1. FrameNet Meets the Semantic Web Srini Narayanan Charles Fillmore Collin Baker Miriam Petruck

2. Talk Outline FrameNet A DAML + OIL Representation of FrameNet An Example: Encoding the Criminal Process Frame Applications of FrameNet. Summary and Future Work

3. Outline of Presentation Semantic Frames and the FrameNet Project Status of FrameNet Data and Software Details on the FrameNet process Comparison to other ontologies/resources Afternoon session: Going through the annotation process demo.

4. The FrameNet Project Phase I (NSF, 1997-2000) –ICSI, U-Colorado –Conceptual basis, used existing tools, and perl Phase II (NSF, 2000-2003) –ICSI, U-Colorado, SRI, SDSU –Scaling up, uses SQL database and Java-based in house tools. Pilot applications developed.

5. The FrameNet Project C Fillmore PI (ICSI) Co-PI’s: S Narayanan (ICSI, SRI) D Jurafsky (U Colorado) J M Gawron (San Diego State U) Staff: C Baker Project Manager B Cronin Programmer C Wooters Database Designer

6. Applications An important goal of our work is to present information about the words in a form that will prove usable in various NLP applications: 1.Question Answering (Berkeley, Colorado) 2.Semantic Extraction (Berkeley, SRI, Colorado) 3.Machine Translation (San Diego State)

7. Frames and Understanding Hypothesis: People understand things by performing mental operations on what they already know. Such knowledge is describable in terms of information packets called frames.

8. FrameNet in the Larger Context The long-term goal is to reason about the world in a way that humans understand and agree with. Such a system requires a knowledge representation that includes the level of frames. FrameNet can provide such knowledge for a number of domains. FrameNet representations complement ontologies and lexicons.

9. The core work of FrameNet 1.characterize frames 2.find words that fit the frames 3.develop descriptive terminology 4.extract sample sentences 5.annotate selected examples 6.derive "valence" descriptions

10. Lexicon Building We study words, describe the frames or conceptual structures which underlie them, examine sentences that contain them (from a vast corpus of written English), and record the ways in which information from the associated frames are expressed in these sentences.

11. The Core Data The basic data on which FrameNet descriptions are based take the form of a collection of annotated sentences, each coded for the combinatorial properties of one word in it. The annotation is done manually, but several steps are computer- assisted.

12. The Process Sentences containing a given word are extracted from the corpus and made available for annotation. Student annotators select the phrases that identify particular semantic roles in the sentences, and tag them with the name of these roles. Automatic processes then provide grammatical information about the tagged phrases.

13. SAMPLE ANNOTATIONS

14. Types of Words / Frames oevents oartifacts, built objects onatural kinds, parts and aggregates oterrain features oinstitutions, belief systems, practices ospace, time, location, motion oetc.

15. Event Frames Event frames have temporal structure, and generally have constraints on what precedes them, what happens during them, and what state the world is in once the event has been completed.

16. Sample Event Frame: Commercial Transaction Initial state: Vendor has Goods, wants Money Customer wants Goods, has Money Transition: Vendor transmits Goods to Customer Customer transmits Money to Vendor Final state: Vendor has Money Customer has Goods

17. Sample Event Frame: Commercial Transaction Initial state: Vendor has Goods, wants Money Customer wants Goods, has Money Transition: Vendor transmits Goods to Customer Customer transmits Money to Vendor Final state: Vendor has Money Customer has Goods (It’s a bit more complicated than that.)

18. Partial Wordlist for Commercial Transactions Verbs: pay, spend, cost, buy, sell, charge Nouns: cost, price, payment Adjectives: expensive, cheap

19. Meaning and Syntax  The various verbs that evoke this frame introduce the elements of the frame in different ways.  The identities of the buyer, seller, goods and money  Information expressed in sentences containing these verbs occurs in different places in the sentence depending on the verb.

