Transcript said

LEXICAL SEMANTICS AND
SEMANTIC ANNOTATION
James Pustejovsky
(with additional slides from:
Martha Palmer, Nianwen Xue,
Olga Babko-Malaya, Ben Snyder)
CLSW 2011
NTU, Taipei
May 4, 2011
EXAMPLES OF SEMANTIC ANNOTATIONS

Predicators and their named arguments


Anaphors and their antecedents


[The protein] inhibits growth in yeast. [It] blocks
production…
Acronyms and their long forms


[The man]agent painted [the wall]patient.
[Platelet-derived growth factor] (known as [pdgf]) impacts …
Semantic Typing of entities

[The man]human fired [the gun]firearm
LINGUISTIC PHENOMENA

Syntactic Structure


Predicate Argument Structure


Who did what to whom: Subject, object, predicate
Temporal Structure


Describes grammatical arrangements of words into
hierarchical structure
Temporal ordering and anchoring of events in a text
Emotive and Discourse Structure

How language is used across sentences, and how content is
expressed emotionally.
Annotated corpora allow us to evaluate and train
systems to be able to make these distinctions
MOTIVATION OF ANNOTATION

Semantic annotation is critical for robust
language understanding


Annotation schemata should focus on a single
coherent theme:


Question answering, summarization, inference, reading,
…
Different linguistic phenomena should be annotated
separately over the same corpus
The Annotate, Train, and Test Model advances
linguistic theory:


Theories needs testing to evaluate coverage and
predictive force.
Semantic theories are too complex to develop without
this model.
METHODOLOGICAL ASSUMPTION
 Annotation

assumes a given feature set
 Feature

set:
encodes specific structural descriptions and
properties of the input data
 Structural

scheme:
descriptions:
theoretically-informed attributes derived from
empirical observations over the data
Theory
Description
Features
Annotation
LINGUISTIC ANNOTATION SCHEMES
PropBank
•
–
Palmer, Gildea, and Kingsbury (2005)
NomBank
•
–
Meyers, Reeves, Macleod, Szekely, Zielinska, Young,
and Grishman (2004)
TimeBank
•
–
Pustejovsky, Littman, Knippen, and Sauri (2005)
Opinion Corpus
•
–
Wiebe, Wilson, and Cardie (2005)
Penn Discourse TreeBank
•
–
Miltsakaki, Prasad, Joshi, and Webber (2004)
PROPBANK
•
•
•
Corpus annotated with semantic roles for
arguments and adjuncts of verbs
1M word Penn Treebank II WSJ corpus.
Coarse-grained sense tags, based on grouping
of WordNet senses
PROPOSITION BANK:
FROM SENTENCES TO PROPOSITIONS
Powell met Zhu Rongji
Powell and Zhu Rongji met
Powell met with Zhu Rongji
Powell and Zhu Rongji had
a meeting
...
Proposition: meet(Powell, Zhu Rongji)
meet(Somebody1, Somebody2)
PROPBANK ANNOTATION EXAMPLE
 [ArgM-ADV
According to reports],
[Arg1sea trials for
[Arg1 a patrol boat]
[Rel_develop.02 developed]
[Arg0 by Kazakhstan]]
are being
[Rel_conduct.01 conducted] and
[Arg1 the formal launch] is [Rel_plan.01 planned]
[ArgM-TMP for the beginning of April this year].
PROPOSITION BANK:
FROM SENTENCES TO PROPOSITIONS
Powell met Zhu Rongji
Powell and Zhu Rongji met
Powell met with Zhu Rongji
Powell and Zhu Rongji had
a meeting
...
Proposition: meet(Powell, Zhu Rongji)
meet(Somebody1, Somebody2)
PROPBANK ANNOTATION EXAMPLE
 [ArgM-ADV
According to reports],
[Arg1sea trials for
[Arg1 a patrol boat]
[Rel_develop.02 developed]
[Arg0 by Kazakhstan]]
are being
[Rel_conduct.01 conducted] and
[Arg1 the formal launch] is [Rel_plan.01 planned]
[ArgM-TMP for the beginning of April this year].
WHAT IS A PROPBANK?

