L EXICAL S EMANTICS AND S EMANTIC A NNOTATION CLSW 2011 NTU, Taipei May 4, 2011 James Pustejovsky...

LEXICAL SEMANTICS AND SEMANTIC ANNOTATION

CLSW 2011

NTU, Taipei

May 4, 2011

James Pustejovsky(with additional slides from: Martha Palmer, Nianwen Xue,Olga Babko-Malaya, Ben Snyder)

EXAMPLES OF SEMANTIC ANNOTATIONS Predicators and their named arguments

[The man]agent painted [the wall]patient. Anaphors and their antecedents

[The protein] inhibits growth in yeast. [It] blocks production…

Acronyms and their long forms [Platelet-derived growth factor] (known as [pdgf])

impacts … Semantic Typing of entities

[The man]human fired [the gun]firearm

LINGUISTIC PHENOMENA Syntactic Structure

Describes grammatical arrangements of words into hierarchical structure

Predicate Argument Structure Who did what to whom: Subject, object, predicate

Temporal 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 Question answering, summarization, inference,

reading, … Annotation schemata should focus on a

single coherent theme: 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 ASSUMPTIONAnnotation scheme:

assumes a given feature setFeature set:

encodes specific structural descriptions and properties of the input data

Structural 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

Proposition: meet(Powell, Zhu Rongji)Powell met with Zhu Rongji

Powell and Zhu Rongji met

Powell and Zhu Rongji had a meeting

. . .

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

Proposition: meet(Powell, Zhu Rongji)Powell met with Zhu Rongji

Powell and Zhu Rongji met

Powell and Zhu Rongji had a meeting

. . .

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?

http://www.cis.upenn.edu/~ace

http://www.cis.upenn.edu/~chinese/cpb

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: GIVEW/ 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) (pass)

arg0 argM arg1

国会最近银行法 (law) (congress)

(IP (NP-SBJ arg0 (NN 国会 )) (VP argM (ADVP (ADV 最近 )) (VP f2 (VV 通过 ) (AS 了 ) arg1 (NP-OBJ (NN 银行

法 )))))

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 leavingArg1: place left

Frameset leave.02 "give":Arg0: giver Arg1: thing givenArg2: 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 senseGrouped 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 TAGSTO CHINESE

increase 提高 / ti2gao1

lift, elevate, orient upwards

仰 / yang3

Collect, levy募集 / mu4ji2筹措 / chou2cuo4筹 ... / chou2…

invoke, elicit, set off 提 / ti4

raise – translations by group

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.”

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 Powell’s/ARG0 meeting with Zhu Rongji/ARG1

Adjective Related 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 ANNOTATIONI think people are happy because Chavez has

fallen.direct subjective span: are happy source: <writer, I, People> attitude:

inferred attitude span: are happy because Chavez has fallen type: neg sentiment intensity: medium target:

target span: Chavez has fallen

target span: Chavez

attitude span: are happy type: pos sentiment intensity: medium target:

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

AAAI 2004 337/27/2004

“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.

MOTIVATING EXAMPLE

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.

MOTIVATING EXAMPLE

low strength

high strength

medium 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 ANNOTATIONSMulti-perspective Question Answering

(MPQA) CorpusSponsored by NRRC ARDA Released November, 2003http://nrrc.mitre.org/NRRC/publications.ht

m

Detailed expression-level annotations of private states: strength

See Wilson and Wiebe (SIGdial 2003)

FreelyAvailable

http://nrrc.mitre.org/NRRC/publications.htm

http://nrrc.mitre.org/NRRC/publications.htm

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

After adjusting for inflation, the Commerce Department saidspending didn’t change in September.

After adjusting for inflation, the Commerce Department saidspending didn’t change in September.

Arg2Connective

Arg1

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

Apply MaxEnt statistical ranker Re-rank top N argument pairs

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

Choose 203 business executives, including, perhaps, someonefrom your own staff, and put them out on the streets, to be deprived for one month of their homes, families and income.

Drug makers shouldn’t be able to duck liability because peoplecouldn’t identify precisely which identical drug was used.

France’s second-largest government-owned insurance company,Assurances Generales de France, has been building its ownNaviation 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.

Coordinator

Subordinator

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 beforethe 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:



DOCUMENT TIME STAMPING: FOR REAL



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

eat

…

dose-2

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

document- or collection-level Template: structure with slots for participant

named entities document-level

Mention: linguistic expression that expresses an underlying event phrase-level (verb/noun)

EVENTS AS TEMPLATESWall 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 Phrase-level (verb/noun)

Since they correspond to surface mentions, easier to annotate and recognize Accuracy is > 88% (ARDA AQUAINT (TARSQI))

Like templates 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.

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.

TIMEML: ANNOTATION OF TEMPORAL RELATIONS

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: TEMPORAL EXPRESSIONS

AP-NR-08-15-90 1337EDT












EXAMPLE: EVENTS

AP-NR-08-15-90 1337EDT












EXAMPLE: TLINKS, ANCHORING EVENT TO TIMEX

AP-NR-08-15-90 1337EDT








Past < Tuesday < Today < Indef Future___________________________________________________________________________war(I,I) say(Bush,S) arrive(H,DC) withdraw(Saddam)captured(sold) seek(Saddam,peace)release(Saddam,soldiers)

extend(US,quarantine)

shut_off(US,trade_route)

EXAMPLE: TLINKS, ORDERING 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 doesnot make right.'' In a speech delivered at the Pentagon, Bush seemed to suggestthat 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: ALINKS, PHASES OF EVENTS

AP-NR-08-15-90 1337EDT


border, today sought peace on another front by promising to withdraw from Iranian

territory and release soldiers captured




Red Sea port of Aqaba to shut off Iraq's last trade route.





EXAMPLE: SLINKS, FACTUAL NATURE OF EVENTS

AP-NR-08-15-90 1337EDT


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: SLINKS, REPORTED SPEECH

MODELING EVENTS RELATIVE TO TIME:

ORDER: The position of the interval relative to others :

MEASURE: 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

L EXICAL S EMANTICS AND S EMANTIC A NNOTATION CLSW 2011 NTU, Taipei May 4, 2011 James Pustejovsky...

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