Presentation Transcript
Practical Applications of Temporal and Event Reasoning: Practical Applications of Temporal and Event Reasoning
James Pustejovsky, Brandeis
Graham Katz, Osnabrück
Rob Gaizauskas, Sheffield
ESSLLI 2003
Vienna, Austria
August 25-29, 2003
Course Outline: Course Outline Monday-
Theoretical and Computational Motivations
Overview of Annotation Task
Events and Temporal Expressions
Tuesday
Anchoring Events to Times
Relations between Events
Wednesday
Syntax of TimeML Tags
Semantic Interpretations of TimeML
Relating Annotations
Temporal Closure
Thursday
Automatic Identification of Expressions
Automatic Link Construction
Friday-
Outstanding Problems
Friday Topics: Friday Topics
Events with Argument Binding
TimeML German Fragment
Outstanding Problems
TimeML-enabled Applications
Features in TimeML 2.0: Features in TimeML 2.0 Argument binding into Events
Pred feature in EVENT
General types with like entailments
Vendler classification:
Scope of Negation and Modality:
Represented on TLINK
Argument Binding into Events: Argument Binding into Events
Syntax of Entity: Syntax of Entity
attributes ::= aid type agreement det
aid ::= ID
{aid ::= argumentID
argumentID ::= a}
type ::=
agreement ::= ???
det ::= ‘a’|’the’|’possessive’|’quant’
Syntax of Arglink: Syntax of Arglink
attributes ::= [lid] [origin] eventInstanceID (relatedEventInstanceID | relatedArgumentID) preposition
lid ::= ID
{lid ::= LinkID
LinkID ::= l}
origin ::= CDATA
eventInstanceID ::= IDREF
{eventInstanceID ::= EventInstanceID}
relatedEventInstanceID ::= IDREF
{eventInstanceID ::= EventInstanceID}
relatedArgumentID ::= IDREF
{argumentID ::= argumentID}
preposition ::= CDATA
Example of Arguments 1: Example of Arguments 1 John left on Saturday.
John
left
on
Saturday
Example of Arguments: Example of Arguments Police arrested the suspect in the airport on Tuesday.
Police
arrested
the suspect
in
the airport
on
Saturday
Negation over Events: Currently: Negation over Events: Currently Survivors
were
not
found
on
Monday
No survivors were found.
Quantifiers and Negation: 1: Quantifiers and Negation: 1 Survivors were not found on Monday.
Survivors
Were
not
found
on
Monday
Quantifiers and Negation: 2: Quantifiers and Negation: 2 No survivors were found on Monday.
No survivors
were
found
on
Monday
INTENDED INTERPRETATION
Reference to the Argument (“no survivors”) provides a resource to the interpretation function for determining the polarity of the TLINK.
TimeML German Fragment: TimeML German Fragment (Due to Frank Schilder, ms. 2003)
TimeML in German: TimeML in German Corpus study in German focussing on the preposition in.
Ca. 100 occurrences of the preposition in extracted from taz articles
Marked with simplified TimeML:
Only TLINKS
Different Aspect specification
Marked with additional features (see below)
Goal: definition of a semantics for the proposition in considering:
Aspectual classes
Granularity
Reference time
Schilder (2003)
German temporal and event expressions: German temporal and event expressions Different tense and aspect system:
Usage of tenses:
Present tense is ambiguous wrt. Present/future tense.
No progressive form
(Past perfect preferred tense in spoken language for expressing past events)
Aspectual information not morphologically encoded in a consistent way
Different Aktionsarten:
Ingressive: verlieben (to fall in love)
Exgressive: verblühen (to wither)
Semelfactive: husten (to cough)
Iterative: hüsteln (coughing)
…
No imperfective/perfective aspect Schilder (2003)
German temporal and event expressions: German temporal and event expressions Different syntactical structure:
Prefix-verbs: ausschließen (exclude) / schließen (close): Die Bedingungen schließen einen Verkauf aus
Reflexive verbs: sich entwickeln (come out) / entwickeln (develop)
Complex verb constructions: sich in der Lage sehen etwas zu tun (feel capable of doing something)
Sah sich die Polizei schon bisher nicht in der Lage …
, dass die die Polizei sich schon bisher nicht in der Lage sah …
Normally, Verbs are at position 2, but
Participle verbs come at the end of a clause and
Subordinate clauses and relative clauses: end of clause Schilder (2003)
Slide17:
“Schröder hatte bereits am Wochenende signalisiert, dass er eine dritte Amtszeit anstrebt.”