A PropBank is a corpus annotated with the
predicate-argument structure of the verbs:

English Propbank: www.cis.upenn.edu/~ace 3/’04 LDC
Kingsbury and Palmer 2002, Palmer, Gildea, Kingsbury, 2005



Wall Street Journal, 1M words, 120K+ predicate instances
Brown, 14K predicate instances
Chinese Propbank: www.cis.upenn.edu/~chinese/cpb
Xue and Palmer 2003, Xue 2004




Xinhua (250K words – almost done),
Sinorama (250K words – estimated 2007)
Nominalized verbs for English = NomBank/NYU
Chinese NomBank?
CAPTURING “NEUTRAL” SEMANTIC ROLES
Boyan broke [ Arg1 the LCD-projector.]
break (agent(Boyan), patient(LCD-projector))



[Arg1 The windows] were broken by the hurricane.
[Arg1 The vase] broke into pieces when it toppled
over
FRAMES FILE EXAMPLE: GIVE
< 4000 FRAMES FOR PROPBANK
Roles:
Arg0: giver
Arg1: thing given
Arg2: entity given to
Example:
double object
The executives gave the chefs a standing ovation.
Arg0:
The executives
REL:
gave
Arg2:
the chefs
Arg1:
a standing ovation
FRAMES FILE EXAMPLE: GIVE
W/ THEMATIC ROLE LABELS
Roles:
Arg0: giver
Arg1: thing given
Arg2: entity given to
Example:
double object
The executives gave the chefs a standing ovation.
Arg0: Agent
The executives
REL:
gave
Arg2: Recipient the chefs
Arg1: Theme
a standing ovation
VerbNet – based on Levin classes
PROPBANK EXERCISE EX.

[He]-Arg1 Theme [will]-MOD [probably]-MOD be
[extradited]-rel [to the U.S]-DIR [for trial under an
extradition treaty President Virgilia Barco has revived]PRP.

He will probably be extradited to the U.S for trial under
[an extradition treaty]-Arg1Theme [President Virgilia
Barco]-Arg0Agent has [revived]-rel.
A CHINESE TREEBANK SENTENCE
国会/Congress 最近/recently 通过/pass 了/ASP 银行法
/banking law
“The Congress passed the banking law recently.”
(IP (NP-SBJ (NN 国会/Congress))
(VP (ADVP (ADV 最近/recently))
(VP (VV 通过/pass)
(AS 了/ASP)
(NP-OBJ (NN 银行法/banking law)))))
THE SAME SENTENCE, PROPBANKED
通过(f2)
(IP (NP-SBJ arg0 (NN 国会))
(VP argM (ADVP (ADV 最近))
(VP f2 (VV 通过)
(AS 了)
(pass)
arg1 (NP-OBJ (NN 银行
法)))))
arg0 argM arg1
国会
最近
(congress)
银行法 (law)
ANNOTATION PROCEDURE


PTB II – Extract all sentences of a verb
Create Frame File for that verb Paul Kingsbury
(3400+ lemmas, 4700 framesets,120K predicates)


1st pass: Automatic tagging Joseph Rosenzweig
2nd pass: Double blind hand correction by verb
Inter-annotator agreement 84% (87% Arg#’s)


3rd pass: Adjudication Olga Babko-Malaya
4th pass: Train automatic semantic role labellers
Dan Gildea, Sameer Pradhan, Nianwen Xue, Szuting Yi,
….
CoNLL-04 shared task, 2004, 2005, ….
WORD SENSES IN PROPBANK

Orders to ignore word sense not feasible for 700+
verbs


Mary left the room
Mary left her daughter-in-law her pearls in her will
Frameset leave.01 "move away from":
Arg0: entity leaving
Arg1: place left
Frameset leave.02 "give":
Arg0: giver
Arg1: thing given
Arg2: beneficiary
How do these relate to traditional word senses in WordNet?
PROPBANK II – ENGLISH/CHINESE
(100K)
We still need relations between events and entities:
 Event ID’s with event coreference
 Selective sense tagging
Tagging nominalizations w/ WordNet sense
 Grouped WN senses - selected verbs and nouns


