning
some Item to a Category. Its frame
partners include consider, classify,
categorize and a few others.

In October 2002,
the U.S. State Department
informed
North Korea
that
the U.S.
was aware
of this program,
and
regards
it
as a violation
of Pyongyang's
nonproliferation
commitments.
Compliance.violation

Compliance.violation
Act
Target
Norm
it
a VIOLATION
of Pyongyang's nonproliferation
commitments

Violation
• Violation, in the sense we have in mind, belongs to
the negative-response set of lexical units in the
Compliance frame.
• Alongside of X violated the rule we have X is a
violation of the rule and X is in violation of the rule.
• The X in these formulas can stand for
– the Protagonist (we),
– the Act (what we did), or
– the State_of_affairs (our situation).

In October 2002,
the U.S. State Department
informed
North Korea
that
the U.S.
was aware
of this program,
and
regards
it
as a violation
of Pyongyang's
nonproliferation
commitments.
Commitment.commitment

Commitment.commitment
Speaker
Message
Target
Pyongyang's
nonproliferation
COMMITMENTS.

Commitment
• The noun commitment in the intended sense belongs
to the Commitment frame.
• Other words sharing this frame include commit, vow,
oath, swear, promise, covenant (some are verbs,
some are nouns, some are both).
• All of them have to do with illocutionary acts that bind
their speakers to a course of action.
• This sense of commitment takes the support verb
make.
– Are you afraid to make a commitment?
– You’ve made a commitment now, so you’d better honor it.

Support Verbs and Polysemy
Commitment also occurs in the
Institutionalization frame: committing a person to a
mental hospital. That meaning does not welcome
the support verb make.
The verb commit itself is a support verb for
crimes and sins: to commit murder is the same as
to murder. But this use of commit has commission,
not commitment, as its nominalization!

Full Text Annotation
• Text annotation is relatively recent task
for FN
– 5 texts from PropBank, for comparison with
PB annotation (NSF subcontract)
– 15 texts from NTI website-- CNS country
profiles, etc. (AQUINAS)
• Must deal with syntactic complexity, etc.

Lexicography vs. Full Text
• Lexicography: Need to fill out frames, fully
exemplify LUs, create training data for
machine learning
• Text annotation: Need to create new frames,
LUs quickly, maximally reuse, fewer
examples of rare LUs, more of common ones
• Created new text-annotation form to speed
up finding same LU across text(s)

PropBank
• Closest annotation project to FrameNet in
many ways
• Starts from Penn TreeBank, mainly WSJ
• Annotates arguments and adjuncts of verbs,
with labels like Arg0, Arg1, ArgM
• These general labels are given definitions
specific to each word sense
• Some generalization across LUs, but no true
semantic frames
• Now supplemented with NomBank for nouns

PropBank - WordNet -
FrameNet Comparison
• Felipe Gonzalez election victory
– (live)
– Capture 1

NIs and anaphora resolution
• The United Nations said Somali
gunmen who had hijacked a U.N.-
chartered vessel carrying food aid
to tsunami victims released the ship
after holding it for more than two
months.
• Words in orange are in FN

FN is not the only resource
you need for NLP…
• Need treatment of negation or quantification
• Need mechanism for syntactic/ semantic
composition
• Need discourse connectives
• Need specialist terms, noun taxonomies
(NER)
• Need recognizers for time expressions,
numbers, etc.

Automation
or
Can’t this be done by a machine?

Some parts just can’t be…
• We are not basing our work on existing
sense inventories; we want to be really
accurate about the number and nature
of the participants in each frame.
• Unsupervised learning can only take
you so far; we believe human judgment
has to be part of the mix.

Frame assignment as WSD
• Title of paper by K. Erk (2005)
• Same basic problems as for machinelearning
WSD-- insufficient data, interannotator
agreement
• Same methods, variety of classification
algorithms, well-researched

Automatic semantic role
labeling (ASRL)
• Gildea & Jurafsky (2002)
• Thompson, Levy and Manning (2003) --
generative HMM model
• Fleishman and Hovy (2003) “Maximum
Entropy Approach to FrameNet Tagging
• Senseval 3 ASRL task (Litkowsky 2004)

Shalmaneser
• Erk & Padó 2006 LREC (“Shallow
Semantic Parsing⇠”)
• FRED-frame assignment = supervised
WSD, Naïve Bayes classifier, MLE
• ROSY-role labeling, 1 or 2-step
• Trained on English, German
• Out of the box or experimental use,
substituting components

Frame Induction
• Rebecca Green’s Ph.D. thesis, U
Maryland, Green & Dorr 2004, 2005
• Used WordNet, LDOCE to find clusters
of Vs, then clusters of associated Ns
• Induced prospective frames and names
for frames and FEs in them
• Roughly 1500 frames, extensive lists of
words in frames, but need more work
• Tested on text tiling task--small but
significant improvement

