Practical Applications of Temporal and Event Reasoning

1
Practical Applications of Temporal and Event
Reasoning

• James Pustejovsky, Brandeis
• Graham Katz, Osnabrück
• Rob Gaizauskas, Sheffield
• ESSLLI 2003
• Vienna, Austria
• August 25-29, 2003

2
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
• TimeBank Corpus
• TANGO
• Automatic Link Construction
• Friday-
• Outstanding Problems

3
Thursday Topics

• Automatic Identification of Expressions
• TimeBank Corpus
• TANGO
• Automatic Link Construction
• Developmental Complexity Models of Narratives

4
Annotating the Corpus

• Distinguishing features of TimeML are
• It builds on TIMEX2 (Ferro et al., 2001), but
  introduces new features such as temporal
  functions to allow intensionally specified
  expressions like three years ago.
• It identifies signals determining interpretation
  of temporal expressions, such as temporal
  prepositions (for, during) and temporal
  connectives (before, after).
• It identifies a wide range of classes of event
  expressions, such as tensed verbs (has left),
  stative adjectives (sunken), and event nominals
  (merger).
• It creates dependencies between events and times
  or other events, such as anchoring (John left on
  Monday), ordering (The party happened after
  midnight), and embedding (John said Mary left).

5
The Conceptual and Linguistic Basis

• TimeML presupposes the following temporal
  entities and relations.
• Events are taken to be situations that occur or
  happen, punctual or lasting for a period of time.
  They are generally expressed by means of tensed
  or untensed verbs, nominalisations, adjectives,
  predicative clauses, or prepositional phrases.
• Times may be either points, intervals, or
  durations. They may be referred to by fully
  specified or underspecified temporal expressions,
  or intensionally specified expressions.
• Relations can hold between events and events and
  times. They can be temporal, subordinate, or
  aspectual relations.

6
Annotating Events

• Events are marked up by annotating a
  representative of the event expression, usually
  the head of the verb phrase.
• The attributes of events are a unique identifier,
  the event class, tense, and aspect.
• Fully annotated example
• All 75 passengers
• aspect"NONE" died
• See full TimeML spec for handling of events
  conveyed by nominalisations or stative adjectives.

7
Annotating Times

• Annotation of times designed to be as compatible
  with TIMEX2 time expression annotation guidelines
  as possible.
• Fully annotated example for a straightforward
  time expression
• temporalFunction"false" July 1966
• Additional attributes are used to, e.g. anchor
  relative time expressions and supply functions
  for computing absolute time values (last week).

8
Annotating Signals

• The SIGNAL tag is used to annotate sections of
  text, typically function words, that indicate how
  temporal objects are to be related to each other.
  
• Also used to mark polarity indicators such as
  not, no, none, etc., as well as indicators of
  temporal quantification such as twice, three
  times, and so forth.
• Signals have only one attribute, a unique
  identifier.
• Fully annotated example
• Two days the
  attack

9
Annotating Relations (1)

• To annotate the different types of relations that
  can hold between events and events and times, the
  LINK tag has been introduced.
• There are three types of LINKs TLINKs, SLINKs,
  and ALINKs, each of which has temporal
  implications.
• A TLINK or Temporal Link represents the temporal
  relationship holding between events or between an
  event and a time.
• It establishes a link between the involved
  entities making explicit whether their
  relationship is before, after, includes,
  is_included, holds, simultaneous, immediately
  after, immediately before, identity, begins,
  ends, begun by, ended by.

10
Annotating Relations (2)

• An SLINK or Subordination Link is used for
  contexts introducing relations between two
  events, or an event and a signal.
• SLINKs are of one of the following sorts Modal,
  Factive, Counter-factive, Evidential, Negative
  evidential, Negative.
• An ALINK or Aspectual Link represents the
  relationship between an aspectual event and its
  argument event.
• The aspectual relations encoded are initiation,
  culmination, termination, continuation.