Nominal Coreference


not names
Clausal Discourse connectives – selected subset
Level of representation that reconciles many surface
differences between the languages
EVENT IDS – PARALLEL PROP II (1)

Aspectual verbs do not receive event IDs:

今年/this year 中国/China 继续/continue 发挥/play 其/it
在/at 支持/support 外商/foreign business 投资
/investment 企业/enterprise 方面/aspect 的/DE 主/main
渠道/channel 作用/role
“This year, the Bank of China will continue to play the
main role in supporting foreign-invested businesses.”
EVENT IDS – PARALLEL PROP II (2)

Nominalized verbs do:

He will probably be extradited to the US for trial.
done as part of sense-tagging
(all 7 WN senses for “trial” are events.)

随着/with 中国/China 经济/economy 的/DE 不断
/continued 发展/development…
“With the continued development of China’s
economy…”
The same events may be described by verbs in
English and nouns in Chinese, or vice versa.
Event IDs help to abstract away from POS tag
EVENT REFERENCE – PARALLEL PROP II

Pronouns (overt or covert) that refer to events:
[This] is gonna be a word of mouth kind of thing.
这些/these 成果/achivements 被/BEI 企业/enterprise 用
/apply (e15) 到/to 生产/production 上/on 点石成金/spin
gold from straw， *pro*-e15 大大/greatly 提高/improve 了
/le 中国/China 镍/nickel 工业/industry 的/DE 生产
/production 水平/level 。
“These achievements have been applied (e15) to production by
enterprises to spin gold from straw, which-e15 greatly improved
the production level of China’s nickel industry.”

Prerequisites:


pronoun classification
free trace annotation
CHINESE PB II: SENSE TAGGING

Much lower polysemy than English

Avg of 3.5 (Chinese) vs. 16.7 (English)
Dang, Chia, Chiou, Palmer, COLING-02


More than 2 Framesets
62/4865 (250K) Ch vs. 294/3635 (1M) English
Mapping Grouped English senses to Chinese
(English tagging - 93 verbs/168 nouns, 5000+
instances)
Selected 12 polysemous English words
(7 verbs/5 nouns)
 For 9 (6 verbs/3 nouns), grouped English senses map to unique
Chinese translation sets (synonyms)

MAPPING OF GROUPED SENSE TAGS
TO CHINESE
increase
提高 / ti2gao1
Collect, levy
募集 / mu4ji2
筹措 / chou2cuo4
筹... / chou2…
raise – translations by group
lift, elevate,
orient upwards
仰 / yang3
invoke, elicit, set off
提 / ti4
DISCOURSE CONNECTIVES:
THE PENN DISCOURSE TREEBANK

WSJ corpus (~1M words, ~2400 texts)
http://www.cis.upenn.edu/~pdtb
Miltsakaki, Prasad, Joshi and Webber, LREC-04, NAACL-04 Frontiers
Prasad, Miltsakaki, Joshi and Webber ACL-04 Discourse Annotation

Chinese: 10 explicit discourse connectives that include
subordination conjunctions, coordinate conjunctions, and
discourse adverbials.