20. CustomerVendor GoodsMoney BUY from for She bought some carrots from the greengrocer for a dollar.

21. CustomerVendor GoodsMoney PAY for to She paid a dollar to the greengrocer for some carrots.

22. CustomerVendor GoodsMoney PAY for She paid the greengrocer a dollar for the carrots.

23. CustomerVendor GoodsMoney SPEND on She spent a dollar on the carrots.

24. CustomerVendor GoodsMoney SELL for to The greengrocer sold some carrots to her for a dollar.

25. CustomerVendor GoodsMoney SELL for The greengrocer sold her some carrots for a dollar.

26. CustomerVendor GoodsMoney CHARGE for The greengrocer charged a dollar for a bunch of carrots.

27. CustomerVendor GoodsMoney CHARGE for The greengrocer charged her a dollar for the carrots.

28. CustomerVendor GoodsMoney COST A bunch of carrots costs a dollar.

29. CustomerVendor GoodsMoney COST A bunch of carrots cost her a dollar.

30. CustomerVendor Goods to do X Money COST IT It costs a dollar to ride the bus.

31. CustomerVendor Goods to do X Money COST IT It cost me a dollar to ride the bus.

32. FrameNet Product For every target word, describe the frames or conceptual structures which underlie them, and annotate example sentences that cover the ways in which information from the associated frames are expressed in these sentences.

33. FN work: characterizing frames One of the things we do is characterize such information packets - beginning with informal descriptions. We can begin with Revenge.

34. The Revenge frame The Revenge frame involves a situation in which a)A has done something to harm B and b)B takes action to harm A in turn c)B's action is carried out independently of any legal or other institutional setting

35. FN work: finding words in frame We look for words in the language that bring to mind the individual frames. We say that the words evoke the frames.

36. Vocabulary for Revenge Nouns: revenge, vengeance, reprisal, retaliation Verbs: avenge, retaliate, revenge, get back (at), get even (with), pay back Adjectives: vengeful, vindictive

37. FN work: choosing FE names We develop a descriptive vocabulary for the components of each frame, called frame elements (FEs). We use FE names in labeling the constituents of sentences exhibiting the frame.

38. FEs for Revenge injury avengeravengerpunishment offenderoffender injury injured_party avengerFrame Definition: Because of some injury to something or someone important to an avenger, the avenger inflicts a punishment on the offender. The offender is the person responsible for the injury. The injured_party may or may not be the same individual as the avenger. avengeroffenderinjury injured_partypunishmentFE List: avenger, offender, injury, injured_party, punishment.

39. FN work: collecting examples We extract from our corpus examples of sentences showing the uses of each word in the frame.

41. Obviously we need to conduct a more regimented search, grouping examples with related structures.

43. Examples of simple use are swamped by the idiomatic phrase "with a vengeance".

44. FN work: annotating examples We select sentences exhibiting common collocations and showing all major syntactic contexts. Using the names assigned to FEs in the frame, we label the constituents of sentences that express these FEs.

46. FN work: summarizing results Automatic processes summarize the results, linking FEs with information about their grammatical realization. The output is presented in the form of various reports in the public website, in XML format in the data release.

49. I avenged my brother.

50. I avenged his death.

51. Querying the data: meaning to form Through various viewers built on the FN database we can, for example, ask how particular FEs get expressed in sentences evoking a given frame.

52. By what syntactic means is offender realized? Sometimes as direct object: we'll pay you back for that Sometimes with the preposition on they'll take vengeance on you Sometimes with against we'll retaliate against them Sometimes with with she got even with me Sometimes with at they got back at you

53. By what syntactic means is offender realized? Sometimes as direct object: we'll pay you back for that Sometimes with the preposition on they'll take vengeance on you Sometimes with against we'll retaliate against them Sometimes with with she got even with me Sometimes with at they got back at you It's these word-by-word specializations in FE-marking that make automatic FE recognition difficult.

54. Querying the data: form to meaning Or, going from the grammar to the meaning, we can choose particular grammatical contexts and ask which FEs get expressed in them.

55. What FE is expressed by the object of avenge? Sometimes it's the injured_party I've got to avenge my brother.Sometimes it's the injury My life goal is to avenge my brother's murder.

56. Evaluation Lexical coverage. We want to get all of the important words associated with each frame. Combinatorics. We want to get all of the syntactic patterns in which each word functions to express the frame.

57. Evaluation We do not ourselves collect frequency data. That will wait until methods of automatic tagging get perfected. In any case, the results will differ according to the type of corpus - financial news, children's literature, technical manuals, etc.