11
Annotating Relations (2)

• Annotated examples
• TLINK John taught on Monday
• signalID"4" relType"IS_INCLUDED"/
• SLINK John said he taught
• relType"EVIDENTIAL"/
• ALINK John started to read
• relType"INITIATES"/

12
The Corpus Text Sources

• The 300 texts in the TIMEBANK corpus were chosen
  to cover a wide variety of media sources from the
  news domain
• DUC (TIPSTER) texts from the Document
  Understanding Conference corpus cover areas like
  biography, single and multiple events (for
  example dealing with news about earthquakes and
  Iraq). This covers 12 of the corpus
• Texts from the Automatic Content Extraction (ACE)
  program come from transcribed broadcast news
  (ABC, CNN, PRI, VOA) and newswire (AP, NYT).
  These comprise 17 and 16 of the corpus,
  respectively.
• Propbank (Treebank2) texts are Wall Street
  Journal newswire texts, making up 55 of the
  corpus.

13
The Annotation Effort

• The annotation of each document involves
• an automatic pre-processing step in which some of
  the events and temporal, modal and negative
  signals are tagged
• a human annotation step which
• checks the output of the pre-processing step
• introduces other signals and events, time
  expressions, and the appropriate links among
  them.
• The average time to annotate a document of 500
  words by a trained annotator is 1 hour.
• The annotators came from a variety of
  backgrounds.
• 70 of the corpus annotated by TimeML developers
• 30 annotated by students from Brandeis
  University.

14
The Annotation Tool (1)

• To help the annotators with the annotation
  effort, a modified version of the Alembic
  Workbench (Vilain and Day 1996) was developed.
• When a text is loaded into the tool
• the text is shown in one window with the results
  of the pre-processing shown via coloured tags.
  These tags can be edited or deleted, and new tags
  can be introduced.
• links are shown in a second window
• These links can be created by selecting tags in
  the text window and inserting these into the link
  window.

15
The Annotation Tool (2)
16
The Annotation Tool (3)
17
Key
EVENT TIMEX STATE
18
OSMACH, Cambodia (AP) - The top commander of a
Cambodian resistance force said Thursday he has
sent a team to recover the remains of a British
mine removal expert kidnapped and presumed killed
by Khmer Rouge guerrillas almost two years ago.
February 19, 1998
irrealis
before
before
said
sent
kidnapped
killed
recover
eventArg
Is_included
Thursday
Cambodian
Durationalmost two years
Is_included
relTypeafter
Signalago
British
DCT
Within-sentence annotation
presumed
time
19
Gen. Nhek Bunchhay, a loyalist of ousted
Cambodian Prime Minister Prince Norodom
Ranariddh, said in an interview with The
Associated Press at his hilltop headquarters that
he hopes to recover the remains of Christopher
Howes within the next two weeks.
irrealis
before
recover
hopes
ousted
Signalwithin
Is_included
the next two weeks
said
loyalist
Is_included
before
Cambodian
DCT
Within-sentence annotation
20
Howes had been working for the Britain-based
Mines Advisory Group when he was abducted with
his Cambodian interpreter Houn Hourth in March
1994. There were many conflicting accounts of
his fate.
working
Signalwhen
ibefore
abducted
Is_included
Signalin
March 1994
DCT
Within-sentence annotation
21
Howes team was clearing mines 17 kilometers (10
miles) from Angkor Wat, the fabled 11th Century
temple that is Cambodias main tourist
attraction, when it was attacked.
clearing
Signalwhen
ibefore
attacked
DCT
Within-sentence annotation
22
before
kidnapped
killed
irrealis
hopes
before
ousted
irrealis
working
ID
before
said
sent
recover
clearing
recover
ibefore
Is_included
said
abducted
Is_included
Is_included
ibefore
Thursday
Is_included
March 1994
Is_included
Is_included
the next two weeks
after
attacked
before
DCT
Within-document annotation (four sentences)
23
TANGO Demo

• Performing link analysis on a text

24
Closure lessons from TANGO

• Discovery aspects less important
• The spatial metaphor of TANGO guides the
  annotator to an event graph that requires less
  user prompting in order to get to a complete
  annotation.
• Closure makes a consistent and complete
  annotation possible
• Closure is still needed to infer implicit
  relations and to have prior choices of links
  restrict the relation type of other links.

