FrameNet, PropBank, VerbNet

Rich Pell

FrameNet, PropBank, VerbNet When syntactic information is not enough Lexical databases Annotate a natural language corpus with

semantic information Largely manual classification efforts

Outline FrameNet

Frame Semantics Overview and Demo Applications

PropBank Overview and Demo Applications

VerbNet Levin Classes Frame Demo

Conclusion

FrameNet

Frame Semantics Sell (v) – to exchange an item for money or its

equivalent Vce (n) – estimator of reactivity delta due to

voids in moderator Definition of a word is useless without

knowledge relating to that word: Entities involved - buyer, seller, item, money Relationships between those entities:

Buyer gives money to seller Seller gives item to buyer Buyer believes value of item >= monetary amount Seller believes value of item <= monetary amount

Semantic Frame Description of an event, relation, or entity and

its participants Captures the ‘essential knowledge’ of a given

word sense Developed by Charles Fillmore

FrameNet Overview Attempt to represent frame semantics in a

human and machine-readable database Developed by Charles Fillmore at Berkeley’s

International Computer Science Institute Founded in 1997 Funded by National Science Foundation and

DARPA Freely available via web interface or download https://framenet.icsi.berkeley.edu/fndrupal/

FrameNet Overview Set of semantic frames Composed of frame elements (FEs) – roles

within the frame Words that evoke this frame are called lexical

units(LUs) – represent a sense of a given word Frame: Commerce_sell FEs: buyer, seller, item, money, place,

reason… LUs: auction.v, retail.v, vend.v…

Frames Definition Core/non-core frame elements

Definition and examples Frame-frame relations Lexical Units

Frame-Frame Relations Inheritance – IS-A relation

Child frame is subtype of parent frame Each frame element in parent has corresponding frame element in

child Revenge inherits from Rewards_and_punishments

Using – child frame presupposes parent frame as background Speed presupposes Motion No one-to-one correspondence between FEs

Subframe – child frame is subevent of complex event represented by parent Criminal_process -> Arrest, Arraignment, Trial, Sentencing

Perspective-on – one frame provides some perspective on (perspectivizes) another frame Commerce_goods_transfer provides perspective on Commerce_sell

Text Annotation [Seller Bob] auctioned [Goods the clock]

[Buyer to John] [Item Colgate’s stock] rose [Difference $3.64]

[Final_value to $49.94] reduction [Item of debt levels][Value_2 to

$665 million][Value_1 from $2.6 billion] [Sleeper They][Copula were]asleep[Duration

for hours] He took a packet of Woodbines out of the

breast pocket of [Wearer his][Material cotton][Garment shirt] and lit one.

Development Characterize frames Find words that fit the frames (lexical units) Extract sample sentences

British National Corpus (editorials, sermons, textbooks, advertisements, novels, sermons)

Linguistic Data Consortium (US newswire texts) American National Corpus ~200 million words

Annotate selected examples

Progress 1000 linked semantic frames comprising:

10,000 lexical units 170,000 manually annotated sentences

Ports to other languages Spanish, German, Chinese, Japanese

Uses Semantic role assignment Natural language understanding Machine translation Part of speech tagging Textual entailment Information extraction NLP applications where a syntactic parse will

not suffice

PropBank

PropBank Adds a semantic layer to Penn Treebank Attempts to capture accurate predicate-argument

structure by annotating predicates and the semantic roles of their arguments

Annotates predicates (verbs) and their arguments: John broke the window -> broke(arg0 = John, arg1 = the

window) The window broke -> broke(arg1 = the window)

Developed in 2001 at the University of Pennsylvania Martha Palmer, Paul Kingsbury Free, open-source, downloadable http://verbs.colorado.edu/~mpalmer/projects/ace.html

PropBank Structure PropBank is a set of frame files Each frame file contains one or more

PropBank verb senses (aka frameset or roleset ID)

Each verb sense is annotated with: Semantic roles for each argument of a predicate Examples Links to other lexical tools (FrameNet, VerbNet)

PropBank Arguments Standardized as much as possible

Arg0 = agent Arg1 = patient Arg2 = instrument/attribute Arg3 = starting point/attribute Arg4 = ending point ArgM = modifier

Obama met him privately in the White House, on Thursday. Rel: met Arg0: Obama Arg1: him ArgM-MNR: privately ArgM-LOC: in the White House ArgM-TMP: on Thursday

PropBank Example…the campaign is drawing fire from anti-smoking advocates…Arg0: the campaignRel: drawingArg1: fireArg2-from: anti-smoking advocates

PropBank Example<predicate lemma="sell"><roleset id="sell.01" name="commerce: seller" vncls="13.1-1"> <roles> <role descr="Seller" n="0"> <vnrole vncls="13.1-1" vntheta="Agent"/></role> <role descr="Thing Sold" n="1"> <vnrole vncls="13.1-1" vntheta="Theme"/></role> <role descr="Buyer" n="2"> <vnrole vncls="13.1-1" vntheta="Recipient"/></role> <role descr="Price Paid" n="3"/> <role descr="Benefactive" n="4"/> </roles>