58. What do we end up with? Frames Lexical entries Annotations

59. Sample from frames list Creating, Crime_scenario, Criminal_investigation, Criminal_process, Cure. Custom, Damaging, Dead_or_alive, Death, Deciding, Deny_permission, Departing, Desirability, Desiring, Destroying, Detaining, Differentiation, Difficulty, Dimension, Direction, Dispersal, Documents, Domain, Duplication, Duration, Eclipse, Education_teaching,Emanating, Emitting, Emotion_active, Emotion_directed, Emotion_heat, Employing, Employment, Emptying, Encoding, Endangering, Entering_of_plea, Entity, Escaping, Evading. Evaluation, Evidence, Excreting, Execution,

60. Sample from lexical unit list * augmentation.N (Expansion) * augur.V (Omen) * August.N (Calendric_unit) * aunt.N (Kinship) * auntie.N (Kinship) * austere.A (Frugality) * austerity.N (Frugality) * author.V (Text_creation) * authoritarian.A (Strictness) * authorization.N (Documents) * autobahn.N (Roadways) * autobiography.N (Text) * automobile.N (Vehicle) * autumn.N (Calendric_unit) * avalanche.N (Quantity) * avenge.V (Revenge) * avenger.N (Revenge) * avenue.N (Roadways) * aver.V (Statement)

61. Added Value: frame relatedness We have ways of linking frames to each other, through relations of –inheritance –subframe –"using" We would like to explore how our frame relationships can be mapped onto ontological relations.

62. Frame-to-frame relations Revenge inherits Punishment/Reward Revenge uses the Hostile_encounter frame (see existing tentative frame hierarchy)

63. Added Value: semantic types We also have the means of adding semantic types to words, frames and frame elements. Some of these: – negative vs. positive (disaster vs. bonanza), –punctual vs. stative (arrive vs. reside), –artifact vs. natural kind (building vs. tree).

64. Added Value: semantic types For the kinds of nouns that occupy particular FE slots in given frames, we should be able to use the WordNet noun taxonomies. This is done in some related work

65. Added Value: support verbs In the case of the event nouns, we keep track of which verbs can combine with which nouns to signal occurrences of the frame evoked by the noun. –take a bath (bathe) –have an argument (argue) –wreak vengeance, –take revenge, –exact retribution.

66. Can annotation be automated? Gildea, D & D Jurafsky, 2000, Automatic labeling of semantic roles, Association for Computational Linguistics, Hong Kong. Mohit & Narayanan, 2003, Semantic Extraction using Wide-coverage lexical resources, HLT-NAACL 2003.

67. The Database The information collected from the data (and a certain amount of information inserted manually by the lexicographers) is stored in a MySQL database.

69. Current Status Current: 7700 Lexical Units –FN1: 1600 Lexical units –FN2: 4400 Lexical Units –Created (not yet annotated): 1280 LU –Other : in process, problems, etc.

70. Current Status 500 Frames 7700 Lexical Units 130,000 Annotated sentences

71. Data Distribution Distributed as XML files with accompanying DTDs Separate files and DTDs for –Frame and FE data ––Annotation data –Frame relation data Easy to parse with standard XML tools. –Approximately 100 research groups have been authorized to download release 1.0 of the FN data (Oct., 2002). Next release scheduled for August, 2003

72. FrameNet Software Distribution All software is pure Java, and can be run on any platform for which a JVM is available Has been successfully run on Solaris, Linux, Mac OS X, and Windows 9x/2000 with very minor modifications Server and clients currently being used in Barcelona for annotation in Spanish FN. We will streamline the installation process if demand warrants We plan to publicly release the full software suite in August, 2003.

73. Multi-Lingual FrameNets Spanish FrameNet –Prof. Carlos Subirats, U A Barcelona –Parallel to English FrameNet, using same frames German FrameNet –Prof. Manfred Pinkal, U Saarlandes –Complete annotation of existing parsed corpus, –using English frames where possible Japanese FrameNet –Prof. Kyoko Ohara, Keio U –Collecting own corpus, building search tools

74. Some Comparisons

75. Is FN an ontology? Not exactly, but some users use FN frames as an ontology of event types.

76. Is FN a thesaurus? Yes, because it groups words into meaning categories, by way of shared membership in frames.

77. How is FN different from WN? FN does not explicitly display semantic relations between words of the sort found in WordNet. (synonymy, antonymy, hyponymy, meronymy, etc.) Furthermore, FN includes many opposing pairs (hot, cold; tall, short) in the same frame.

78. Are FN annotations a treebank? FrameNet accumulates annotations, but FN annotations are mainly sentences in which only one word is analyzed thoroughly. Unlike existing treebanks, e.g., U Penn's PropBank, FN has a richer semantics.