Argument determination, sense disambiguation
[arg1 学校/school 不/not 教/teach 理财/finance management]， [conn 结果
/as a result] [arg2 报章/newspaper 上/on 的/DE 各/all 种/kind 专栏
/column 就/then 成为/become 信息/information 的/DE 主要/main 来源
/source]。
“The school does not teach finance management. As a result, the different
kinds of columns become the main source of information.”
MAPPING OF GROUPED SENSE TAGS
TO CHINESE
Zhejiang|浙江zhe4jiang1 will|将jiang1 raise|提高ti2gao1 the
level|水平shui3ping2 of|的de opening up|开放kai1fang4 to|对
dui4 the outside world|外wai4. （浙江将提高对外开放的水平。）
 I|我wo3 raised|仰yang3 my|我的wo3de head|头tou2 in
expectation|期望qi1wang4.（我仰头望去。）
 …, raising|筹措chou2cuo4 funds|资金zi1jin1 of|的de 15
billion|150亿yi1ban3wu3shi2yi4 yuan|元yuan2 (…筹措资金150
亿元。)
 The meeting|会议hui4yi4 passed|通过tong1guo4 the “decision
regarding motions”|议案yi4an4 raised|提ti4 by 32 NPC|人大
ren2da4 representatives|代表dai4biao3 （会议通过了32名人大代
表所提的议案。）

NOMBANK
•
•
Provides argument structure for 5000
common noun lemmas from the Penn
Treebank II corpus.
Borrows heavily from PropBank
where possible (for example for
nominalizations)
NOMBANK EXAMPLES

Verb-Related


Adjective Related


Powell’s/ARG0 meeting with Zhu Rongji/ARG1
The absence of patent lawyers/ARG1 in the court/ARG2
Nominals (16 classes)
Her/ARG1 husband/ARG0
 An Oct. 1/ARG2 date for the attack/ARG1

NOMBANK ANNOTATION EXAMPLE
 According
to [Rel_report.01 reports],
[Arg1 sea [Rel_trial.01 trials]
[Arg1 for [Arg1-CF_launch.01 a patrol boat]
developed by Kazakhstan]
are being conducted and the
[ArgM-MNR formal]
[Rel_launch.01 launch]
is planned for the
[[REL_beginning.01 beginning] [ARG1 of April this
year]].
OPINION ANNOTATION
I think people are happy because Chavez has fallen.
direct subjective
span: think
source: <writer, I>
attitude:
attitude
span: think
type: positive arguing
intensity: medium
target:
target
span: people are happy because
Chavez has fallen
direct subjective
span: are happy
source: <writer, I, People>
attitude:
attitude
span: are happy
type: pos sentiment
intensity: medium
target:
target
span: Chavez has fallen
inferred attitude
span: are happy because
Chavez has fallen
type: neg sentiment
intensity: medium
target:
target
span: Chavez
MOTIVATING EXAMPLE
“I think people are happy because Chavez has fallen.
But there’s also a feeling of uncertainty about how
the country’s obvious problems are going to be
solved,” said Ms. Ledesma.
7/27/2004
AAAI 2004
33
MOTIVATING EXAMPLE
medium strength
Though some of them did not conceal their criticisms
of Hugo Chavez, the member countries of the
Organization of American States condemned the coup
and recognized the legitimacy of the elected president.
high strength
low strength
PRIVATE STATES AND SUBJECTIVE
EXPRESSIONS
Private state: covering term for opinions, emotions,
sentiments, attitudes, speculations, etc. (Quirk et al., 1985)
Subjective Expressions: words and phrases that express
private states (Banfield, 1982)
“The US fears a spill-over,” said Xirao-Nima.
“The report is full of absurdities,” he complained.
CORPUS OF OPINION ANNOTATIONS
 Multi-perspective
(MPQA) Corpus



Question Answering
Sponsored by NRRC ARDA
Freely
Released November, 2003
http://nrrc.mitre.org/NRRC/publications.htmAvailable
 Detailed
expression-level annotations of
private states: strength
 See
Wilson and Wiebe (SIGdial 2003)
PENN DISCOURSE TREEBANK (PDTB)
•
•
Annotate discourse connectives and their arguments
Discourse connectives take clauses as their arguments
and express relations between clauses
–
•
i.e., relations between propositions, events, situations
Discourse connectives such as
- and, or, but, because, since, while, when, however,
instead, although, also, for example, then, so that,
insofar as, nonetheless
•
Subordinate conjunctions, Coordinate conjunctions,
Adverbial connectives, Implicit connectives
•
•
Because [Arg2 he was sick], [Arg1 John left early]
Since [Arg2 the store is closed], [Arg1 we’ll go home].
THE PROBLEM
Connective
Arg2
After adjusting for inflation, the Commerce Department said
spending didn’t change in September.
Arg1
After adjusting for inflation, the Commerce Department said
spending didn’t change in September.
Given a discourse connective, identify the heads of its two
arguments
IDENTIFYING ARGUMENTS IN PDTB

