1. Semantics and Time in Language MAS.S60 Rob Speer Catherine Havasi Some slides: James Pustejovsky

2. Lexical semantics We’ve been trying to make word meanings into a functional programming language Applying functions to each other, up the parse tree, gives us a logic expression in the end But how do we figure out crazy functions like: \X \y. X(\x. chase(y, x))

3. Being an un-parser Work backwards from the result you want Un-parse your way down the parse tree

4. “A dog barks.” A dog barks. exists x. (dog(x) & bark(x))

5. “A dog barks.” A dog barks. exists x. (dog(x) & bark(x)) (A dog) (barks) A dog: barks:

6. “A dog barks.” A dog barks. exists x. (dog(x) & bark(x)) (A dog) (barks) A dog: \P. exists x. (dog(x) & P(x)) barks: \z. bark(z)

7. “A dog barks.” A dog barks. exists x. (dog(x) & bark(x)) (A dog) (barks) A dog: \P. exists x. (dog(x) & P(x)) barks: \z. bark(z) (A(dog)) (barks) A: dog:

8. “A dog barks.” A dog barks. exists x. (dog(x) & bark(x)) (A dog) (barks) A dog: \P. exists x. (dog(x) & P(x)) barks: \z. bark(z) (A(dog)) (barks) A: \Q. \P. exists x. (Q(x) & P(x)) dog: \z. dog(z)

9. Lexical items we learned A: \Q. \P. exists x. (Q(x) & P(x)) dog: \z. dog(z) barks: \z. bark(z)

10. “Angus chases a dog.” Angus chases a dog: exists x. (dog(x) & chase(Angus, x))

11. “Angus chases a dog.” Angus chases a dog: exists x. (dog(x) & chase(Angus, x)) Angus(chases a dog) chases a dog: \y. exists x. (dog(x) & chase(y, x) a dog: \P. exists x. (dog(x) & P(x)) from earlier slides

12. “Angus chases a dog.” Angus chases a dog: exists x. (dog(x) & chase(Angus, x)) Angus(chases a dog) chases a dog: \y. exists x. (dog(x) & chase(y, x) a dog: \P. exists x. (dog(x) & P(x)) from earlier slides (chases) (a dog) Let’s try to make something like this: (\P. exists x. (dog(x) & P(x)) (\z. chase(y, z))

13. “Angus chases a dog.” Angus chases a dog: exists x. (dog(x) & chase(Angus, x)) Angus(chases a dog) chases a dog: \y. exists x. (dog(x) & chase(y, x) a dog: \P. exists x. (dog(x) & P(x)) from earlier slides (chases) (a dog) Let’s try to make something like this: (\P. exists x. (dog(x) & P(x)) (\z. chase(y, z))

14. “Angus chases a dog.” Angus chases a dog: exists x. (dog(x) & chase(Angus, x)) Angus(chases a dog) chases a dog: \y. exists x. (dog(x) & chase(y, x) a dog: \P. exists x. (dog(x) & P(x)) from earlier slides (chases) (a dog) Let’s try to make something like this: (\P. exists x. (dog(x) & P(x)) (\z. chase(y, z)) chases: \y. doSomethingWith(\z. chase(y, z))

15. “Angus chases a dog.” Angus chases a dog: exists x. (dog(x) & chase(Angus, x)) Angus(chases a dog) chases a dog: \y. exists x. (dog(x) & chase(y, x) a dog: \P. exists x. (dog(x) & P(x)) from earlier slides (chases) (a dog) Let’s try to make something like this: (\P. exists x. (dog(x) & P(x)) (\z. chase(y, z)) chases: \X. \y. X(\z. chase(y, z))

16. Your turn We add a feature grammar rule that allows for ditransitive (two-object) verbs: VP[SEM= ] -> DTV[SEM=?v] NP[SEM=?obj] PP[+TO,SEM=?pp] What are the semantics of a DTV?

17. High-level overview of C&C Parses using a Combinatorial Categorial Grammar (CCG) – fancier than a CFG – includes multiple kinds of “slash rules” for gaps and fillers – lots of grad student time spent transforming Treebank

18. High-level overview of C&C MaxEnt “supertagger” tags each word with a semantic value Possible semantic values for verbs determined by VerbNet

19. High-level overview of C&C Combine the resulting semantic “tags” Find the highest-probability result with coherent semantics Doesn’t this create billions of parses that need to be checked?

20. High-level overview of C&C Find the highest-probability result with coherent semantics Doesn’t this create millions of parses that need to be checked? Yes. A typical sentence uses 25 GB of RAM to find the best parse. That’s where the Beowulf cluster comes in.

21. Questions about time? The Pierre Vinken example Events in FrameNet Question answering

22. Time in Q&A When are finals this semester? Who is currently president of the United States? How many different airports has Pittsburgh had? How many classes have we had since January? When did the Berlin wall fall?

23. Difficulties More than 66% of times in documents are relative Only 15% of documents refer to the “date of creation” (DOC) 42% percent of the uses of the word “today” are non-specific

24. James Allen Created a temporal logic 13 basic relations – 6 types, their inverses and equal

25. Allen’s Relations

26. Types of Information Properties – Hold over an interval and all subintervals – “Rob was asleep all morning.” Events – Hold over a interval and no sub events – “Lance wrote a program last night.” Processes – Hold over some sub intervals – “Brett demoed during sponsor week.”

27. What is TimeML? (ISO) Standard language for the mark-up of: – temporal expressions – events – temporal anchoring of events (relations between events and temporal expressions) – temporal ordering of events (relations between events and other events)

28. Labeling What? Events are taken to be situations that occur or happen, punctual or lasting for a period of time. Times may be either points, intervals, or durations. Relations can hold between events and events and times.

29. An example “ Two Russians and a Frenchman left the Mir and endured a rough landing on the snow- covered plains of Central Asia on Thursday. The two Russians arrived on the Mir last August. Solovyou celebrated his 50th birthday during his six-month space voyage. ”

30. An example “ Two Russians and a Frenchman left the Mir and endured a rough landing on the snow- covered plains of Central Asia on Thursday. The two Russians arrived on the Mir last August. Solovyou celebrated his 50th birthday during his six-month space voyage. ”

31. Events and Relations Event expressions; – tensed verbs; has left, was captured, will resign; – stative adjectives; sunken, stalled, on board; – Nominals: merger, Military Operation, Gulf War; Dependencies between events and times: – Anchoring; John left on Monday. – Orderings; The party happened after midnight. – Embedding; John said Mary left.

32. LINKs Temporal: TLINK It represents the temporal relationship holding between events or between an event and a timex: Mary arrived in Boston last Thursday. Aspectual: ALINK It represent the relationship between an aspectual event and its argument event. She finished assembling the table. Subordination: SLINK It is used for contexts introducing relations between an I- ACTION/I-STATE event and its event argument, or an event and a negation or modal : She tried to buy some wine.

33. TARSQI Add and tag time expressions in text TempEx (MITRE) – Determines extents and nomalizations GUTime (Brandeis) – Ground things like “last week” Evita (Brandeis) – Recognize events in time

34. TARSQI GUTenLink (Georgetown) – Temporal Tagger Slinket (Brandeis) – Event logging SputLink – Based on James Allen’s time logic

35. Open a Document

36. Processed Document

37. Results

38. Making a timeline