25
Domains and Data Sets

• Document Collection (300)
• ACE
• DUC
• PropBank (WSJ)
• Query Corpus Collection
• Excite query logs
• MITRE Corpus
• TREC8/9/10
• Queries from TIMEBANK

26
Corpus Statistics (1)

• The statistics collected so far give
• the proportion of tagged text in the corpus
• the distribution of
• event classes
• TIMEX3 types
• LINK types
• Information like this gives a useful starting
  point when analysing the mechanisms used to
  convey temporal information.
• For example, 62 of links were TLINKs, indicating
  the importance of this link type.
• Further analysis of the TLINK will reveal the
  proportion of explicitly expressed temporal
  relations (i.e. a signal is used) to implicitly
  expressed temporal relations (no signal is used).

27
Corpus Statistics (2)

• For example, here is the distribution of tag
  types

28
The Question Corpus

• TimeML aims to contribute to Question Answering
  (QA) temporal question answering in
  particular.
• Temporal questions can be broadly classified into
  two categories
• Questions that ask for a temporal expression as
  an answer, like
• When was Clinton president of the United
  States?
• When was Lord of the Rings The Two Towers
  released?
• We call this type explicit.
• Questions that either use temporal expression to
  ask for a non-temporal answer or that ask about
  the relations holding between events.
• Who was president of the United States in 1990?
• Did world steel output increase during the 1990s?
  
• We call this type of temporal question implicit.

29
The Question Corpus (2)

• To evaluate the usefulness of TimeML for
  (temporal) QA, a question corpus of 50 questions
  has been created.
• This corpus was annotated according to a
  specially developed annotation scheme. This
  scheme allows features such as
• the type of the expected answer
• the volatility of the answer (i.e. how often it
  changes)
• to be annotated.
• The questions contained in the corpus cover both
  types mentioned above. Examples of questions in
  the corpus are 
• When did the war between Iran and Iraq end?
• When did John Sununu travel to a fundraiser for
  John Ashcroft?
• How many Tutsis were killed by Hutus in Rwanda in
  1994?
• Who was Secretary of Defense during the Gulf War?

30
Conclusion

• There has as yet been no time to analyse the
  corpus
• the statistics collected so far do not represent
  such an analysis, but only a very preliminary
  scoping.
• We anticipate that the corpus will allow a new
  range of explorations both theoretical and
  practical. For example
• Theoretical can study to what extent temporal
  ordering of events is conveyed
• explicitly through signals, such as temporal
  subordinating conjunctions, versus
• implicitly through the lexical semantics of the
  verbs or nominalizations expressing the events.
• Practical can train and evaluate algorithms to
  determine event ordering and time-stamping, and
  explore their utility in QA.

31
Tempex

• Wilson and Mani (2002) MITRE
• Timex2 parsing
• Direct Interpretation to ISO value

32
What is TempEx?

• Perl module that implements the TIDES Temporal
• Annotation Guidelines
• Handles many formats
• - Feb. 10, Feb. 10th, February Tenth
• Some parts of standard not fully implemented
• - Embedded Expressions Two weeks ago tomorrow
• - Unknown Components June 10 (VAL XXXX0610)
• Some very small extensions
• - Easter gets an ALT_VAL

33
Sample OutputPOS Tags removed
I got up MOD"EARLY"early this morning. I ate
lunch an
hour and a half ago. In TYPE"DATE" VAL"FUTURE_REF"the future,
I will know better. I went to Hong Kong
the week of
October third. I went to Hong Kong
the third week
of October. Reference Date
02/16/2001 133700
34
Performance
Interannotator agreement TIMEX VAL MOD Human
x Human 0.789 0.889 0.871 TempEx x
Human 0.624 0.705 0.301 Speed -
0.5Megabyte/Minute Demo Tempex
35
TIMEX3 Parser Objects (T3PO)

• Automatic TimeML Markup
• Extends TIDES TIMEX2 annotation
• Broader Coverage of temporal expressions
• Larger lexicon of temporal triggers
• Delays Computation of Temporal Math
• Annotation with Temporal Functions
• Import Hobbs Semantic Web Temporal System
• Distinct Cascaded Processes
• TIMEX3 and signal recognizer
• Event Predicate recognizer
• LINK creation transducer.