Task




Identify lexicalized relations in Penn Discourse TreeBank (PDTB)
Identify head-words of arguments
Don’t identify relation type or non-lexicalized relations
Approach

Rank Arg1 & Arg2 candidate arguments separately


Re-rank top N argument pairs



Apply MaxEnt statistical ranker
Model both argument candidates jointly
Re-ranking reduces error 5-11%
Main Results: 74% accuracy at identifying both arguments correctly
for a connective

Using gold-standard TreeBank parses
PDTB EXAMPLES
Coordinator
Choose 203 business executives, including, perhaps, someone
from your own staff, and put them out on the streets, to be
deprived for one month of their homes, families and income.
Subordinator
Drug makers shouldn’t be able to duck liability because people
couldn’t identify precisely which identical drug was used.
France’s second-largest government-owned insurance company,
Assurances Generales de France, has been building its own
Naviation Mixte stake, currently thought to be between 8% and 10%.
Analysts said they don’t think it is contemplating a takeover,
however, and its officials couldn’t be reached.
Discourse Adverbial
MOTIVATION FOR TIME AND EVENT MARKUP
Natural language is filled with references to past
and future events, as well as planned activities
and goals;
 Without a robust ability to identify and
temporally situate events of interest from
language, the real importance of the information
can be missed;
 A Robust Annotation standard can help leverage
this information from natural language text.

TEMPORAL AWARENESS IN REAL TEXT
The bridge collapsed during the storm but after
traffic was rerouted to the Bay Bridge.
 President Roosevelt died in April 1945 before
 the war ended. (event happened)
 he dropped the bomb. (event didn’t happen)
 The CEO plans to retire next month.
 Last week Bill was running the marathon when
he twisted his ankle. Someone had tripped him.
He fell and didn't finish the race.

CURRENT TIME ANALYSIS TECHNOLOGY

Document Time Linking


Find the document creation time and link that to all
events in the text;
Local Time Stamping

find an event and a “local temporal expression”, and
link it to that time;
DOCUMENT TIME STAMPING
April 25, 2010
 President Obama paid tribute Sunday to 29
workers killed in an explosion at a West Virginia
coal mine earlier this month, saying they died "in
pursuit of the American dream." The blast at the
Upper Big Branch Mine was the worst U.S. mine
disaster in nearly 40 years.Obama ordered a
review earlier this month and blamed mine
officials for lax regulation.
DOCUMENT TIME STAMPING:
April 25, 2010
 President Obama paid tribute Sunday to 29
workers killed in an explosion at a West Virginia
coal mine earlier this month, saying they died "in
pursuit of the American dream." The blast at the
Upper Big Branch Mine was the worst U.S. mine
disaster in nearly 40 years.Obama ordered a
review earlier this month and blamed mine
officials for lax regulation.
DOCUMENT TIME STAMPING: FOR REAL
April 25, 2010
 President Obama paid tribute Sunday to 29
workers killed in an explosion at a West Virginia
coal mine earlier this month, saying they died "in
pursuit of the American dream." The blast at the
Upper Big Branch Mine was the worst U.S. mine
disaster in nearly 40 years.Obama ordered a
review earlier this month and blamed mine
officials for lax regulation.
TIME STAMPING: THE GOOD, BAD, …
✓
 ☺Set up a meeting on Tuesday with EMC.
✓
 ☺Franklin arrives tomorrow from London.
✗
 ☹ Franklin arrives on the afternoon flight from
London tomorrow.
✗
 ☹ ☹ Most people drive today while talking on the
phone.
TEMPORAL AWARENESS CHALLENGE
Identification of all important events in a text
 Actual temporal ordering and time anchoring of
these events to temporal expressions.