79. Comparison with Dictionaries

80. American Heritage Dictionary avenge v. 1. To inflict a punishment or penalty in return for; revenge 2. To take vengeance on behalf of revenge v. 1. To inflict punishment in return for (injury or insult) 2. To seek or take vengeance for (oneself or another person); avenge

81. American Heritage Dictionary avenge v. 1. To inflict a punishment or penalty in return for; revenge 2. To take vengeance on behalf of revenge v. 1. To inflict punishment in return for (injury or insult) 2. To seek or take vengeance for (oneself or another person); avenge The FEs of the direct objects are expressed prepositionally; injury " in return for " marks the injury; " for " or " on behalf of " marks injured_party the injured_party.

82. American Heritage Dictionary avenge v. 1. To inflict a punishment or penalty in return for [ ]; revenge 2. To take vengeance on behalf of [ ] revenge v. 1. To inflict punishment in return for (injury or insult) 2. To seek or take vengeance for (oneself or another person); avenge revenge definer added qualifications on the missing argument, avenge definer didn't.

83. American Heritage Dictionary avenge v. 1. To inflict a punishment or penalty in return for; revenge 2. To take vengeance on behalf of revenge v. 1. To inflict punishment in return for (injury or insult) 2. To seek or take vengeance for (oneself or another person); avenge avenge definer claims avenge and revenge are synonym in sense 1; the revenge definer claims avenge and revenge are synonyms in sense 2.

84. American Heritage Dictionary avenge v. 1. To inflict a punishment or penalty in return for; revenge 2. To take vengeance on behalf of revenge v. 1. To inflict punishment in return for (injury or insult) 2. To seek or take vengeance for (oneself or another person); avenge revenge definer included "seek vengeance", not supported by FN examples.

85. American Heritage Dictionary avenge v. 1. To inflict a punishment or penalty in return for; revenge 2. To take vengeance on behalf of revenge v. 1. To inflict punishment in return for (injury or insult) 2. To seek or take vengeance for (oneself or another person); avenge Both definers include "take vengeance" in their definitions, as if that's more transparent than the simple verb.

86. Comparison with WordNet

87. We make fewer distinctions. 1. revenge, avenge, retaliate -- (take revenge for a perceived wrong; "He wants to avenge the murder of his brother") 2. retaliate, strike back -- (make a counterattack and return like for like, esp. evil for evil; "The Empire strikes back"; "The Giants struck back and won the opener"; "The Israeli army retaliated for the Hamas bombing")

88. We make fewer distinctions. 1. revenge, avenge, retaliate -- (take revenge for a perceived wrong; "He wants to avenge the murder of his brother") 2. retaliate, strike back -- (make a counterattack and return like for like, esp. evil for evil; "The Empire strikes back"; "The Giants struck back and won the opener"; "The Israeli army retaliated for the Hamas bombing") Hard to figure out what motivates distinguishing two senses; personal vs. institutional?

89. We make fewer distinctions. 1. revenge, avenge, retaliate -- ( take revenge for a perceived wrong ; " He wants to avenge the murder of his brother ") 2. retaliate, strike back -- ( make a counterattack and return like for like, esp. evil for evil ; " The Empire strikes back "; " The Giants struck back and won the opener "; " The Israeli army retaliated for the Hamas bombing ") Like FrameNet, these entries include Definitions and Examples. FrameNet limits its examples to attested sentences from a Corpus.

90. FN has more detailed syntax. revenge, avenge, retaliate -- (take revenge for a perceived wrong; "He wants to avenge the murder of his brother") *> Somebody ----s something retaliate, strike back -- (make a counterattack and return like for like, esp. evil for evil; "The Israeli army retaliated for the Hamas bombing") *> Somebody ----s *> Somebody ----s PP The WN sentence templates are impoverished structurally and do not indicate the semantic roles. In fact, retaliate is wrongly described as taking a simple object.

91. FN has more detailed syntax. revenge, avenge, retaliate -- (take revenge for a perceived wrong; "He wants to avenge the murder of his brother") *> Somebody ----s something retaliate, strike back -- (make a counterattack and return like for like, esp. evil for evil; "The Israeli army retaliated for the Hamas bombing") *> Somebody ----s *> Somebody ----s PP The identity of the P in PP is important: strike back at offender marks the offender, as does retaliate against; retaliate injury for marks the injury.

