part 3 real world applications: sumtime-mousam. dept. of computing science, university of aberdeen2...

Part 3Real World Applications:

SumTime-Mousam

Dept. of Computing Science, University of Aberdeen 2

In this lecture you learn

• SumTime-Mousam – Knowledge acquisition– Design

• Document planning• Microplanning• realization

– Evaluation• Post-edit• End-user


Introduction

• So far we studied– Data analysis techniques

• Time series data• Spatial data

– Visualization techniques– NLG techniques

• Now we will study– SumTime-Mousam

• a weather forecast text generation system

– HCE 3.0• a visual knowledge discovery tool


SumTime-Mousam

• NLG system that automates the task of writing weather forecasts– Developed in our department

• Input:Numerical Weather Prediction (NWP) data– Data samples for a few dozens of parameters

every hour/3 hour from two NWP models• Output: marine forecasts - forecasts for

offshore oilrig applications• Has been used by our industrial

collaborator since June 2002.– Forecasts for 150 locations per day


ExampleDay Hour Wind

DirWindSpeed10m

WindSpeed50m

Gust10m

Gust50m

13-06-02 0000 WSW 12.0 15.0 15.0 19.0

13-06-02 0300 WSW 15.0 19.0 19.0 23.0

13-06-02 0600 WSW 19.0 24.0 24.0 30.0

13-06-02 0900 WSW 18.0 22.0 22.0 28.0

13-06-02 1200 W 17.0 21.0 21.0 27.0

13-06-02 1500 W 15.0 19.0 19.0 23.0

13-06-02 1800 WSW 13.0 16.0 16.0 20.0

13-06-02 2100 WSW 11.0 14.0 14.0 17.0

14-06-02 2400 WSW 11.0 14.0 14.0 17.0


3. FORECAST 0 - 24 GMT, Thu 13-Jun 2002

WIND(KTS)

10M: WSW 10-15 increasing 17-22 by early morning, then gradually easing 9-14 by midnight.

50M: WSW 13-18 increasing 22-27 by early morning, then gradually easing 12-17 by midnight.

WAVES(M)

SIG HT: 0.5-1.0 rising 1.5-2.0 by early morning, then falling0.5-1.0 by midnight.

MAX HT: 1.0 or less rising 2.0-2.5 by early morning, then falling 1.0-1.5 by midnight.

PERIOD(SEC)

WAVE PERIOD: 2-4 rising 5-7 by morning, then falling 3-5by midnight.

WINDWAVE PERIOD: 2-4 rising 5-7 by morning, thenfalling 3-5 by midnight.

SWELL PERIOD: 5-7 rising 8-10 by midday, then falling 5-7by midnight.

WEATHER: Cloudy with light rain becoming partly cloudy around midnight.

VIS(NM): Greater than 10 reduced to 5-8 in precipitation.

AIR TEMP(C): 9-11 rising 12-14 in the early evening falling 10-12 around midnight.

CLOUD(OKTAS/FT): 6-8 ST/SC 100-300 lifting 2-4 ST/SC 500-700 around midnight.

Example


Knowledge Acquisition (KA)• KA Tasks

– Think aloud sessions– Direct Acquisition of knowledge– Onsite Observations– Corpus analysis– Collaborative prototype development


Corpus Description

• SumTime-Meteo - parallel Text-Data Corpus• Size - 1045 parallel Text-Data units• Unit

– NWP Model Data– Human Written Forecast Text

• Similar in concept to statistical MT (Machine Translation)

• Naturally Occurring– written for oilrig staff in the North Sea

• Distribution of the Corpus– Available in the public domain


Parallel Text - DataHour Wind Dir Wind Speed00 WSW 1203 WSW 1506 WSW 1909 WSW 18

12 W 1715 W 1518 WSW 1321 WSW 1124 WSW 11

WSW 10-15 increasing 17-22 by early morning, then gradually easing 9-14 by midnight.


Corpus Analyses

• Meanings of Time phrases– Meanings of time phrases in terms of

numerical data– required for lexical choice in

summarization

• No standard time phrase mappings exist

• Numerical time values not mentioned in forecasts


Alignment

• Step 1– Parsing the forecast texts

• parser tuned for forecast text syntax• break the text into phrases• extract information such as wind speed

and wind direction• parser carried forward values for the

missing fields (shown later in the example)


Example

InformationSr.No.