ISO-TIMEML ENABLES TEMPORAL PARSING
A new generation of language analysis tools that
are able to temporally organize events in terms of
their ordering and time of occurrence
 These tools can be integrated with visualization,
summarization, question answering, and link
analysis systems to help analyze large event-rich
information spaces.

ISO-TIMEML PROVIDES ELEMENTS TO:
Find all events and times in newswire text
 Link events to the document time and to local
times
 Order event relative to other events
 Ensure consistency of the the temporal relations

TEMPORAL PARSING TECHNOLOGIES
Build temporal representations of events in
document collections;
 Track people and the events they participated in;
 Answer questions about when events occur.

APPLICATIONS IMPACTED
Health Care, Bioinformatics, Insurance
 Object Tracking
 Search and Categorization
 Trend Analysis and Prediction

TEMPORAL AWARENESS

Take your 1st dose of levaquin in the morning
before any food, 2nd dose before sleep.
dose-1
dose-2
…
eat
sleep
TEMPORAL AWARENESS

No food or drink after midnight before surgery,
until you are in recovery.
12:00 am
¬food &¬drink
surgery
food & drink
recovery
DIFFERENT NOTIONS OF EVENTS

Topic: “well-defined subject” for searching


Template: structure with slots for participant
named entities


document- or collection-level
document-level
Mention: linguistic expression that expresses an
underlying event

phrase-level (verb/noun)
EVENTS AS TEMPLATES
Wall Street Journal, 06/15/88 MAXICARE HEALTH PLANS INC and
UNIVERSAL HEALTH SERVICES INC have dissolved a joint venture
which provided health services.
Systems can fill such
templates
at ~ 60% accuracy from
news (MUC evals)
ACE EVENT TYPES
ACE Event Roles
EVENTS IN TIMEML

Mention: linguistic expression that expresses an underlying
event


Since they correspond to surface mentions, easier to annotate
and recognize


Accuracy is > 88% (ARDA AQUAINT (TARSQI))
Like templates


Phrase-level (verb/noun)
they are linked to times
Unlike templates

the times are resolved




87% accuracy in time resolution (TERN evals: timex2.mitre.org)
the links involve temporal relations
the events are temporally ordered
the links also involve other logical relations (subordinating and
aspectual)
FEATURES OF ISO-TIMEML
Identifies temporal expressions;




Dates, times
Temporal Functions: three years ago
Anchors to events and other temporal expressions: three
years after the Gulf War
Identifies signals determining interpretation of temporal
expressions;



Temporal Prepositions: for, during, on, at;
Temporal Connectives: before, after, while.
Identifies event expressions;




tensed verbs; has left, was captured, will resign;
stative adjectives; sunken, stalled, on board;
event nominals; merger, Military Operation, Gulf War;
Creates dependencies between events and times:




Anchoring; John left on Monday.
Orderings; The party happened after midnight.
Embedding; John said Mary left.
ISO-TIMEML TAGS






<TIMEX3>
 Used to mark up explicit temporal expressions, such as times, dates, durations,
etc. It is modeled on the TIDES TIMEX2 tag.
<EVENT>
 Used to annotate those elements in a text that mark the semantic events
described by it. Events are typically verbs, although event nominals, such as
"crash" in "...killed by the crash", are also annotated as events.
<TLINK>
 One of the three TimeML link tags. Link tags encode the various relations that
exist between the temporal elements of a document. A TLINK is a temporal link.
It represents the relation between two temporal elements.
<SLINK>
 A subordination link that is used for contexts involving modality, evidentials,
and factives. An SLINK is used in cases where an event instance subordinates
another event instance type.
<ALINK>
 An aspectual link, it indicates an aspectual connection between two events. In
some ways, it is like a cross between TLINK and SLINK in that it indicates both
a relation between two temporal elements, as well as aspectual subordination.
<ARGLINK>
 A link establishing a relationship between an event and each of its participants.
ARGLINK uses the entity ID and binds it to the event.
TIMEML:
ANNOTATION OF TEMPORAL ENTITIES

Temporal expressions: <TIMEX3>
Times: 3 o’clock, mid-morning.
 Dates:

Fully Specified: June 11, 1989; Summer, 2002.
 Underspecified: Monday, next month, two days ago.
 Durations: three months, two years.
 Sets: every month, each Tuesday.