36
T3PO Overview

• Preprocessing
• POS, Shallow Parsing
• Three Finite State modules
• Temporal Expressions
• Events
• Signals
• Links
• Discourse Information

37
Temporal Expressions

• Extension to Timex2
• Coverage
• Absolute ISO Values
• Signals
• Functional Representation
• Anchor Resolution
• Suite of Temporal Functions

38
Event Recognition

• In Verbal uses VG chunks
• Encodes Tense and Aspect information
• Nominal Events using
• Morphological information
• POS ambiguity
• Signals
• Semantic Information

39
Link Recognition

• Event -Timex Links
• Use of heuristics.
• Extra-sentential (Event-DCT Links)
• Event-Event Links
• Intrasentential
• SLINKS (evidential)
• SLINKS (infinitivals)
• Extrasentential

40
Preliminary Tests Estimation(6 documents with
human annotated version)
41
Mani et al. (2003)

• A variety of theories have been proposed as to
  the roles of semantic and pragmatic knowledge in
  event ordering
• Very little prior work on corpus-based methods
  for event ordering
• They carried out a pilot experiment with 8
  subjects who provided event-ordering judgments
  for 280 clause pairs. Results revealed that
• A. Narrative convention applied only 47 of the
  time in ordering events in 131 pairs of
  successive past-tense clauses
• B. 75 of clauses lack explicit time expressions
  
• Suggests that anchoring events only to explicit
  times wouldnt be sufficient

42
Motivation

• Question Answering from News
• When do particular events occur
• When did the war between Iran and Iraq end?
• Which events occur in a temporal relation to a
  given event
• What is the largest U.S. military operation since
  Vietnam?
• Multi-Document News Summarization
• Event chronologies (e.g., timelines) are used
  widely in everyday news
• Need to know when events occur, to avoid
  inappropriate merging of distinct events

43
Problem Characteristics

• In news, events arent usually described in the
  (narrative) order in which they occur
• Temporal structure dictated by perceived news
  value
• Latest news usually presented first
• News sometimes expresses multiple viewpoints,
  with commentaries, eyewitness recapitulations,
  etc.,
• Temporal ordering appears to involve a variety of
  knowledge sources
• Tense aspect
• Max entered the room. Mary stood up/was seated on
  the desk.
• Temporal adverbials
• Simpson made the call at 3. Later, he was spotted
  driving towards Westwood.
• Rhetorical relations and World Knowledge
• Narration Max stood up. John greeted him.
• Cause/Explanation Max fell. John pushed him.
• Background Boutros-Ghali Sunday opened a meeting
  in Nairobi of ....He arrived in Nairobi from
  South Africa.

44
Event Ordering and Reference Time

• Reference Time (Reichenbach 47) provides
  temporal anchoring for events
• uI hadr mailede the letter (when John came and
  told me the news).
• Past Perfect e
• Movement of Reference Time depends on tense,
  aspect, rhetorical relations, world knowledge,
  etc.
• u1John pickedr1,e1 up the phone (at 3 pm)
• u2He hadr2 tolde2 Mary he would call her
• Assuming r2 e1 (stative), e2
• (Hwang Schubert 92)

u1,u2
r13pm e1
r2
e2
45
Two Clause Interpretation

• Past2Past
• Max stood up. John greeted him
• AFTER relation
• Max fell. John pushed him.
• BEFORE relation
• Max entered the room. Mary was seated behind the
  desk.
• Equal (SIMULTANEOUS or INCLUDE) relation
• Past2PastPerfect
• Max entered the room. He had drunk a lot of wine
• BEFORE relation
• PastPerfect2Past
• Max had been in Boston. He arrived late.
• AFTER relation

46
Factors That Determine Relation

• Aspect
• Progressive or not
• Order
• The iconic order in text
• Tempex
• The existence of a temporal expression
• Tense
• Past vs. Past Perfect
• Meaning
• Lexical or constructional semantics of the
  sentence.

