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Verb Semantics Janine Scott Nov. 9, 2004 74.406. Overview Introduction to Grammatical Concepts Background & features of WordNet Using WordNet online Dr.

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Presentation on theme: "Verb Semantics Janine Scott Nov. 9, 2004 74.406. Overview Introduction to Grammatical Concepts Background & features of WordNet Using WordNet online Dr."— Presentation transcript:

1 Verb Semantics Janine Scott Nov. 9, 2004 74.406

2 Overview Introduction to Grammatical Concepts Background & features of WordNet Using WordNet online Dr. Fernando Gomez’s work on defining predicates for English verbs Conclusion & Application

3 Introduction to Concepts subjects & predicates predicates & argument structure subcategorization & selectional restrictions thematic relations & theta roles complements & adjuncts ontologies

4 Introduction to Concepts Subjects & Predicates: We can divide a sentence into a subject and a predicate. In English, syntax usually dictates that the subject precedes the predicate.

5 Introduction to Concepts Subjects and Predicates: The dog barked. John liked his new car. The driver of the big yellow bus put the one-way ticket on the seat next to the little boy. The movie was seen by all the students.

6 Introduction to Concepts Predicates & Their Arguments: arguments are obligatory elements predicates may have 0 – 3 arguments predicates impose both syntactic and semantic restrictions on their arguments these restrictions may be encoded using thematic relations bundles of thematic relations are called theta roles

7 Introduction to Concepts 0 arguments: Sit down! (imperative command, the argument “you” is implied) 1 argument: The dog barked. (intransitive) 2 arguments: John likes his new car. (transitive) 3 arguments: The driver of the big yellow bus put the one-way ticket on the seat next to the little boy. (ditransitive)

8 Introduction to Concepts Subcategorization (syntactic) restrictions: *I looked the table. The verb look requires a PP, not an NP complement. Selectional (semantic) restrictions: *The ocean likes hockey. The inanimate ocean cannot like anything.

9 Introduction to Concepts Thematic relations: agent – doer of an action theme – undergoes action, or is moved, experienced, or perceived goal – entity towards which motion takes place recipient – goal in a change of possession source – entity from which a motion takes place (opposite of goal)

10 Introduction to Concepts Thematic relations continued… location – place where action occurs instrument – object with which an action is performed benefactive – entity for whose benefit an event took place (from Carnie 2001)

11 Introduction to Concepts More than one thematic relation may be assigned to one argument: John gave Anna the books. John is both the agent and the source. Anna is the recipient the books are the theme The verb assigns one theta role to each argument (a theta role can consist of more than one thematic role).

12 Introduction to Concepts Complements & Adjuncts: Adjuncts are never arguments, and they are not assigned theta roles. Today I went to the store. It was cold on Monday. Adjuncts may be moved or removed from a clause (because they are not obligatory): I went to the store today. / I went to the store. On Monday it was cold. / It was cold.

13 Introduction to Concepts Complements & Adjuncts: Complements are arguments required by the verb, and usually have a fixed position. Each complement is assigned one theta role. John likes pizza. *John likes. *Pizza John likes. *John pizza likes. John is the agent, and pizza is the theme.

14 Introduction to Concepts Ontology: An ontology is an explicit formal specification of how to represent the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them. (University of Illinois at Urbana-Champaign Digital Libraries Initiative glossary

15 WordNet Background Features & their definitions –synsets –hypernyms –troponyms Using WordNet online

16 WordNet Developed at Princeton University, WordNet is an online lexical reference system whose design is inspired by current psycholinguistic theories of human lexical memory. English nouns, verbs, adjectives and adverbs are organized into synonym sets, each representing one underlying lexical concept. Different relations link the synonym sets.

17 WordNet Synsets: A synset is a set of words that have the same part- of-speech and are interchangeable in some context. Lexical and semantic relations between synsets are represented by pointers. Some relations represented are: hypernymy/hyponymy, antonymy, entailment, and meronymy/holonymy. Nouns and verbs are organized into hierarchies based on hypernymy/hyponymy relations.

18 WordNet Hypernyms: A hypernym is a generic term used to designate a whole class of specific instances of the class. eg. Flower is a hypernym of tulip. (Tulip is a hyponym of flower.) Troponyms: A troponym is a verb expressing a specific manner elaboration of another verb. eg. shuffle, prowl, and march are all troponyms of walk (they are particular ways to walk).