92. FN has more detailed syntax. revenge, avenge, retaliate -- (take revenge for a perceived wrong; "He wants to avenge the murder of his brother") *> Somebody ----s something retaliate, strike back -- (make a counterattack and return like for like, esp. evil for evil; "The Israeli army retaliated for the Hamas bombing") *> Somebody ----s *> Somebody ----s PP Where WordNet merely shows that the words in the second synset can occur intransitively, FN would say something about the anaphoric nature of the omitted offender.

93. Comparison with ontologies

94. Switching frames Revenge is a simple frame, but neither SUMO nor OpenCYC seem to have any conceptual link to it. A particular family of frames that we have concentrated on are those that make up the steps and institutions of Criminal_process.

95. Complex Frames With Criminal_process we have, for example, – sub-frame relations (one frame is a component of a larger more abstract frame) and –temporal relations (one process precedes another)

97. Inferencing These are the frames with which we are trying to set up inferencing rules for texts about crime reports. (Details in the presentation later.)

98. In SUMO SUMO (Adam Pease) deals with only the upper ontology, and moves toward our frame along this path, stopping at legal action. –entity – process – intentional process – social interaction – contest – legal action

99. In OpenCYC: ArrestingSomeone ArrestingSomeone: "A specialization of Social Occurrence and CapturingAnimal. In each instances of ArrestingSomeone a law enforcement officer arrests another person, who is then taken into custody. See the related constant #$HeldCaptive."

100. Trial comment : [[Def]] "The subcollection of #$LegalConflict events whose instances are heard and decided by a court and are officiated by a #$Judge." requiredActorSlots : [[Mon]] plaintiffs [[Mon]] defendants

101. Legal activities comment : [[Def]] "The collection of all events performed with the purpose of enforcing laws, that are performed by people officially charged with this this duty. Includes most activities of law enforcement officials (such as police) including detection of crime, identification of offenders, and arrests."

102. LawEnforcementOfficer comment : [[Def]] "An instance of PersonTypeByOccupation, and a specialization of PersonWithOccupation. Each instance of LawEnforcementOfficer is a person whose job is to detect, stop, and/or punish people engaged in illegal activities. The collection LawEnforcementOfficer includes members of local, state, and special police (e.g., transit police) forces, as well as federal agents (e.g., members of border patrols, national security agents). Consequently, a given instance of Law EnforcementOfficer typically also belongs to one of the following collections: #$StateEmployee, #$LocalGovernment Employee, or NationalGovernmentEmployee (see Public SectorEmployee)."

103. FrameNet for Applications Semantic Web (http://www.semanticweb.org)http://www.semanticweb.org –FN database in DAML+OIL (http://www.ai.sri.com/~narayana/frame-desc.daml)http://www.ai.sri.com/~narayana/frame-desc.daml Semantic Extraction using FrameNet Frame Simulation and Inference –Translation from frame structure to a simulation based inference tool (KarmaSIM) (COLING 2002)

104. Talk Outline FrameNet A DAML + OIL Representation of FrameNet An Example: Encoding the Criminal Process Frame Web Applications of FrameNet. Summary and Future Work

105. Semantic Web The World Wide Web (WWW) contains a large and expanding information base. HTML is accessible to humans but does not formally describe data in a machine interpretable form. XML remedies this by allowing for the use of tags to describe data (ex. disambiguating crawl) Ontologies are useful to describe objects and their inter-relationships. DAML+OIL (http://www.daml.org) is an markup language based on XML and RDF that is grounded in description logic and is designed to allow for ontology development, transfer, and use on the web.http://www.daml.org

106. FrameNet Entities and Relations Frames –Background –Lexical Frame Elements (Roles) Binding Constraints –Identify ISA(x:Frame, y:Frame) SubframeOf (x:Frame, y:Frame) Subframe Ordering –precedes Annotation

107. A DAML+OIL Frame Class The most general class

108. DAML+OIL Frame Element

109. FE Binding Relation See http://www.daml.org/services http://www.daml.org/services

110. Subframes and Ordering

111. Talk Outline FrameNet A DAML + OIL Representation of FrameNet An Example: Encoding the Criminal Process Frame Applications of FrameNet. Summary and Future Work

113. The Criminal Process Frame Frame ElementDescription CourtThe court where the process takes place DefendantThe charged individual JudgeThe presiding Judge ProsecutionFE indentifies the attorneys’ prosecuting the defendant DefenseAttorneys’ defending the defendant

114. The Criminal Process Frame in DAML+OIL

115. DAML+OIL Representation of the Criminal Process Frame Elements

116. FE Binding Constraints The idenfication contraints can be between Frames and Subframe FE’s. Between Subframe FE’s DAML does not support the dot notation for paths.

