1. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 1 Semantics and Semantic Analysis CSC 9010: Special Topics. Natural Language Processing. Paula Matuszek, Mary-Angela Papalaskari Spring, 2005

2. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 2 Meaning So far, we have focused on the structure of language, not on what things mean We have been doing natural language processing, but not natural language understanding. So what is natural language understanding? –Answering an essay question on an exam? –Deciding what to order at a restaurant by reading a menu? –Realizing you’ve been insulted? –Appreciating a sonnet? As hard as answering "What is artificial intelligence?"

3. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 3 Meaning, cont On the practical side, we want to “understand” natural language because morphology- and syntax-based methods will only take us so far in some things: –Machine translation –Generation –Question answering So we saw how we could use n-grams to choose the more likely translation for a word given other words. But n-grams can’t give us the potential translations in the first place.

4. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 4 Semantics What kinds of things can we not do well with the tools we have already looked at? –Retrieve information in response to unconstrained questions: e.g., travel planning –Accurate translations –Play the "chooser" side of 20 Questions –Read a newspaper article and answer questions about it These tasks require that we also consider semantics: the meaning of our tokens and their sequences

5. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 5 So What IS Meaning? From NLP viewpoint, meaning is a mapping from linguistic forms to some kind of representation of knowledge of the world It is interpreted within the framework of some sort of action to be taken. Often we manipulate symbols all the way through; the “meaning” is put in by the human user. Translations, for instance. But not always – voice command systems, for instance, may map from the representation into actions.

6. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 6 Example Question: Is there a restaurant in King of Prussia serving vegetarian dinners? From a restaurant database –Lemon Grass is a sister restaurant to the one in West Philadelphia serving traditional Thai dishes in addition to a complete vegetarian Thai menu. The service and atmosphere are quite pleasant. A welcome addition to the King of Prussia area. What do we need to know to answer this question from this text? Can we unambiguously answer it?

7. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 7 Example, continued Is there a restaurant in King of Prussia serving vegetarian dinners? Yes. Lemon Grass is a sister restaurant to the one in West Philadelphia serving traditional Thai dishes in addition to a complete vegetarian Thai menu. The service and atmosphere are quite pleasant. A welcome addition to the King of Prussia area. What do we need to know? –Vegetarian Thai menu = vegetarian dinners –Welcome addition to the KoP area = in KoP Can we unambiguously answer it? –No. But we can be fairly certain. The only parse that makes sense Restaurants normally do serve the dinner meal.

8. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 8 Knowledge Representation for NLP So what representation? A useful KR needs to give us a way to encode the knowledge relevant to our problem in a way which allows us to use it. Any representation which does this will work. How do we decide what we want to represent? –Entities, categories, events, time, aspect –Predicates, relationships among entities, arguments (constants, variables) –And…quantifiers, operators (e.g. temporal) How much structure do we capture?

9. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 9 Structured Knowledge Representations Model-based representations reflect the structure of the domain, and then reason based on the model. –Semantic Nets –Frames –Scripts Sometimes called associative networks

10. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 10 Basics of Associative Networks All include –Concepts –Various kinds of links between concepts “has-part” or aggregation “is-a” or specialization More specialized depending on domain Typically also include –Inheritance –Some kind of procedural attachment

11. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 11 Semantic Nets ulabeled, directed graph unodes represent objects, concepts, or situations ulabels indicate the name unodes can be instances (individual objects) or classes (generic nodes) ulinks represent relationships uthe relationships contain the structural information of the knowledge to be represented uthe label indicates the type of the relationship

12. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 12 Semantic Net Examples Ship Hull Propulsion Steamboat HasA InstanceOf giveBob Candy Mary Person agent recipient object Instance

13. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 13 Generic / Individual Generic describes the idea--the “notion” –static Individual or instance describes a real entity –must conform to notion of generic –dynamic –“individuate” or “instantiate” A lot of NLP using semantic nets involves instantiating generic nets based on a given piece of text.

14. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 14 Individuation example givePerson Thing Person agent recipient object giveBob Candy Mary agent recipient object Generic Representation Process the sentence “Bob gave Mary some candy”. Instantiation

15. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 15 Frames uRepresent related knowledge about a subject uFrame has a title and a set of slots uTitle is what the frame “is” – the concept uSlots capture relationships of the concept to other things uTypically can be organized hierarchically uMost frame systems have an is-a slot uallows the use of inheritance uSlots can contain all kinds of items uRules, facts, images, video, questions, hypotheses, other frames uIn NLP, typically capture relationships to other frames or entities uSlots can also have procedural attachments uon creation, modification, removal of the slot value

16. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 16 Simple Frame Example Slot NameFiller RestaurantLemon Grass CuisineThai, Vegetarian PriceExpensive ServiceExcellent AtmosphereGood LocationKoP Web pageAsk Google.

17. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 17 Usage of Frames Most operations with frames do one of two things: Fill slots –Process a piece of text to identify an entity for which we have a frame –Fill as many slots as possible Use contents of slots –Look up answers to questions –Generate new text [Rogers 1999]

18. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 18 Scripts Describe typical events or sequences Components are –script variables (players, props) –entry conditions –transactions –exit conditions Create instance by filling in variables

19. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 19 Restaurant Script Example generic template for restaurants –different types –default values script for a typical sequence of activities at a restaurant Often has a frame behind it; script is essentially instantiating the frame [Rogers 1999]

20. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 20 Restaurant Script EAT-AT-RESTAURANT Script Props: (Restaurant, Money, Food, Menu, Tables, Chairs) Roles: (Hungry-Persons, Wait-Persons, Chef-Persons) Point-of-View: Hungry-Persons Time-of-Occurrence: (Times-of-Operation of Restaurant) Place-of-Occurrence: (Location of Restaurant) Event-Sequence: first: Enter-Restaurant Script then: if (Wait-To-Be-Seated-Sign or Reservations) then Get-Maitre-d's-Attention Script then: Please-Be-Seated Script then: Order-Food-Script then: Eat-Food-Script unless (Long-Wait) when Exit-Restaurant-Angry Script then: if (Food-Quality was better than Palatable) then Compliments-To-The-Chef Script then: Pay-For-It-Script finally: Leave-Restaurant Script [Rogers 1999]

21. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 21 Comments on Scripts Obviously takes a lot of time to develop them initially. –The script itself has much of the knowledge –May be serious overkill for most NLP tasks We need this level of detail if we want to include answers based on reasoning like “Most restaurants do serve dinner.”

22. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 22 First Order Predicate Calculus (FOPC) Terms –Constants: Lemon Grass –Functions: LocationOf(Lemon Grass) –Variables: x in LocationOf(x) Predicates: Relations that hold among objects –Serves(Lemon Grass,VegetarianFood) Logical Connectives: Permit compositionality of meaning –I only have $5 and I don’t have a lot of time –Have(I,$5)  Have(I,LotofTime)

23. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 23 FOPC Semantics Sentences in FOPC can be assigned truth values True or False

24. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 24 Variables and Quantifiers Existential (  ): There exists –A restaurant that serves Vegetarian food in KoP –(  x) Restaurant(x) Serves(x,VegetarianFoor) Near(LocationOf(x), KoP) Universal (  ): For all –All vegetarian restaurants serve vegetarian food –(  x) VegetarianRestaurant(x) -> Serves(x,VegetarianFood)

25. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 25 FOPC Examples John gave Mary a book –Previously: Give(John,Mary,book) Better: –(  x) Giving(x) Giver(John,x)  Givee(Mary,x) Given(book,x) Full Definition of Give: –(  w,x,y,z) Giving(x)  Giver(w,x)  Givee(z,x)  Given(y,x)

26. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 26 Choosing a Representation We would like our representation to support: –Verifiability –Unambiguous Representation –Canonical Form –Inference –Expressiveness

27. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 27 Verifiability System can match input representation against representations in knowledge base. If it finds a match, it can return Yes; Otherwise No. Does Lemon Grass serve vegetarian food? Serves(Lemon Grass,vegetarian food)

28. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 28 Unambiguous Representation Single linguistic input can have different meaning representations Each representation unambiguously characterizes one meaning. Example: small cars and motorcycles are allowed –car(x) & small(x) & motorcycle(y) & small(y) & allowed(x) & allowed(y) –car(x) & small(x) & motorcycle(y) & allowed(x) & allowed(y)

29. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 29 Ambiguity and Vagueness An expression is ambiguous if, in a given context, it can be disambiguated to have a specific meaning, from a number of discrete, possible meanings. E.g., bank (financial institution) vs bank (river bank) An expression is vague, if it refers to a range of a scalar variable, such that, even in a specific context, it’s hard to specify the range entirely. E.g., he’s tall, it’s warm, etc.

30. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 30 Representing Similar Concepts Distinct inputs could have the same meaning –Does Lemon Grass have vegetarian dishes? –Do they have vegetarian food at Lemon Grass? –Are vegetarian dishes served at Lemon Grass? –Does Lemon Grass serve vegetarian fare? Alternatives –Four different semantic representations –Store all possible meaning representations in KB

31. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 31 Canonical Form Solution: Inputs that mean same thing have same meaning representation Is this easy? No! –Vegetarian dishes, vegetarian food, vegetarian fare –Have, serve What to do?

32. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 32 How to Produce a Canonical Form Systematic Meaning Representations can be derived from thesaurus –food ___ –dish ___|____one overlapping meaning sense –fare ___| We can systematically relate syntactic constructions –[S [NP Lemon Grass] serves [NP vegetarian dishes]] –[S [NP vegetarian dishes] are served at [NP Lemon Grass]]

33. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 33 Inference Consider a more complex request –Can vegetarians eat at Lemon Grass? –Vs: Does Lemon Grass serve vegetarian food? Why do these result in the same answer? Inference: Draw conclusions about truth of propositions not explicitly stored in KB serve(Lemon Grass,VegetarianFood) => CanEat(Vegetarians,At Lemon Grass)

34. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 34 Non-Yes/No Questions Example: I'd like to find a restaurant where I can get vegetarian food. serve(x,VegetarianFood) Matching succeeds only if variable x can be replaced by known object in KB.

35. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 35 Meaning Structure of Language Human Languages –Display a basic predicate-argument structure –Make use of variables –Make use of quantifiers –Display a partially compositional semantics

36. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 36 Compositional Semantics Assumption: The meaning of the whole is comprised of the meaning of its parts –George cooks. Dan eats. Dan is sick. –Cook(George) Eat(Dan) Sick(Dan) –If George cooks and Dan eats, Dan will get sick. (Cook(George) ^ eat(Dan))  Sick(Dan) Meaning derives from –The people and activities represented (predicates and arguments, or, nouns and verbs) –The way they are ordered and related: syntax of the representation, which may also reflect the syntax of the sentence

37. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 37 Syntax-Driven Semantics S NP VP eat(Dan) Nom V N Dan eats So….can we link syntactic structures to a corresponding semantic representation to produce the ‘meaning’ of a sentence in the course of parsing it?

38. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 38 Specific vs. General-Purpose Rules We don’t want to have to specify for every possible parse tree what semantic representation it maps to We want to identify general mappings from parse trees to semantic representations One approach: –Augment the lexicon and the grammar –Devise a mapping between rules of the grammar and rules of semantic representation

39. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 39 Semantic Attachments Extend each grammar rule with instructions on how to map the components of the rule to a semantic representation S  NP VP {VP.sem(NP.sem)} Each semantic function defined in terms of the semantic representation of choice Problem: how to define these functions and how to specify their composition so we always get the meaning representation we want from our grammar?

40. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 40 A Simple Example McDonald’s serves burgers. Associating constants with constituents –ProperNoun  McDonald’s {McDonald’s} –PlNoun  burgers {burgers} Defining functions to produce these from input –NP  ProperNoun {ProperNoun.sem} –NP  PlNoun {PlNoun.sem} –Assumption: meaning representations of children are passed up to parents for non-branching constuents Verbs are where the action is

41. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 41 V  serves {E(e,x,y) Isa(e,Serving) ^ Server(e,x) ^ Served(e,y)} where e = event, x = agent, y = patient Will every verb have its own distinct representation? –McDonald’s hires students. –McDonald’s gave customers a bonus. –Predicate(Agent, Patient, Beneficiary) Typically, divide verbs into classes based on what roles participate in the verb.

42. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 42 Doing Compositional Semantics To incorporate semantics into grammar we must –Figure out right representation for each constituent based on the parts of that constituent (e.g. Adj) –Figure out the right representation for a category of constituents based on other grammar rules, making use of that constituent (e.g. V.sem) This gives us a set of function-like semantic attachments incorporated into our CFG –E.g. Nom  Adj Nom { x Nom.sem(x) ^ Isa(x,Adj.sem)}

43. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 43 What do we do with them? Alter a parser such as an Early-style parser so when constituents (dot at the end of the rule) are completed, the attached semantic is function applied and meaning representation is created and stored with state Or, let parser run to completion and then walk through resulting tree running semantic attachments from bottom-up

44. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 44 Option 1 (Integrated Semantic Analysis) S  NP VP {VP.sem(NP.sem)} –VP.sem has been stored in state representing VP –NP.sem stored with the state for NP –When rule completed, retrieve value of VP.sem and of NP.sem, and apply VP.sem to NP.sem –Store result in S.sem. As fragments of input parsed, semantic fragments created Can be used to block ambiguous representations But: you also perform semantic analysis on orphaned constituents that play no role in final parse

45. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 45 Option 2: Post-hoc Semantic Analysis Do semantics after syntactic parse –Don’t do any semantic work on constituents we will back out of – can be a significant efficiency gain –But can’t use semantics to aid the parse.

46. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 46 Compositional Semantics Summary Hypothesis: Principle of Compositionality –Semantics of NL sentences and phrases can be composed from the semantics of their subparts Rules can be derived which map syntactic analysis to semantic representation (Rule-to- Rule Hypothesis) Parsing and mapping can be done during syntactic analysis or as a separate phase.

47. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 47 Non-Compositional Language What do we do with language whose meaning isn’t derived from the meanings of its parts –Non-compositional modifiers: fake, former, local –Metaphor: You’re the cream in my coffee. She’s the cream in George’s coffee. The break-in was just the tip of the iceberg. This was only the tip of Shirley’s iceberg. –Idioms: The old man finally kicked the bucket. The old man finally kicked the proverbial bucket. –Deferred reference: The ham sandwich wants his check. Mix lexical items with special grammar rules? Take a totally different approach?

48. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 48 Feature and Information Extraction We don’t always need a complete parse. –We may be after only some specific information –And we know what that information is Lemon Grass is a sister restaurant to the one in West Philadelphia serving traditional Thai dishes in addition to a complete vegetarian Thai menu. The service and atmosphere are quite pleasant. A welcome addition to the King of Prussia area. –We don’t care at all about the information in red Other semantic methods are more useful here.

49. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 49 Feature Extraction Group individual terms into more complex entities (which in turn become tokens) Examples –Dates, times, names, places –URLs, HREFs and IMG tags – Relationships like “X is president of Y” Can involve quite high-level features: language of a sample of text Not usually a goal in itself In this case we are generating semantics for further use by an application

50. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 50 Feature Extraction Human-meaningful features: Parse token stream, applying pattern-matching rules –general, broadly applicable features (dates) –domain-specific features (chemical names) –Can involve very sophisticated domain knowledge. Statistical features: –document length, vocabulary used, sentence length, document complexity, etc, etc Often first step in document-based analysis such as classification

51. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 51 Information Extraction Retrieve some specific information which is located somewhere in this set of documents. Don’t want all the information, just what we have specified. Information may occur multiple times in the text but we just need to find it once Often what is really wanted from a web search, for instance. Often involves long sentences, complex syntax, extended discourse, multiple writers May involve sentence fragments and other non-grammatical text

52. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 52 Some Examples of Information Extraction Financial Information –Who is the CEO/CTO of a company? –What were the dividend payments for stocks I’m interested in for the last five years? Biological Information –Are there known inhibitors of enzymes in a pathway? –Are there chromosomally located point mutations that result in a described phenotype? Other typical questions –Who is expert in some area? –Is there a vegetarian restaurant in KoP? –Can I get a flight to Chicago tomorrow?

53. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 53 Create a model of information to be extracted Create knowledge base of rules for extraction –concepts –relations among concepts Process information applying a series of tools Always involves some level of domain modeling and considerable natural language processing Information Extraction -- How

54. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 54 Staged Approach Apply a series of tools to an input text At each stage an element of is identified for possible use in the next level The end result is a set of relations suitable for entry into a database

55. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 55 Cascaded Process Tokenize POS Tag Tag named entities: Names, dates, locations Tag complex entities and relationships –Grammatical, such as noun phrases –Semantic, such as CEO Bind values to tags throughout.

56. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 56 Named Entities Named entity recognition… –Labeling all the occurrences of named entities in a text… People, organizations, lakes, bridges, hospitals, mountains, etc… This is well-understood technology, readily available and works well. Typically uses a combination of enumerated lists (often called gazetteers) and regular expressions.

57. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 57 Complex Entities and Relationships Uses Named Entities as components Pattern-matching rules specific to a given domain May be multi-pass: One pass creates entities which are used as part of later passes. –EG: First pass locates CEOs, second pass locates dates for CEOs being replaced. May involve syntactic analysis as well, especially for things like negatives and reference resolution

58. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 58 Why Does This Work? The problem is very constrained. –Only looking for a small set of items that can appear in a small set of roles We can ignore stuff we don’t care about. BUT: creating the knowledge bases can be very complex and time- consuming.

59. Some slides adapted from Dorr, www.umiacs.umd.edu/~christof/courses/cmsc723-fall04, Kurfess: www.csc.calpoly.edu/~fkurfess/Courses/CSC- 481/W03/Slides/3-Knowledge-Representation.ppt and Hirschberg: www1.cs.columbia.edu/~julia/cs4705/syllabus.html CSC 9010: Special Topics, Natural Language Processing. Spring, 2005. Matuszek & Papalaskari 59 Summary Some NLP tasks require more than morphology and syntax; they require some model of meaning: semantics Semantics can be modeled in a variety of different formalisms. The goal of all of them is to map from the sample of text being processed into some representation relevant to a real-world task Complete generic semantic parses gives us a very rich representation of text, but typically depend on the assumption that language is compositional. Other tasks such as Information Extraction may not require a detailed parse, but may depend on extensive domain knowledge How you represent and work with semantics will always depend on the NLP task you are trying to accomplish.