19 WordNet Let’s look at examples of verbs in WordNet: eg. explain (3 senses) eg. give (44 senses) WordNet has been mapped into other languages, and these systems have even been grouped in multilingual databases like EuroWordNet.

20 Fernando Gomez, Ph.D. Professor of Computer Science at the University of Central Florida, Orlando, Florida. Introduction to his research Hierarchy of inheritance with sub- and super- predicates Algorithm for semantic interpretation Examples of predicates

21 Background The goal of his research is to define predicates for every English verb and place them into a hierarchy in which thematic roles and inferences can be inherited. The definition of predicates linked to a general ontology, such as WordNet, and to grammatical relations, can solve the crucial issues of verb and prepositional meaning and thematic roles.

22 Background Verb classes in WordNet have been grouped based on troponymy (a verb elaborating upon the specification provided by another verb). In contrast, Gomez defines predicate classes based on the inheritance of thematic roles in a hierarchy of predicates. This hierarchy uses subpredicates and superpredicates for inheritance.

23 Inheritance Hierarchy A generic predicate subsumes all its subpredicates like the generic concept book subsumes all its subconcepts (novel, textbook, diary, etc.). A subpredicate inherits thematic roles and inferences from its super-predicate.

24 Inheritance Hierarchy An example of a predicate hierarchy (from most specific to most general) is: open-an-enterprise is-a initiate-an-enterprise-project is-a organize is-a initiate-something is-a give-rise-to-something is-a make-or-create- something

25 Inheritance Hierarchy The difference between a generic predicate and its subpredicates is given by one or more of the following: a) specific selectional restrictions for the thematic roles b) different syntactic realizations of the thematic roles c) specific sets of inferences associated with the subpredicates

26 Inheritance Hierarchy In the hierarchy shown above, all predicates inherit the thematic roles (such as agent and theme) of make-or-create-something, but there may be differences in selectional restrictions or syntactic relations. For example, a selectional restriction would allow monkeys or other non-human animate beings to be agents in make-or-create- something, but not in open-an-enterprise.

27 Inheritance Hierarchy We can coalesce several WordNet synsets into one predicate or map the same WordNet synset into distinct predicates. For example, WordNet senses 1, 2, & 3 for “gain” are coalesced into one predicate “gain-something”…

28 Inheritance Hierarchy WordNet: 1. derive, gain -- (obtain; "derive pleasure from one's garden") 2. acquire, win, gain -- (win something through one's efforts; "I acquired a passing knowledge of Chinese"; "Gain an understanding of international finance") 3. profit, gain, benefit -- (derive a benefit from; "She profited from his vast experience“) Gomez’s predicate: [gain-something; gain1, gain2, gain3 in WN (is-a (transfer-of-something)) (theme (thing) (obj)) (from-poss (human-agent) ((prep from))) (source-t (-human-agent thing) ((prep from)) (prep-cp by from)))]

29 Algorithm Semantic interpreter algorithm: The semantic interpreter algorithm is based on the idea that the meaning of the verb depends not only on its selectional restrictions, but also on the syntactic relations that realize them. The semantic interpretation algorithm is activated by the parser after parsing a clause. The parser does not resolve structural ambiguity, which is delayed until semantic interpretation. The mapping of WordNet verb synsets to predicates provides a list containing the predicates for the verb of the clause. The goals of the algorithm are to select one predicate from that list, attach PPs and identify thematic roles and adjuncts. All these tasks are simultaneously achieved.

30 Algorithm For each syntactic relation (SR) in the clause (starting with the NPcomplements) and for every predicate in the list of predicates, the algorithm checks if the predicate explains the SR. A predicate explains an SR if there is a thematic role in the predicate realized by the SR and the selectional restrictions of the thematic role subsume the ontological category of the head noun of the syntactic relation. This process is repeated for each SR in the clause and each predicate in the list of predicates. Then, the predicate that explains the most SRs is selected as the meaning of the verb. The thematic roles of the predicate are identified as a result of this process. In case of ties, the predicate having the greatest number of thematic roles realized is selected.