117. Criminal Process Subframes A subframe A subframe

118. Specifying Subframe Ordering

119. DAML+OIL CP Annotations "36352897" In July last year a German border guard apprehended two Irishmen with Kalashnikov assault rifles.

120. Current Status of DAML Encoding All FrameNet 1 data is available in DAML+OIL – annotations –frame descriptions. The translator has also been updated to handle the more complex semantic relations (both frame and frame element based) in FrameNet 2. We plan to release both the XML and the DAML+OIL versions of all FrameNet 2 releases.

121. Talk Outline FrameNet A DAML + OIL Representation of FrameNet An Example: Encoding the Criminal Process Frame Applications of FrameNet. Summary and Future Work

122. FrameNet for Applications Semantic Web (http://www.semanticweb.org)http://www.semanticweb.org –FN database in DAML+OIL (http://www.ai.sri.com/~narayana/frame-desc.daml)http://www.ai.sri.com/~narayana/frame-desc.daml Semantic Extraction using FrameNet Or can FrameNet be automated Frame Simulation and Inference –Translation from frame structure to a simulation based inference tool (KarmaSIM) (COLING 2002)

123. Semantic Extraction Behrang Mohit and Srini Narayanan –HLT-NAACL 2003.

124. Enhancing IE Techniques IE techniques currently use no inference (mostly!) –Robert Pickett was charged with felony possession of a handgun and sentenced to 5 years in a federal prison. Says Pickett was arrested Frame-based inferences can be useful for a variety of applications including individual/topic tracking, bridging inferences/co-reference resolution. FrameNet subframe structure and bindings can be exploited for this purpose.

125. A Simulation Semantics for Inference Frame Structure and bindings specify parameters for a simulation/enactment of the event Based on previous work (IJCAI 99, AAAI 99, CogSci 2000, COLING 2002, WWW 2002) –using an “X-schema” based representation, we simulate the temporal and inferential structure of the Frame- Element and Frame/Subframe relations from FrameNet. –Direct translation from both the mySQL FN database and the DAML+OIL representation

126. Reasoning about Events for NL applications (QA, NLU) Reasoning about dynamics –Complex event structure Multiple stages, interruptions, resources, framing –Evolving events Conditional events, presuppositions. –Nested temporal and aspectual references Past, future event references –Metaphoric references Use of motion domain to describe complex events. Reasoning with Uncertainty –Combining Evidence from Multiple, unreliable sources –Non-monotonic inference Retracting previous assertions Conditioning on partial evidence

128. Previous work Models of event structure that are able to deal with the temporal and aspectual structure of events Models frame-based and metaphoric inference about event structure. Based on an active semantics of events and a factorized graphical model of complex states. –Models event stages, embedding, multi-level perspectives and coordination. –Event model based on a Stochastic Petri Net representation with extensions allowing hierarchical decomposition. –State is represented as a Temporal Bayes Net (T(D)BN). –The Event-State representation requires branching time bayes nets with synchronization or Coordinated Bayes Nets (CBN)

129. Active representations Many inferences about actions derive from what we know about executing them Representation based on extending stochastic Petri nets captures dynamic, parameterized nature of actions Walking: bound to a specific walker with a direction or goal consumes resources (e.g., energy) may have termination condition (e.g., walker at goal ) ongoing, iterative action walker =Harry goal =home energy walker at goal

130. States Factorized Representation of State uses Dynamic Belief Nets (DBN’s) –Probabilistic Semantics –Structured Representation

131. States and Domain Knowledge Factorized Representation using Dynamic Belief Nets (DBN’s) –Probabilistic Semantics –Structured Representation

132. Active Event Representations Actions and events are coded in active representations called x-schemas which are extensions to Stochastic Petri nets. x-schemas are fine-grained and can be used for monitoring and control as well as for inference. Badler’s (U Penn) group uses same idea for commanding simulated robots (Jack). Nils Nilsson (SU) uses a similar idea for robot planning called Teleo-Reactive programs. Semantic basis for DAML-S, process descriptions of the Semantic Web

133. Compositional Primitives process atomic process composite process inputs (conditional) outputs preconditions (conditional) effects control constructs composedBy while sequence If-then-else fork...