PhrasesSpeed Direction Time Phrase Change Verb

1 SSW 12-16 12-16 SSW None None2 BACKING ESE 16-20

IN THE MORNING,16-20 ESE IN THE

MORNINGBACKING

3 BACKING NE EARLYAFTERNOON

16-20 NE EARLYAFTERNOON

BACKING

4 THEN NNW 24-28LATE EVENING

24-28 NNW LATEEVENING

BACKING

SSW 12-16 BACKING ESE 16-20 IN THE MORNING, BACKING NE EARLY AFTERNOON THEN NNW 24-28 LATE EVENING


Alignment (2)

• Step 2– Associate each phrase with an entry in

the input data set• 43% of the phrases matched with a single

entry (without ambiguity)• heuristics used for improving the accuracy

of alignment to 70%

• Further improvements in alignment under investigation


Example (2)Example Phrase

VEERING SW 10-14 BY EVENING

Input Data

1800 SW

By evening ---------> 1800 hours

Example Phrase

BACKING ESE 16-20 IN THE MORNING

Input Data

0600 ESE 18

0900 ESE 16

In the morning -------------> 0600 hours


Results

Time Most common phrase incorpus

Expert suggested phrases Phrases used inSUMTIME-MOUSAM

0 By late evening Around midnight By midnight3 Tonight In early hours After midnight6 Overnight In early morning By early morning9 By midday During midday By morning12 By midday Around midday By midday15 By mid afternoon In mid afternoon By mid afternoon18 By evening In early evening By early evening21 By evening During night By evening


Limitations of Corpus Analysis

• Quality of knowledge acquired– good in some cases– poor in many cases– required clarifications from experts

• Useful when used along with other KA techniques


KA MethodologyDirectly Ask

Experts for Knowledge

Structured KA with Experts

Corpus Analysis

Expert Revision

Initial Prototype

Initial Version of Full System

Final System


SumTime-Mousam:Architecture

• Document planning – content selection and organisation

• Microplanning– selecting words and phrases– ellipsis

• Realisation– output text using the words and phrases by

applying grammar rules• Control Data

– derived from end user profile

Doc. Planning

MicroPlanning

RealisationInputData

OutputText

Control Data


Content Selection

• What data items are worth picking up for the summary?– Reasoning from first principles - no detailed

user model– Reusing data analysis techniques used by KDD

community • Attractive • but not developed for communication

• Adapting data analysis techniques to suit needs of communication using the Gricean Maxims


Data Analysis

• Expert’s View– Step Method– Report changes above thresholds

(Significant changes)

• Corpus View– Segmentation Method– Report changes in Slopes/ report trends


ExampleMAGNUS / THISTLE / NW HUTTON, EAST OF SHETLANDday hour wind dir wind speed

(Knots)20-1-01 6 S 420-1-01 9 S 620-1-01 12 S 720-1-01 15 S 1020-1-01 18 S 1220-1-01 21 S 1621-1-01 0 S 18

FORECAST FOR 06-24 GMT, 20- Jan 2001:S 02-06 INCREASING 16-20 BY EVENING


Expert’s View-Step ModelStep Model

0

2

4

6

8

10

12

14

16

18

20

Time

Win

d S

pee

d(k

no

ts)

Wind Speed 4 6 7 10 12 16 18

6 9 12 15 18 21 24

5 knots

5 knots

S 3-8 INCREASING 8-13 BY AFTERNOON AND 13-18 BY EVENING.


Corpus View-Segmentation Model

Segmentation Model

0

2

4

6

8

10

12

14

16

18

20

6 9 12 15 18 21 24

Time

Win

d S

pee

d

S 3-8 INCREASING 15-20 BY MIDNIGHT.


Gricean Maxims (Grice 1975)• Maxim of Quality: Try to make your

contribution one that is true. More specifically:– Do not say what you believe to be false.– Do not say that for which you lack adequate

evidence.

• Maxim of Quantity:– Make your contribution as informative as is required

(for the current purposes of the exchange).– Do not make your contribution more informative

than is required.

• Maxim of Relevance: Be relevant.• Maxim of Manner: Be perspicuous. More

specifically:– Avoid obscurity of expression. -Avoid ambiguity.– Be brief. -Be orderly.


