Semantics and Time in Language

MAS.S60Rob Speer

Catherine HavasiSome slides: James Pustejovsky

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))

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

“A dog barks.”• A dog barks.

exists x. (dog(x) & bark(x))

“A dog barks.”• A dog barks.

exists x. (dog(x) & bark(x))• (A dog) (barks)

A dog:barks:

“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 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:

“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)

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

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

“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

“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))

“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))

“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))

“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))

Your turn• We add a feature grammar rule that allows for

ditransitive (two-object) verbs:VP[SEM=<?v(?obj,?pp)>] -> DTV[SEM=?v] NP[SEM=?obj] PP[+TO,SEM=?pp]

• What are the semantics of a DTV?

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

High-level overview of C&C• MaxEnt “supertagger” tags each word with a

semantic value• Possible semantic values for verbs determined

by VerbNet

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?

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.

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

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?

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

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

Allen’s Relations

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.”

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)

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.

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.”

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.”

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.

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: ALINKIt represent the relationship between an aspectual event and its argument event.

She finished assembling the table.

• Subordination: SLINKIt 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.

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

TARSQI • GUTenLink (Georgetown)– Temporal Tagger

• Slinket (Brandeis)– Event logging

• SputLink – Based on James Allen’s time logic

Open a Document

Processed Document

Results

Making a timeline