Abaca: Technically Assisted Sensitivity Review of Digital Records Presentation of our proof-of-concept classifier for assisting sensitivity review of digital records.

1. Abaca:
Technically Assisted Sensitivity
Review of Digital Records
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2. Agenda
● Transferring of Records to Archives
● The Digital Problem
● The Abaca Project
● Abaca Classifier Experiment
● The Test Collection
● The Abaca Project - Where Next?
● Break-Out Group Session
● Groups Discussion
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3. Transferring of Records to Archives
● Department selects and appraises records
for permanent preservation
– In paper, about 5% of output selected - digital
may rise to 20%
● Prior to transfer, department must
complete sensitivity review
– Paper review is well understood
– Digital presents many new challenges and is
not so well understood
● Hence our research !
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4. The Digital Problem
● The file has gone
● Volume will increase
– The way business is done has changed
– Largely unstructured despite EDRMs
● Big transfers of departmental records
● Appraisal
– Separate issue not addressed today
● Precautionary closure
– Need to research a solution
● Not unique to public records
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5. Our Approach
● Provide a Framework of Utilities ...
– to assist the Review Process
● Need Methods ...
– that respect the reality of Digital Records in all
their “Glory”
– that can be tailored to specific circumstances
● Need tools ...
– to help reviewers be more productive
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6. The Abaca Project
● Research to show that utilities will help
● Two Phases
– Proof of Concept (In Progress)
– Full Project (Seeking external funding)
● Today we are describing our proof-of-concept
work
● Abaca:
Technically Assisted Sensitivity Review of Digital Records
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7. Abaca Classifier Experiment
● Overview of the Task & Approach
● Predicting Exemptions using a Classifier
– Features
– Types of Features
● Example Sensitive Document
● Research Question
● Overview of Classification
● Evaluation Methodology
● Results
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8. The Task
Produce a classifier that can predict the presence of
sensitive material within unstructured text.
Initially focusing on two FOIA sensitivities
Section 27: International Relations
Section 40: Personal Information
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9. Approach
Manually review sensitive data to create a test collection.
Split test collection into training and test sets.
Train a classifier to predict the sensitivities in documents
using the set of identified features.
Test the classifier on previously “unseen” documents.
Measure classification success.
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10. External Resources
External Resources
Predict Exemptions Using a Classifier
Feature
Extraction
Learn
Classifier
Features represented
as real numbers.
Documents represented
as feature vectors.
Feature
Extraction
Run
Classifier
Features represented
as real numbers.
Documents represented
as feature vectors.
Learned Model
Predictions
Using
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11. Features
Document features, such as the words it contains or the
entities it references, convey information about a
document.
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12. Features
Document features, such as the words it contains or the
entities it references, convey information about a
document.
A document can be modelled by using a statistical
representation of its features.
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13. Features
Document features, such as the words it contains or the
entities it references, convey information about a
document.
A document can be modelled by using a statistical
representation of its features.
We use external knowledge bases, Natural Language
Processing and semantic analysis to better understand
the document features.
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14. Features
Document features, such as the words it contains or the
entities it references, convey information about a
document.
A document can be modelled by using a statistical
representation of its features.
We use external knowledge bases, Natural Language
Processing and semantic analysis to better understand
the document features.
The classifier recognises patterns in the documents’
feature sets and uses them for prediction.
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15. The features we use can be divided into three main categories.
Types of Features
Feature Type Examples Comments
Structure
Lists of Words (tf/idf)
Document Length
Number of Recipients
Ubiquitous throughout the collection.
Can expose patterns in document types.
High value information about the nature
of the communication.
Content
Subjectivity
Verbs
“D.O.B”
Negation
By applying techniques such as Natural
Language Processing and dictionary
based term matching, we can identify the
tone of the communication.
Entities
Countries
People
Organisations
Tells us what the document “is about”.
Context related to the entity, such as a
“high-risk” country or a “significant”
person or role can suggest sensitivity
likelihood.
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16. Research Question:
Can we produce a classifier that can predict the presence
of sensitive material within unstructured text?
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17. Research Question:
Measure:
Can we produce a classifier that can predict the presence
of sensitive material within unstructured text?
Balanced Accuracy - Arithmetic mean of True Positive and
True Negative predictions, with random = 0.5000
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18. Research Question:
Measure:
Test Collection:
Can we produce a classifier that can predict the presence
of sensitive material within unstructured text?
Balanced Accuracy - Arithmetic mean of True Positive and
True Negative predictions, with random = 0.5000
Total Documents 1849
Total Section 27 208
Total Section 40 142
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19. Overview of Classification
Learn
Classifier
on training
data
Run
Classifier
on unseen
data
Learned Model
Predictions
Test
Collection
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20. Evaluation Methodology
Test Collection
Assessor
Judgments
ResultsStatistical analysis
Classifier
Predictions
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21. Results
By adding features to a tf/idf text classification baseline, we
see noticeable improvement in both Section 27 and
Section 40 predictions.
But there is still much work to be done !
Balanced AccuracyBalanced Accuracy
Features s27 s40
Text Classification 0.6327 0.6344
+ Source Count 0.6369 0.6303
+ Country Count 0.6453 0.6406
+ Country Risk Score 0.6417 0.6368
+ DOB Score 0.6327 0.6391
+ Negation Score 0.6378 0.6382
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22. Test Collection - Aims
● To provide sensitivity judgements and
training data to develop and measure tools
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23. Test Collection - Aims
● To provide sensitivity judgements and
training data to develop and measure tools
● To measure and understand assessors’
behavior
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24. Test Collection - Measurments
● Time
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25. Test Collection - Measurments
● Time
● Agreement of sensitivity
– Not previously studied
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26. Test Collection - Measurments
● Time
● Agreement of sensitivity
– Not previously studied
● Hard Judgements
● Identify borderline cases
● Sensitivities sub-categories
– Good indicator for features
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27. The Abaca Project - Where Next?
● Understanding the real digital environment
– Changes in working practice
● Testing our proof-of-concept system against real
data
● More, wider and deeper
– More exemptions, more data, more features
– BIS, HO, MOJ, FCO, ... and more to come!
– Funding
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28. Questions and Feedback
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29. Break-Out Groups
Discuss sensitivity review in the
Welsh Government and language context.
Share your understanding and
develop some ideas.
Aims:
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30. Break-Out Groups
Questions:
1. What digital records does The Welsh Government
create?
2. What sort of sensitivities are expected within these
digital records?
3. What aspects of the sensitivity review process could
be technically supported by a software tool or system?
4. What document features could be used to identify the
expected sensitivities?
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31. Contact
http://projectabaca.wordpress.com/
graham.mcdonald@glasgow.ac.uk
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