47
Event Ordering Human Experiment
Foreign Minister John Chang confirmed to
reporters that Lien, during a Sunday stopover in
New York, had made a detour to a third country''
with which Taiwan has no diplomatic ties and
would not return to Taipei as scheduled on
Monday. But Chang and other Taiwan spokesmen
pointedly refused to confirm local media reports
that Lien was in Europe, much less to confirm
that he had flown to France. Since a civil war
divided them in 1949, China has regarded Taiwan
as a rebel province ineligible for sovereign
foreign relations. In mid-1995, a furious
Beijing downgraded ties with Washington and froze
talks with Taiwan after President Lee Teng-hui
made a private visit to the United States.
meets and during/includes not used
48
Results on Human Event Ordering
5 subjects X 48 exs 240 exs
131
109
While shallow features can be leveraged in
ordering, meaning and commonsense knowledge also
play a crucial role
Narrative convention applies in less than half of
the Past to Past cases and less than two-thirds
of the Past Perfect to Past cases
49
Inter-annotator Agreementon Temporal Ordering

• Overall 24/40 60
• Removing Unclears 24/33 72
• Unclears Breakdown
• Clear 1 POS error 1 Not enough context 5
• Other Disagreements
• Polar Opposition 4 (1 difficult)
• Entirely Before vs Equal
• (1) In an interview with Barbara Walters to be
  shown on ABCs Friday nights, Shapiro said he
  tried on the gloves and realized they would never
  fit Simpsons larger hands.
• Entirely Before vs Upto
• (2) They had contested the 1992 elections
  separately and won just six seats to 70 for MPRP.

• Based on 3 subjects on a common set of 40
  examples
• Fine-grained decisions about temporal ordering,
  are difficult
• Subjects show an acceptable level of agreement on
  more coarse-grained ordering (collapsing Entirely
  Before and Upto)

50
Automatic Link Identification in Text

• Mani, Schiffman, and Zhang (2003)

51
Approach Mixed-initiative Corpus Annotation

• Automatic preprocessing
• time expression flagging and evaluation (TempEx
  using TIDES TIMEX2 spec)
• clause structure (Clause-IT)
• events identified with finite clause indices
• lexical aspect (lexicon)
• tense (part-of-speech and patterns)
• Automatic computing of reference time value
  (tval) for each clause (given finding B above)
• tval is either time value of explicit timex in
  clause, or, when timex is absent, an implicit
  time value inferred from context by a naïve
  algorithm
• Simpson made the call at 3. He had visited
• Human annotation
• specify anchoring relation (AT, BEF, AFT, undef)
  of event wrt corrected tval
• Automatic learning of anchoring rules
• Automatic computation of temporal ordering

52
Time Expression and Clause Processing
TIDES TIMEX2 Annotation Scheme The Foreign
Minister told Thailand's Nation Newspaper VAL1998-01-04Sunday Pol Pot had left
Cambodia but was not in Thailand, ending credence
to a claim last
summer the aged and ailing former Khmer
Rouge leader had fled to China.
TIMEX2 Accuracy 5 annotators
F-measure 193 TDT2 docs Extent
Value Human Agreement .79 .86 TempEx
1.03 .76 .82

• CLAUSE-IT Tagger
• special-purpose finite-state grammars used with
  CASS to identify NPs, PPs, and VPs, and links
  between verbs and their subjects.
• proposed clause boundaries confirmed or adjusted
  using verb subcategorization information from
  Penn Treebank
• e.g., a PP can be attached to a VP containing an
  object NP if the verb has been followed in the
  PTB by a NP and a PP headed by the current prep.

Clause Tagging The United States unleashed
what appearedto be its fiercest daylight
strike on Afghanistan on
VAL1991-01-21Monday but the
administration faced concern from Saudi Arabia
and Pakistan over the bombardment to force
Taliban leaders to hand over Saudi
militant Osama bin Laden.
53
Computing Reference Times
Explicit reference time
Implicit reference time encoded in
clause tval feature
history_list doc_date for each finite clause
c do rtime timex2(c) if rtime then tval(c)
rtime unless type(c, rel_clause)
push(rtime, history_list) elsif reporting_verb(c)
then tval(c) doc_date elsif ?j s.t.
inside_quote(c, j) then tval(c) tval(j) else
tval(c) last (history_list)
A Naïve Algorithm For Computing tval (59
accurate)
54
Partially Ordering Links

• Machine-learnt rules used to generate anchor
  tuples
• Timex2 sorting used to generate tval tuples
• Some
  280,000 federal workers have been furloughed
  
• After
  breakfast with weekend participants, Clinton went
  to play 18 holes of golf with several friends
  despite fog and rain.
• The
  president and his family celebrated New Year's
  Eve at a dinner party sponsored by the
  Renaissance Weekend.