29.8.03
Schröder
hatte
bereits
am
Wochenende
signalisiert
dass
er
eine dritte Amtszeit
anstrebt
Outstanding Problems: Outstanding Problems
Semantics of TimeML: Semantics of TimeML A text T is satisfied by a model M iff there are functions
fe: Dome -> Pow(E), and
fei: Domei -> E
ft: Domt -> I , such that
for all tags t Tag(T), t is satisfied by fe fei and ft in M.
A tag t is satisfied by fe,ft, and fei in M iff if t has the form
“” then fe() = Val()
“” then ft() = Val()
“” then (fei()) ft ( )
Problems for Interpretation: Problems for Interpretation Negation
John didn’t teach on Tuesday
-> SCOPE for negation
Multiple Events
John taught twice on Tuesday
“” then fei() fe()
Condition on Embedding Functions
Problems for TimeML: Problems for TimeML Set-valued Times
John taught three days every month
PROBLEM: the temporal identifier can’t be interpreted as denoting a particular interval of time, it must be a set of intervals (or even a set of sets of intervals!)
Disjunction
John taught on Monday or on Wednesday
Some Solutions: Some Solutions Negation:
Use TLINK as a scope domain, eliminate MAKEINSTANCE
John didn’t teach on Tuesday
New TLINK Rules
“” there is an e E such that e fe() and (e) ft ( )
“”
there is no e E such that e fe() and (e) ft ( )
Some Solutions: Some Solutions Multiple events
Add cardinality element to the TLINK
John taught twice on Tuesday
“” is satisfied iff there are Val() distinct e E such that e fe() and (e) ft ( )
Harder Problems: Harder Problems Vagueness
When he left, shortly after 5 am Tuesday, he discovered someone had smashed a window.
Appavu has been involved with healthcare standards development for about a decade, an interest he developed shortly after he began working with information systems at Cook County.
Domino's Pizza of Washington reported that they delivered "In excess" of 100 large pizzas to the White House late this afternoon.
It was then,early in December of 1977, that he went to the NORML conference.
Vagueness: Vagueness Current Treatment:
late this afternoon
early in December of 1977
Problem:
No semantics for mod attributes means no possibility for doing reasoning.
It was then,early in December of 1977, that he went to the NORML conference. Two weeks later he was a convert.
Before or after Christmas?
We might fake a solution by being overly general:
Interpret START to mean “the first half of”
Current Treatment: Current Treatment No general solution for mod values:
Shortly after 5am -> minutes
Shortly after he began working -> weeks or months
Semantic Weakness: Semantic Weakness Simple annotation of temporal relations is too week:
President John F. Kennedy's gravesite at Arlington National Cemetery has been restored to its original condition, after someone tried unsuccessfully to dig up some of its granite paving stones.
South Africa, after losing the toss, were bowled out for 107 against England.
How long after?
Days or weeks
An hour or two.
This is not generally encoded overtly.
Context Dependent Vagueness: Context Dependent Vagueness If we did code this, lots of world-knowledge based information could be encoded by annotators:
They ate lunch early on Monday.
They ate dinner early on Monday.
They ate breakfast early on Monday.
Probably
before noon
in the early evening
in the very early morning
Questions: Questions How to talk about a “likely distribution” in time?
How to compare such annotations?
TimeML-enabled Applications: TimeML-enabled Applications
Web-basedTemporal Reasoning: Web-based Temporal Reasoning Web Negotiation Agents (Brokers)
Scheduling Programs
Semantic Web: Semantic Web
Delivery within five business days.
order
delivery
within
five business days
Scheduling Issues: Scheduling Issues
Mary teaches on Mondays and Wednesdays in the fall.
Sophie goes to daycare on Thursday and Friday at 4:00pm in October.
www.TimeML.org: www.TimeML.org