134. Sequence: P1;P2 startfinish Done(P1;P2) Atomic Process P2 Done(P1) Atomic Process P1 Ready

135. Fork: P1|| P2 start finish Done(P1 || P2) Atomic Process P2 Ready(P1) Atomic Process P1 Ready(P2)

136. Concurrent-Sync Done(P2) Done(P1) startfinish Atomic Process P2 Ready(P1) Atomic Process P1 Ready(P2)

138. Implementation DAML-S translation to the modeling environment KarmaSIM [Narayanan, 97] (http://www.icsi.berkeley.edu/~snarayan) Basic Program: Input: DAML-S description of Frame relations Output: Network Description of Frames in KarmaSIM Procedure: Recursively construct a sub-network for each control construct. Bottom out at atomic frame. Construct a net for each atomic frame Return network

147. A Precise Notion of Contingency Relations Activation: Executing one schema causes the enabling, start or continued execution of another schema. Concurrent and sequential activation. Inhibition: Inhibitory links prevent execution of the inhibited x-schema by activating an inhibitory arc. The model distinguishes between concurrent and sequential inhibition, mutual inhibition and aperiodicity. Modification: The modifying x-schema results in control transition of the modified xschema. The execution of the modifying x-schema could result in the interruption, termination, resumption of the modified x-schema.

148. Results of Model Captures fine grained distinctions needed for interpretation –Frame-based Inferences (COLING02) –Aspectual Inferences (Cogsci98, CogSci01, IJCAI 99, CL03) –Metaphoric Inferences (AAAI99) –Biological Evidence (CogSci03, BL03) Sufficient Inductive bias for verb learning (Bailey97, CogSci99), construction learning (Chang03, to Appear) Model for DAML-S (ISWC02, WWW02, Computer Networks 03)

149. Distributed OPErational (DOPE) Semantics Maps Situation Calculus action axiomatization to CBN Formalism [Narayanan 99, NM2002, NM2003] Features of CBN representation  Can deal with quantitative information & resources  Natural representation of stochastic actions (selection and effects)  Variety of well established analysis and simulation techniques including mappings to other logics of change.  Natural representation of change, concurrency, and synchronization  Execution semantics

150. Problems with T(D)BN Scaling up to relational structures Supports linear (sequence) but not branching (concurrency, coordination) dynamics

151. Structured Probabilistic Inference

152. Probabilistic inference for Events –Filtering P(X_t | o_1…t,X_1…t) Update the state based on the observation sequence and state set –MAP Estimation Argmax h1…hn P(X_t | o_1…t, X_1…t) Return the best assignment of values to the hypothesis variables given the observation and states –Smoothing P(X_t-k | o_1…t, X_1…t) modify assumptions about previous states, given observation sequence and state set –Projection/Prediction/Reachability P(X_t+k | o_1..t, X_1..t)

153. Open-Source FrameNet Use the idea of open source Linux development –Frame hackers around the world –Distributed vanguard and peer review process –Pilot projects in large social networks (ICSI BCIS project) Develop software and infrastructure –Frame Creation and Modification –Annotation structures –Common API for semantic resources. –Specialized domain FrameNets

154. Conclusion Reasoning about event structure in language needs complex inference that deals with –Relational Structure –Uncertain source and domain knowledge –Complex dynamics and evolving events FrameNet is a unique resource that tackles the relational structure inherent in language and could enable qualitative changes in NL applications. We have developed a representation and inference algorithm that uses FrameNet and is capable of tractable inference for a variety of domains. Our pilot results suggest that we can effectively couple our inference techniques with FrameNet to build scalable cognitive information systems.

155. Summary  The FrameNet Project is making good progress toward our goal of producing a lexicon for a significant number of English words with uniquely detailed information about their argument structure and the semantics associated with it.  We have an automatic translation from FrameNet to computational representations that  Are able to translate FN annotations and frame structure for use by Semantic Web researchers and use ontologies on the web for semantic typing of FE’s.  Translates Frame representations to a simulation semantics that can perform frame-based inference and may provide a scalable semantics for NL systems.