31 Algorithm Every syntactic relation that has not been linked to a thematic role must be an adjunct or an NP modifier. The entries for adjuncts are stored in the root node action and are inherited by all predicates. Adjuncts are identified after the meaning of the verb has been determined because they do not belong to the argument structure of the predicate.

32 Predicate Examples Example of a predicate: The WordNet synset “move2, displace, make move – (cause to move)”: [cause-to-change-location (is-a (action)) (wn-map (move2)) (agent (human-agent animal) (subj-if-obj)) (theme (phy-thing) (obj subj-if-no-obj)) (source (location phy-thing) ((prep from))) continued…

33 Predicate Examples “move2” continued… (goal (location phy-thing) ((prep to towards toward in through into back-to along over beside above by on under below throughout beyond past across near up))) (instrument (instrumentality animal-body-part) (subj-if- obj ((prep with on in))) (animal) ((prep on))) (distance (distance linear-measure) ((prep for))) (inanimate-cause (phenomenon physical-thing) (subj- if-obj))]

34 Predicate Examples Sentences interpreted with the above predicate: I moved. I moved the chair. I moved the chair from the kitchen. I moved the chair past the sofa. I moved from the kitchen to the dining room on one foot.

35 Predicate Examples Example of a subpredicate: The predicate pull, which corresponds to the synset “pull1, draw, force,” and that contains such forms as “jerk, twitch, trail, drag” etc. is: [pull (is-a (cause-to-change-location)) (wn-map (pull1)) (agent (human-agent animal) (subj-if-obj)) (theme (instrumentality physical-thing) (obj (prep from off))) (source (location physical-thing) ((prep from off)))]

36 Predicate Examples More subpredicate examples: [transport (is-a (cause-to-change-location)) (theme (physical-thing) (obj obj2)) (goal (human-agent animal) (obj-if-obj2 (prep for))) (location physical-thing) ((prep to towards toward in through into back-to along over beside above by on under below throughout beyond past across near up)))]

37 Predicate Examples [put (is-a (cause-to-change-location)) (wn-map (put1)) (theme (physical-thing) (obj)) (goal (location physical-thing) ((prep on in towards through into back-to along over beside above by under below throughout beyond past across near))) (instrument (instrumentality) ((prep with)) (source (nil) (nil))] Note: prep to is not in the goal list

38 Conclusion & Application Gomez’s work shows that thematic roles are linked to the syntactic relations that realize them. Verb senses may be defined by specifying more and more specific instances of semantic categories with subpredicates that inherit thematic roles and selectional restrictions of their superpredicates.

39 Conclusion & Application Gomez’s manipulation of the WordNet ontology is useful for defining predicates in English. This ontology of predicates could facilitate multilingual tasks such as information retrieval and machine translation, as well as deeper monolingual natural language processing tasks, such as understanding.

40 Conclusion & Application Since the selectional restrictions of the predicates are ontological categories in WordNet, we can hypothesize that the predicates defined for English can be mapped into other languages, like WordNet has been mapped into other languages. Gomez is currently testing and applying his work with a project called SNOWY that is reading and acquiring knowledge from texts of the WorldBook encyclopedia.

41 References & Links Carnie, Andrew. 2002. Syntax: A Generative Introduction. United Kingdom: Blackwell Publishing. Gomez, F., C. Segami, and R. Hull. 1997. Determining Prepositional Attachment, Prepositional Meaning, Verb Meaning, and Thematic Roles. Computational Intelligence, 13(1), pp. 1-31. Gomez, F. 1998. Linking WordNet Verb Classes to Semantic Interpretation. Proceedings of the COLING-ACL Workshop on the Usage of WordNet on NLP Systems. Université de Montréal, Montréal. Gomez, F. 2001.An Algorithm for Aspects of Semantic Interpretation Using an Enhanced WordNet. Proceedings of the 2nd Meeting of the North American Chapter of the Association for Computational Linguistics, NAACL-2001, CMU, Pittsburgh. Gomez, F. (working paper as of March 26, 2003) Verb Predicates, Meaning and Multilinguality. Available at: workshop/papers/gomez.pdf Gomez, F. 2004. Building Verb Predicates: A computational View. Proceedings of the 42nd Meeting of the Association for Computational Linguistics, ACL-2004, Barcelona, Spain, 2004. University of Illinois at Urbana-Champaign Digital Libraries Initiative glossary at WordNet online at

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