<example name="intransitive"> <text> They-1 have *trace*-1 to sell when things look like they're falling. </text> <arg n="0">*trace*</arg> <rel>sell</rel> <arg f="TMP" n="M">when things look like they're falling</arg></example>

<example name="Ergative"> <text> A painting by August Strindberg sold at auction in Stockholm. </text> <arg n="1">A painting by August Strindberg</arg> <rel>sold</rel> <arg f="LOC" n="M">at auction</arg> <arg f="LOC" n="M">in Stockholm</arg></example>

Differences From FrameNet Verb-specific Each verb is its own predicate Closer to syntactic parse More thorough but simpler annotation of

corpus

PropBank Progress 3500 verbs annotated Work on translating to Dutch, Arabic Semantic role labeling Knowledge discovery Semantic parsing

VerbNet

VerbNet Lexicon of English verbs Groups verbs based upon shared syntactic

behavior 5800 verbs in 270 verb classes

Based on Levin classes and their extensions Developed by Karin Kipper-Schuler at

University of Pennsylvania via NSF and DARPA grants

Free, open source, downloadable http://verbs.colorado.edu/~mpalmer/

projects/verbnet.html

Levin Classes English Verb Classes and their Annotations,

Beth Levin, 1993 Syntactic behavior of a verb is based upon its

meaning Possible to syntactically group verbs into

classes based upon how they interact with specific objects/prepositions/subjects and expect them to have some semantic similarity

e.g. Locative alternation – involves moving something into or onto a location Verbs of placement and covering Scatter, pump, hang, drizzle, cram, load

VerbNet Roles Groups verbs based upon Levin classes Add semantic role labels to Levin classes, e.g.

Agent – actor in an event who carries out the event Theme – undergoer that is central to event or state that

does not have control over the way the event occurs Destination – goal that is a concrete, physical location …

23 total Illustrate the “who what how when where”

information contained in a sentence Analogous to FrameNet’s frame elements or

PropBank’s numbered arguments

VerbNet Classes Set of member verbs Thematic roles used in predicate-argument

structure of verbs in the class Selectional restrictions on the roles

“Sam drank a coffee.” “Sam drank a car.”

Set of frames: Brief description Example Syntactic description Set of semantic predicates, includes temporal

function indicating when a predicate is true

VerbNet applications Verbs typically convey the main idea of a

sentence Maps the syntactic nature of PropBank

predicate/argument parses into a richer semantic context

Machine translation Document classification Word sense disambiguation Semantic role labeling 3D animation (parameterized action

representations) Planning Automatic verb acquisition

Automatically Extending VerbNet Semantic information for several verbs at a

time captured in VerbNet classes Can automatically add new candidate verbs to

a class by testing against pre-defined class specifications

Removes need for exhaustive manual encodings

Automatically Extending VerbNet Apply k-means clustering to some other

resource: PropBank WordNet FrameNet

Observe the clusters to see if they correspond to any VerbNet class

If so, do they contain any verbs not in the existing VerbNet class?

Able to add 47 verbs

Summary FrameNet, PropBank, VerbNet all annotate an NL

corpus with semantic information: FrameNet – defines a set of semantic frames annotating

additional semantic information needed to capture meaning of a word

PropBank – annotates propositions and their arguments in a structured fashion

VerbNet – groups verbs into syntactically and semantically similar classes

All are used when a syntactic parse is not enough Highly linked:

Unified Verb Index - http://verbs.colorado.edu/verb-index/ SemLink - http://verbs.colorado.edu/semlink/

Questions

ReferencesVerbNet Guidelines, http://verbs.colorado.edu/verb-index/VerbNet_Guidelines.pdf

Palmer, M. 2009. Semlink: Linking PropBank, VerbNet and FrameNet. Proceedings of the Generative Lexicon Conference. Sept. 2009, Pisa, Italy: GenLex-09.

2012 Data Format Specifications for English PropBank, http://verbs.colorado.edu/~mpalmer/projects/ace/EPB-data-format.txt.

M. Palmer et al, “English PropBank Annotation Guidelines,” 2012, http://verbs.colorado.edu/~mpalmer/projects/ace/EPB-annotation-guidelines.pdf

Karin Kipper, Anna Korhonen, Neville Ryant, and Martha Palmer. Extending VerbNet with Novel Verb Classes. Fifth International Conference on Language Resources and Evaluation (LREC 2006). Genoa, Italy. June, 2006.

Karin Kipper, Anna Korhonen, Neville Ryant, and Martha Palmer. Extensive Classifications of English verbs. Proceedings of the 12th EURALEX International Congress. Turin, Italy. September, 2006.

Paul Kingsbury and Karin Kipper. Deriving Verb-Meaning Clusters from Syntactic Structure.. Workshop on Text Meaning, held in conjunction with HLT/NAACL 2003. Edmonton, Canada, May 2003.

Karin Kipper-Schuler, VerbNet: a Broad-Coverage, Comprehensive Verb Lexicon,” Dissertation, University of Pennsylvania, 2005.

Michael Ellsworth et al, “FrameNet II: Extended Theory and Practice,” 2010, https://framenet2.icsi.berkeley.edu/docs/r1.5/book.pdf.