7 docs, 194 clauses, 723 human links
55
Mani et al. Results

• Introduces a corpus-based approach for anchoring
  and ordering events
• Approach is motivated by a pilot experiment
  investigating human event ordering capabilities
• Uses clause tagging and shallow semantic tagging
  of tense, aspect, time expressions
• Achieves .84 accuracy in anchoring events and .75
  F-measure in partially ordering them

56
Developmental Narrative Models

• Use Developmental Studies to Model Event
  Narrative Structure
• Take corpora from developmental models to train
  algorithms

57
Developmental Corpus Level 1

• David wants to buy a Christmas present for a very
  special person, his mother. David's father gives
  him 5.00 a week pocket money and David puts
  2.00 a week into his bank account.
• After three months David takes 20.00 out of his
  bank account and goes to the shopping mall. He
  looks and looks for a perfect gift.
• Suddenly he sees a beautiful brooch in the shape
  of his favorite pet. He says to himself "Mother
  loves jewelry, and the brooch costs only l7.00."
  He buys the brooch and takes it home. He wraps
  the present in Christmas paper and places it
  under the tree.
• He is very excited and he is looking forward to
  Christmas morning to see the joy on his mother's
  face.
• But when his mother opens the present she screams
  with fright because she sees a spider.

58
Event Ordering Level 1

• David wants to buy a Christmas present for a very
  special person, his mother. David's father gives
  him 5.00 a week pocket money and David puts
  2.00 a week into his bank account.
• After three months David takes 20.00 out of his
  bank account and goes to the shopping mall. He
  looks and looks for a perfect gift.
• Suddenly he sees a beautiful brooch in the shape
  of his favourite pet. He says to himself "Mother
  loves jewelry, and the brooch costs only l7.00."
  He buys the brooch and takes it home. He wraps
  the present in Christmas paper and places it
  under the tree.
• He is very excited and he is looking forward to
  Christmas morning to see the joy on his mother's
  face.
• But when his mother opens the present she screams
  with fright because she sees a spider.

• Present stative want, give, put
• Take
• See
• Present stative love, cost
• Buy
• Present stative be-excited,
• looking-forward
• Open

59
Narrative Convention Level 1

• Strategies
• - Scene setting with present tense
• - Narration with present tense
• For a state sentence in present tense A, if there
  is a sentence in present tense, B, in the
  document, interpret T(B) T(A).
• For an action sentence in present tense, A, if
  there is a sentence in present tense, B, in the
  document, interpret T(B)

60
Developmental Corpus Level 2

• Mrs Wilson and Mrs Smith are sisters. Mrs Wilson
  lives in a house in Duncan and Mrs Smith lives in
  a condominium in Victoria. One day Mrs Wilson
  visited her sister. When her sister answered the
  door Mrs Wilson saw tears in her eyes. "What's
  the matter?" she asked. Mrs Smith said "My cat
  Sammy died last night and I have no place to bury
  him".
• She began to cry again. Mrs Wilson was very sad
  because she knew her sister loved the cat very
  much. Suddenly Mrs. Wilson said "I can bury your
  cat in my garden in Duncan and you can come and
  visit him sometimes. Mrs. Smith stopped crying
  and the two sisters had tea together and a nice
  visit.
• It was now five o'clock and Mrs Wilson said it
  was time for her to go home. She put on her hat,
  coat and gloves and Mrs Smith put the dead Sammy
  into a shopping bag. Mrs Wilson took the shopping
  bag and walked to the bus stop. She waited a long
  time for the bus so she bought a newspaper. When
  the bus arrived she got on the bus, sat down and
  put the shopping bag on the floor beside her
  feet. She then began to read the newspaper. When
  the bus arrived at her bus stop she got off the
  bus and walked for about two minutes. Suddenly
  she remembered she left the shopping bag on the
  bus.