Event expressions: <EVENT>
Expressions denoting events that participate in the narrative of a
given document, and which can be temporally ordered.
 Event-related grammatical features:
 Tense: past, present, past, etc.
 Aspect: progressive, perfective, perfective-progressive.
 Polarity: positive, negative.
 Modality: would, could, may, etc.
 Class: occurrence, state, aspectual, intensional, etc.

TIMEML:
ANNOTATION OF TEMPORAL RELATIONS

Temporal links: <TLINK>
Anchoring of Events to Times
 Ordering of Events
 13 temporal relations (based on Allen’s relations), among which:

•
•
•
•
•
•
•

Simultaneous
Before (e.g., For most of the murders, suspects have already been arrested)
After
Immediately before (e.g., All passengers died when the plane crashed into
the mountain)
Immediately after.
Including (e.g., John arrived in Boston last Thursday)
Etc.
Aspectual links: <ALINK> Phases of an event




Initiation: John started to read.
Culmination: John finished assembling the table.
Termination: John stopped talking.
Continuation: John kept talking.
TIMEML:
ANNOTATION OF TEMPORAL RELATIONS

Subordinating links <SLINK>


Events that syntactically subordinate other events
Providing information about the factual nature of the embedded
event:
Factive: The embedded event is presupposed or entailed as factual.
John forgot that he was in Boston last year.
Mary regrets that she didn't marry John.
Counterfactive: The embedded event is presupposed as non-factual:
John forgot to buy some wine.
John prevented the divorce.
Evidential: Introduced by REPORTING or PERCEPTION:
John said he bought some wine.
Mary saw John carrying only beer.
Negative evidential: Introduced by REPORTING events conveying negative
polarity:
John denied he bought only beer.
Modal: Expressing different degrees of uncertainty, possibility, thought, etc.
Analysts also suspect suppliers have fallen victim to their own success.
EXAMPLE: TEMPORAL EXPRESSIONS
AP-NR-08-15-90 1337EDT
Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi
border, today sought peace on another front by promising to
withdraw from Iranian territory and release soldiers captured
during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in
Washington seeking to mediate the Persian Gulf crisis. President Bush on
Tuesday said the United States may extend its naval quarantine to
Jordan's
Red Sea port of Aqaba to shut off Iraq's last unhindered trade route.
In another mediation effort, the Soviet Union said today it had
sent an envoy to the Middle East on a series of stops to include
Baghdad. Soviet officials also said Soviet women, children and
invalids would be allowed to leave Iraq.
EXAMPLE: EVENTS
AP-NR-08-15-90 1337EDT
Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi
border, today sought peace on another front by promising to
withdraw from Iranian territory and release soldiers captured
during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in
Washington seeking to mediate the Persian Gulf crisis. President Bush
on
Tuesday said the United States may extend its naval quarantine to
Jordan's
Red Sea port of Aqaba to shut off Iraq's last unhindered trade route.
In another mediation effort, the Soviet Union said today it had
sent an envoy to the Middle East on a series of stops to include
Baghdad. Soviet officials also said Soviet women, children and
invalids would be allowed to leave Iraq.
EXAMPLE:
TLINKS, ANCHORING EVENT TO TIMEX
AP-NR-08-15-90 1337EDT
Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi
border, today sought peace on another front by promising to
withdraw from Iranian territory and release soldiers captured
during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in
Washington seeking to mediate the Persian Gulf crisis. President Bush on
Tuesday said the United States may extend its naval quarantine to
Jordan's
Red Sea port of Aqaba to shut off Iraq's last unhindered trade route.
In another mediation effort, the Soviet Union said today it had
sent an envoy to the Middle East on a series of stops to include
