1. Lexical Semantics Read J & M Chapter 16. The task of classifying all the words of language, or what's the same thing, all the ideas that seek expression, is the most stupendous of logical tasks. Anybody but the most accomplished logician must break down in it utterly; and even for the strongest man, it is the severest possible tax on the logical equipment and faculty. Charles Sanders Peirce, letter to editor B. E. Smith of the Century Dictionary

2. Relating Words and Concepts WordsConcepts Surface properties MorphologicalSome properties, e.g. number Spelling Pronunciation Grammatical function Part of speechObjects, actions, events, properties Subcategorization " MeaningTaxonomic relations Inference rules RegisterDiscourse conventions

3. One to Many Mappings Homonyms (same spelling, same pronounciation, different meanings) spring

4. One to Many Mappings Homographs (same spelling, different pronounciation, same meaning) bass

5. One to Many Mappings Homophones (different spelling, same pronounciation, different meanings) night knight http://www.cooper.com/homophonezone/

6. One to Many Mappings Polysemy (multiple related meanings) knight

7. Many to One Mappings Synonymy food nourishment grub

8. Synsets The largest synset in WordNet is: buttocks, nates, arse, butt, backside, bum, buns, can, fundament, hi ndquarters, hind end, keister, posterior, prat, rear, rear end, rump, s tern, seat, tail, tail end, tooshie, tush, bottom, behind, derriere, f anny, ass The next is: dohickey, dojigger, doodad, doohickey, gimmick, hickey, gizmo, gismo, gubbins, thingamabob, thingumabob, thingmabob, thingamajig, thingumajig, thingmajig, thingummy http://www.cogsci.princeton.edu/~wn/

9. Other Relations Among Words Hyponymy animal mammal horse horse is a hyponym of mammal and animal. mammal is a hypernym of horse.

10. The Same Thing for Verbs Troponymy go walk shuffleambleswaggermarch walk is a troponym of go.

11. Another Relation between Words Meronymy Nouns:brim and crown are meronyms of hat Verbs:step is a meronym of walk

12. WordNet http://www.cogsci.princeton.edu/~wn/

13. WordNet Sense Distribution

14. Maybe We Need to Represent Relationships Among Concepts, not Words

15. weightless light pale Maybe We Need to Represent Relationships Among Concepts, not Words

16. Ontology The subject of ontology is the study of the categories of things that exist or may exist in some domain. The product of such a study, called an ontology, is a catalog of the types of things that are assumed to exist in a domain of interest D from the perspective of a person who uses a language L for the purpose of talking about D. The types in the ontology represent the predicates, word senses, or concept and relation types of the language L when used to discuss topics in the domain D. An uninterpreted logic, such as predicate calculus, conceptual graphs, or KIF, is ontologically neutral. It imposes no constraints on the subject matter or the way the subject may be characterized. By itself, logic says nothing about anything, but the combination of logic with an ontology provides a language that can express relationships about the entities in the domain of interest. An informal ontology may be specified by a catalog of types that are either undefined or defined only by statements in a natural language. A formal ontology is specified by a collection of names for concept and relation types organized in a partial ordering by the type-subtype relation. Formal ontologies are further distinguished by the way the subtypes are distinguished from their supertypes: an axiomatized ontology distinguishes subtypes by axioms and definitions stated in a formal language, such as logic or some computer-oriented notation that can be translated to logic; a prototype-based ontology distinguishes subtypes by a comparison with a typical member or prototype for each subtype. Large ontologies often use a mixture of definitional methods: formal axioms and definitions are used for the terms in mathematics, physics, and engineering; and prototypes are used for plants, animals, and common household items.axiomatized ontologyprototype-based ontology - John Sowa (http://www.jfsowa.com/ontology/)http://www.jfsowa.com/ontology/

17. An Example of an Ontology Penman (Generalized) Upper Model: http://www.darmstadt.gmd.de/publish/komet/gen-um/node9.html

18. What are the Real Differences between Words and Concepts? Concepts without words (e.g., schadenfreude, or ) Many to many mappings Surface linguistic facts, such as subcategorization frames: I gave the book to John. I donated the book to John. I gave John the book.* I donated John the book. I was mad at John. I was angry at John. * I was sore at John. * I was livid at John.

19. Linguistic Facts are More Arbitrary than World Knowledge John gave/sent/read Bill the book. * John donated/returned/transferred Bill the book. One possible explanation: Give, send, and read come to English through German. Donate, return, and transfer come to English from Latin.

20. What Classes Should an Ontology Contain? Use words as the concepts. WordNet synsets do this. Use words plus some general concepts for which we don’t have words: SAYING&SENSING in the Upper Model Psychological Feature in WordNet Create new primitives and break words down into them: Conceptual Dependency

21. Representing Events and Relationships Many nouns have a straightforward semantics: The noun corresponds to a set of objects in the world. Examples: cat, apple, car. Many adjectives can also be represented as sets of things that possess some property: red, fuzzy, sharp. But most verbs, as well as many nouns, adjectives, and adverbs, represent events and relationships that have internal structure: John gave Bill the book before he left for school.

22. How Close to the Surface Should We Stay? John kicked the ball. (1)  e,x isa(e,kicking)  kicker(e,John)  kicked-obj(e,x)  isa(x,ball) (2)  e,x isa(e,kicking)  agent(e,John)  AE(e,x)  isa(x,ball) (3) John  PROPEL  ball John  MOVE  foot  Loc(ball)

23. When is Deep Meaning Worthwhile? Much of the early work on CD was for story comprehension. We need deep meaning if we want to be able to answer questions like, “What moved?” But suppose we’re interested in MT?

24. Thematic Roles A middle ground:

25. Mapping Surface Forms to Thematic Roles Priority for subject assignment: AGENT, INSTRUMENT, THEME Sue cooked the potatoes. The steam cooked the potatoes. The potatoes cooked. Mary cooked. How to assign roles? Selectional restrictions.

26. Mapping Words to Meanings FrameNet (http://www.icsi.berkeley.edu/~framenet/)http://www.icsi.berkeley.edu/~framenet/ Conceptual Dependency

27. One Important Issue – Lexicons Change all the Time As new concepts emerge: http://www.ananova.com/news/story/sm_801230.html?menu=news.technology.email As new expressions for old concepts emerge: 24/7