61
Event Ordering Level 2

• Mrs Wilson and Mrs Smith are sisters. Mrs Wilson
  lives in a house in Duncan and Mrs Smith lives in
  a condominium in Victoria. One day Mrs Wilson
  visited her sister. When her sister answered the
  door Mrs Wilson saw tears in her eyes. "What's
  the matter?" she asked. Mrs Smith said "My cat
  Sammy died last night and I have no place to bury
  him".
• She began to cry again. Mrs Wilson was very sad
  because she knew her sister loved the cat very
  much. Suddenly Mrs. Wilson said "I can bury your
  cat in my garden in Duncan and you can come and
  visit him sometimes. Mrs. Smith stopped crying
  and the two sisters had tea together and a nice
  visit.
• It was now five o'clock and Mrs Wilson said it
  was time for her to go home. She put on her hat,
  coat and gloves and Mrs Smith put the dead Sammy
  into a shopping bag. Mrs Wilson took the shopping
  bag and walked to the bus stop. She waited a long
  time for the bus so she bought a newspaper. When
  the bus arrived she got on the bus, sat down and
  put the shopping bag on the floor beside her
  feet. She then began to read the newspaper. When
  the bus arrived at her bus stop she got off the
  bus and walked for about two minutes. Suddenly
  she remembered she left the shopping bag on the
  bus.

• Present stative be-sister, live-1, live-2
• visit
• Present stative be-the-matter
• die (last night)
• Present stative -have
• begin cry
• Present stative be-sad,
• BECAUSE know
• love
• .

62
Narrative Convention Level 2

• Strategies
• - Scene setting with present tense
• - Narration with past tense

63
Developmental Corpus Level 3

• One day Nasreddin borrowed a pot from his
  neighbour Ali. The next day he brought it back
  with another little pot inside. "That's not
  mine," said Ali. "Yes, it is," said Nasreddin.
  "While your pot was staying with me, it had a
  baby."
• Some time later Nasreddin asked Ali to lend him a
  pot again. Ali agreed, hoping that he would once
  again receive two pots in return. However, days
  passed and Nasreddin had still not returned the
  pot. Finally Ali lost patience and went to demand
  his property. "I am sorry," said Nasreddin. "I
  can't give you back your pot, since it has died."
  "Died!" screamed Ali, "how can a pot die?"
  "Well," said Nasreddin, "you believed me when I
  told you that your pot had had a baby."

64
Developmental Corpus Level 3.5

• One day, Nasreddin was up on the roof of his
  house, mending a hole in the tiles. He had nearly
  finished, and he was pleased with his work.
  Suddenly, he heard a voice below call "Hello!"
  When he looked down, Nasreddin saw an old man in
  dirty clothes standing below.
• "What do you want?" asked Nasreddin.
• "Come down and I'll tell you," called the man.
• Nasreddin was annoyed, but he was a polite man,
  so he put down his tools. Carefully, he climbed
  all the way down to the ground.
• "What do you want?" he asked, when he reached the
  ground.
• "Could you spare a little money for an old
  beggar?" asked the old man. Nasreddin thought for
  a minute.
• Then he said, "Come with me." He began climbing
  the ladder again. The old man followed him all
  the way to the top. When they were both sitting
  on the roof, Nasreddin turned to the beggar.
• "No," he said.

65
Developmental Corpus Level 4

•  It was a cold night in September. The rain was
  drumming on the car roof as George and Marie
  Winston drove through the empty country roads
  towards the house of their friends, the
  Harrisons, where they were going to attend a
  party to celebrate the engagement of the
  Harrisons' daughter, Lisa. As they drove, they
  listened to the local radio station, which was
  playing classical music.
•      They were about five miles from their
  destination when the music on the radio was
  interrupted by a news announcement
•      "The Cheshire police have issued a serious
  warning after a man escaped from Colford Mental
  Hospital earlier this evening. The man, John
  Downey, is a murderer who killed six people
  before he was captured two years ago. He is
  described as large, very strong and extremely
  dangerous. People in the Cheshire area are warned
  to keep their doors and windows locked, and to
  call the police immediately if they see anyone
  acting strangely."
•      Marie shivered. "A crazy killer. And he's
  out there somewhere. That's scary."
•      "Don't worry about it," said her husband.
  "We're nearly there now. Anyway, we have more
  important things to worry about. This car is
  losing power for some reason -- it must be that
  old problem with the carburetor. If it gets any
  worse, we'll have to stay at the Harrisons'
  tonight and get it fixed before we travel back
  tomorrow."

66
Conclusion and Discussion