Baghdad. Soviet officials also said Soviet women, children and
invalids would be allowed to leave Iraq.
EXAMPLE:
TLINKS, ORDERING EVENTS
AP-NR-08-15-90 1337EDT
Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi
border, today sought peace on another front by promising to
withdraw from Iranian territory and release soldiers captured
during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in
Washington seeking to mediate the Persian Gulf crisis. President Bush on
Tuesday said the United States may extend its naval quarantine to Jordan's
Red Sea port of Aqaba to shut off Iraq's last unhindered trade route.
Past < Tuesday
< Today
< Indef Future
___________________________________________________________________________
war(I,I)
say(Bush,S)
captured(sold)
arrive(H,DC)
seek(Saddam,peace)
withdraw(Saddam)
release(Saddam,soldiers)
extend(US,quarantine)
shut_off(US,trade_route)
EXAMPLE:
ALINKS, PHASES OF EVENTS
President Bush today denounced Saddam's ``ruinous policies of war,'' and
said the United States is ``striking a blow for the principle that might does
not make right.''
In a speech delivered at the Pentagon, Bush seemed to suggest
that American forces could be in the gulf region for some time.
``No one should doubt our staying power or determination,'' he said.
The U.S. military buildup in Saudi Arabia continued at fever pace, with Syrian
troops now part of a multinational force camped out in the desert to guard the
Saudi kingdom from any new thrust by Iraq.
In a letter to President Hashemi Rafsanjani of Iran, read by a broadcaster over
Baghdad radio, Saddam said he will begin withdrawing troops from Iranian
territory a week from tomorrow and release Iranian prisoners of war.
EXAMPLE:
SLINKS, FACTUAL NATURE OF EVENTS
AP-NR-08-15-90 1337EDT
Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi
border, today sought peace on another front by promising to withdraw from
Iranian
territory and release soldiers captured
during the Iran-Iraq war. Also today, King Hussein of Jordan arrived in
Washington seeking to mediate the Persian Gulf crisis. President Bush on
Tuesday said the United States may extend its naval quarantine to Jordan's
Red Sea port of Aqaba to shut off Iraq's last trade route.
In another mediation effort, the Soviet Union said today it had
sent an envoy to the Middle East on a series of stops to include
Baghdad. Soviet officials also said Soviet women, children and
invalids would be allowed to leave Iraq.
EXAMPLE:
SLINKS, REPORTED SPEECH
AP-NR-08-15-90 1337EDT
Iraq's Saddam Hussein, facing U.S. and Arab troops at the Saudi
border, today sought peace on another front by promising to withdraw from
Iranian territory and release soldiers captured during the Iran-Iraq war.
Also today, King Hussein of Jordan arrived in Washington seeking to mediate
the Persian Gulf crisis. President Bush on Tuesday said the United States may
extend its naval quarantine to Jordan's Red Sea port of Aqaba to shut off
Iraq's last unhindered trade route.
In another mediation effort, the Soviet Union said today it had sent an envoy
to the Middle East on a series of stops to include Baghdad. Soviet officials also
said Soviet women, children and invalids would be allowed to leave Iraq.
MODELING EVENTS RELATIVE TO TIME:

ORDER:


MEASURE:


The position of the interval relative to others :
The size of the interval;
QUANTITY:

The number of intervals.
ORDER


John taught on Tuesday.
John taught before Mary arrived.
MEASURE
John taught for three hours on Tuesday.
 Introduce MLINK:

<EVENT id="e1" pred="TEACH"/>
<TIMEX3 id="t2" type="DURATION" value="P3H"/>
<MLINK eventID="e1" relatedToTime="t2" />
QUANTITY

John taught every Monday in November.
SUMMARY OF ISO-TIMEML
Enhances our ability to annotate temporal and
event expressions in multiple languages
 Has an explicit semantics associated with the
abstract syntactic specification
 Is already being tested against SemEval
standards competetions.
 Integrated into the TTK (TARSQI Toolkit) at
Brandeis

THANK YOU!
timeml.org