156. Ongoing Work: Question Answering As part of the AQUAINT program (UCB, ICSI, Stanford), we are tasked with –coming up with a uniform formalism to encode frames, schemas and metaphors (ScaNaLU 2002) –Designing inference algorithms to reason with semantic schemas. –Others (UCB, Stanford) are tasked with trying to identify semantic relations from text. –One possible interchange language choice is DAML- S/OWL-S Hypothesis: Simulation based inference over semantic relations is useful for question answering.

157. http://www.icsi.berkeley.edu/framenet http://www.icsi.berkeley.edu/NTL

158. Ongoing Work Modeling Perspective information in Frames Building Frame Parsers –Gildea and Jurafsky

159. A Structured Event Representation of Frames Representation is motivated by –Cognitive Linguistic Theory –Tractability/Expressiveness concerns –Ability to support simulation semantics for inference Fundamental Components (from ECG) –Feature structures for cognitive linguistic primitives –Inheritance, role restrictions, constaints, and complex slots –An Evokes primitive

160. Events and actions schema Event roles before : Phase transition : Phase after : Phase nucleus constraints transition :: nucleus schema Action evokes Event as e roles actor : Entity undergoer : Entity self  e.nucleus beforeaftertransition nucleus undergoer actor

161. The Commercial-Transaction schema schema Commercial-Transaction subcase of Exchange roles customer  participant1 vendor  participant2 money  entity1 : Money goods  entity2 goods-transfer  transfer1 money-transfer  transfer2

162. The Buy schema schema Buy subcase of Action evokes Commercial-Transaction as ct roles self  ct.nucleus buyer  actor  ct.customer  ct.agent goods  undergoer  ct.goods

163. The Sell schema schema Sell subcase of Action evokes Commercial-Transaction as ct roles self  ct.nucleus seller  actor  ct.vendor  ct.agent goods  undergoer  ct.goods

164. The Pay schema schema Pay subcase of Action evokes Commercial-Transaction as ct roles self  ct.money-transfer.nucleus payer  actor  ct.customer  ct.money-transfer.agent payment  ct.money payee  ct.vendor

165. The Receive schema schema Receive subcase of Action evokes Possession as p roles receiver  actor  p.possessor received  undergoer  p.possession constraints e.before :: p.holds  false e.after :: p.holds  true

166. The Transfer schema schema Transfer subcase of Event evokes Cause-Effect as c roles act-schema : Action rec-schema : Receive agent  act-schema.receiver source : Entity theme  rec-schema.received recipient  rec-schema.receiver constraints transition :: act-schema transition :: rec-schema c.cause  act-schema c.effect  rec-schema

167. The Exchange schema schema Exchange subcase of Event roles participant1 : Human participant2 : Human entity1 : Entity entity2 : Entity transfer1 : Transfer transfer2 : Transfer agent : Entity constraints transition :: transfer1 transition :: transfer2 transfer1.source  participant1 transfer1.theme  entity1 transfer1.recipient  participant2 transfer2.source  participant2 transfer2.theme  entity2 transfer2.recipient  participant1

174. Features of Simulation Semantics Captures Fine Grained distinctions needed for interpretation –Frame-based Inferences –Aspectual Inferences (Cogsci98, IJCAI 99, COLING02) –Metaphoric Inferences (AAAI 99) –Sufficient Inductive bias for verb learning (Bailey97, CogSci99) Captures essential features of neural computation (Feldman&Ballard82, Feldman89, Valiant 94) –Active, context sensitive knowledge representation. –Natural model of concurrent and distributed computation at the knowledge level. Proposition: Simulation Semantics is Biologically Motivated. (Boccino et al. 2001, NBL01, CNS02)

175. Summary  The FrameNet Project is making good progress toward our goal of producing a lexicon for a significant number of English words with uniquely detailed information about their argument structure and the semantics associated with it.  We have an automatic translation from FrameNet to a computational representation that  Is motivated from research on neural computation and results in cognitive science.  Uses simulation semantics that may provide some features of a scalable semantics for NL systems.

176. Ongoing Work Modeling Perspective information in Frames FrameNet for Information Extraction. –Automatic pattern generation using similarity in frame space. Building Frame Parsers –Gildea and Jurafsky

177. Evidence From Biology Boccino et al (2002) show how observation and execution are matched with a (PMC but also in hot areas). Hypothesis: A neural system that implemented x-schemas would exhibit mirror properties. Experiment: Replace Boccino experiment with language input.

178. http://www.icsi.berkeley.edu/framenet http://www.icsi.berkeley.edu/NTL