"Rapid Vanguarding"
• Using principles of Kilgarriff's Word
Sketch Engine/ WASPbench
• WSE provides filtered summary of
corpus data www.sketchengine.co.uk
• WASPbench allows grouping into
word senses (running at ITRI,
Brighton)

Sample Word Sketch

Bootstrapping annotation
• We are close to situation in which
output of Shalmaneser can be input to
FN annotation, output of annotation can
be training data for Shalmaneser
• Need to do error analysis, selective
sampling to create best training data
• Plan to create large corpus with FN
annotation of differing quality-- platinum,
gold, silver, …

Applications

Information Extraction
• Mohit and Narayanan 2003 HLT-
NAACL “Semantic Extraction with Wide-
Coverage Lexical Resources”
• IE using GATE on 100 crime stories
• Used FN frames as “seeds” for IE
patterns
• Walked WN hierarchy to increase
coverage

Question Answering
• Narayanan and Harabagiu 2004 Coling
• Analyse predicate argument structs and
semantic frames in qns and answers
• Coordinated Probablistic Relational
Model of events
• Probabalistic inference from extracted
events

Textual Entailment
• Aljoscha Burchardt and Anette
Frank 2006 (conference?)
“Approximating Textual Entailment with
LFG and FrameNet Frames”
• LFG f-struct -> FN roles
• PASCAL RTE challenge
• Computing match graphs for text and
hypothesis

Modeling Sentence
Processing
• Ulrike Padó 2006 (conference?)
• Semantic/syntactic “garden path”
– The hunter shot by the teenager was quite
young.
– The deer shot by the hunter was truly
impressive.
• FN/PB comparison
– PB better coverage
– FN better modeling

Related projects

The SALSA Project
• Manfred Pinkal, Universität des
Saarlandes
• Starts from TIGER German treebank
• Adds FrameNet labels to constituents
• Own annotation tool, TIGER XML
• Where FN frames do not yet exist,
creates “unknowns”, annotates
somewhat like PropBank

FrameNets in other
Languages
• Spanish FrameNet: Carlos Subirats, U
Autónoma, Barcelona
http://gemini.uab.es/SFN
• Japanese FrameNet: Kyoko Ohara,
Keio U (and others from Tokyo Univ)
http://jfn.st.hc.keio.ac.jp
• German FrameNet: Hans C. Boas, U
Texas, Austin
http://gframenet.gmc.utexas.edu/

Spanish FrameNet
• Universidad Autónoma de Barcelona
• Built corpus of 350 million words of Old and
New World Spanish
• Own system for extracting sentences,
“subcorporation”
• Using a copy of the FN software for
annotation
• Using FN frames, with some modifications for
Spanish

Japanese FrameNet
• Own Corpus (20 million words), Search
Tool
• JFN Desktop
• JFN Report System (Dynamic)
• HTML Report Creator (Static)
• Currently annotating LUs in frames
related to Motion, Risk, Commerce,
Theft, etc.

German FrameNet
• German corpora for GFN:
– LDC German newspaper corpus
– Institut für Deutsche Sprache: Datenbank
gesprochenes Deutsch
– Tagging with STTS
• Working on lexicon, pipeline for texts

FrameNets by Projection
• English ⇒ French: Guillaume Pitel,
CNRS (Nancy)
• English ⇒ Chinese: BiFrameNet:
Pascale Fung (HKUST)
• Related work: Padó and Lapata (2005)
English ⇒ German, French

FrameSQL
• Prof. Hiroaki Sato, Senshu University,
Kawasaki, Japan http://sato.fm.senshuu.ac.jp/fn23/notes/
• FEs extracted from FN data, loaded in
to SQL database, form-based searching
over the web
• In areas where SFN has the same
frames as English, can do parallel
searches

Soccer FrameNet
• Thomas Schmidt, Ph.D. U Hamburg--
ICSI post-Doc
• http://www.kicktionary.de

FN in OWL DL
• Jan Scheffczyk (Bundeswehr U, ICSI
Post-doc) and Srini Narayanan (ICSI)
(OntoLex 2006)
• Frame db in OWL DL, reasoning with
standard tools
• Frames, FEs = classes; annotations =
instances
• Program for converting Annotation db
into OWL DL, too big to distribute all

Linking FN to SUMO
• Jan Scheffczyk and Michael Ellsworth
(ICSI) (OntoLex 2006)
• Links expressed in SUMO KIF
• Manually specified links from FN
semantic types to SUMO nodes
• Semi-automatically link roughly 1/2 of
FEs to SUMO nodes
• SUMO already linked to WN

From FN STs to SUMO nodes

Data releases
• Fifth data release “soon”-- R 1.3
• HTML for browsing and XML for
machines
• OWL DL and program for generating
• Java API?
• The Book
• Hundreds of users around the world

Thanks!
• Talk to us
• http://framenet.icsi.berkeley.edu