Application of Gricean Maxims - Example

• Maxim of Quality– Try to report true values from the input

data– Use linear interpolation instead of linear

segmentation– Uncertainty in the input data needs to

be communicated to the user


Sample DataHour Wind

DirectionWindSpeed

0600 S 8

0900 S 6

1200 S 7

1500 S 10

1800 S 12

2100 S 16

0000 S 20


Linear Regression Vs Linear Interpolation

Linear Regression

0

5

10

15

20

25

0 3 6 9 12 15 18 21 24 27

Time in Hours

Win

d S

pee

d i

n K

no

ts

Linear Interpolation

0

5

10

15

20

25

0 3 6 9 12 15 18 21 24 27

Time in HoursW

ind

Sp

eed

in

Kn

ots


Linear Regression Vs Linear Interpolation (2)

• Linear Regression– S 03-07 INCREASING 16-20 BY MIDNIGHT

• Linear Interpolation– S 06-10 INCREASING 18-22 BY MIDNIGHT

• Human Written Forecast– S 06-10 INCREASING 18-22 BY MIDNIGHT

• Although visually linear regression looks better forecasters do not use it.

• Uncertainty– Speed values are mentioned as ranges e.g. 06-

07 & 18-22


Intrinsic Evaluation of content determination

• Metrics– Short - Size (Accessibility)– Accurate - Error (Informativeness)

• Size Computation– measured at the conceptual level– number of wind states

• Error Computation– Vertical distance from the line of

approximation– combined error in wind speed and wind

direction – normalized


Results of Evaluation• Segmentation produces shorter

summaries without losing accuracy• Details

– 16.5% of cases segmentation is better than step in both size and error

– 0.56% of cases the step method is better than segmentation in both size and error

– 2.5% of cases segmentation is better then step error wise but worse size wise

– 32% of cases segmentation is better then step size wise but worse error wise

– 31% of cases segmentation is better than step error wise but equal size wise


Micro-planning & Realization

• Based on Parallel corpus analysis (described earlier) and

• Expert KA/Revision• Details in Papers at

– www.csd.abdn.ac.uk/research/sumtime/papers.html

http://www.csd.abdn.ac.uk/research/sumtime/papers.html

http://www.csd.abdn.ac.uk/research/sumtime/papers.html


SumTime-Mousam at Weathernews (UK) Ltd.

SumTime-MousamData 1

Pre-edited TextEdited Data

Text 1

Marfors Data Editor

Marfors Data Editor

SumTime_Mousam

Marfors Text Editor

NWP Data

Post-edited Text


Post-edit Evaluation

• Total number of forecasts analysed = 2728• 2728 texts divided into 73041 phrases• 7608 (10%) phrases could not be aligned• Alignment failures imply that forecasters are

not happy with our content determination– Which is dependent on a process called

segmentation

• Forecasters seem to perform more sophisticated reasoning than simple segmentation


Analysis results (1)

• Out of the successfully aligned phrases– 43914 phrases matched perfectly– 21519 phrases are mismatches

• Detailed analysis of the mismatches


Analysis Results (2)

mismatches29%

perfect matches

61%

alignment failures

10% word additions & deletions,

65%

data replacements,

20%

lexical replacements,

15%

The pie chart shows the results of phrase level comparisons

The bar chart shows the detailedanalysis of the mismatched phrases


End-user Evaluation• 73 End-users (oil company staff supporting offshore oilrigs)

participated in this evaluation• used forecasts produced by the following three methods

– human written weather forecasts– SumTime-Mousam generated weather forecasts– SumTime-Mousam expressing Human select content

• Each participant completed a questionnaire that has two parts– Part 1

• forecast produced by one of the above three methods (anonymous)• Participant is required to answer comprehension questions based on the

forecast– Part 2

• showed any two forecasts from the above three methods (anonymous)• Participant specified his/her preference for one of the two forecasts

• The main result– end-users consider the SumTime-Mousam generated output

linguistically better than human written forecasts– Content of SumTime-Mousam is not as good as human selected

content


Conclusion

• SumTime-Mousam is the result of knowledge obtained from– several knowledge acquisition studies

• Expert based• Corpus based

– Several evaluation studies• Intrinsic evaluation• Post-edit evaluation• End-user evaluation

• The development of SumTime-Mousam went through many cycles

• Building novel technology requires iterative approach with multiple KA and evaluation studies

part 3 real world applications: sumtime-mousam. dept. of computing science, university of aberdeen2...

Documents

computing science

sumtimemousam slide

investigation slide

day slide

university of aberdeen2

evening input data

late evening slide

parallel textdata corpus