An update on the EXTRA project - an open source rules based classifier for news content. Including the application for additional funding from Google DNI for FRANCIS

1. “Extra” by Jeremy Brooks https://flic.kr/p/4aKH3c

2. EXTRA and FRANCIS
Stuart Myles * Associated Press * 24th April 2018
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https://flic.kr/p/fBshW3
https://flic.kr/p/atFSAr

3. Rules-Based Classification
• Rules better for breaking news than statistical methods
– You don’t need 50 examples before you can start tagging
– A rule for a new topic doesn’t require other rules to change
• More consistent and scalable than hand tagging
• Easier to explain why rules classify content
– Machine learning methods can be “black boxes”
– Easier to precisely explain - and correct - mistakes
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4. EXTRA
EXTraction Rules Apparatus
Rules-based classification of text
Open source software https://iptc.github.io/extra/
EXTRA was developed by the IPTC
€50,000 Grant from the Digital News Initiative
https://www.digitalnewsinitiative.com/fund/
You can use your own taxonomy, rules and formats
- Example rules help us drive development of the EXTRA system
- You can use the example rules to see how to develop your own
- Rules could apply IPTC Media Topics or any other taxonomy
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5. Development Process
The EXTRA software was developed by Infalia
- All software is open source
Two linguists creating rules in English and German
- Samples rules to apply IPTC Media Topics
Example news corpora licensed for EXTRA
- English from Thomson Reuters
- German from APA
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6. EXTRA Components
Elasticsearch
Percolator
+ Custom
Code
Classification
Rule
authoring
Corpus
Testing
Schema
Management
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7. Classification using Percolator
• Elasticsearch
– A sophisticated, open source full-text search engine
– Lets you query documents stored in an index
• Elasticsearch Percolator
– Store queries in an index and match documents to queries
– Classification uses the percolator to match documents to rules
• EXTRA Rule Language
– Rule-writer-friendly language (easier than ES DSL)
– Access to all ES features, plus custom operators
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8. Schema and Rules Example
• Two fields - headline and body- with body allowed to be
queried by paragraph
headline
body
body_paragraph
• A rule to require that “angela merkel” and “us elections”
appear in the same paragraph
(prox/unit=paragraph/distance=1
(body adj "angela merkel")
(body adj "us elections")
)
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9. FRANCIS*
Using machine learning to empower rule-based
classification of news with semantics.
• “aboutness” evaluation
– Given that a story is about a topic, how much is it about it?
• Rule suggestion
– Suggest rules based on a pre-tagged corpus
• Enriched rule operators
– For example, nested “count” operators
– Using EXTRA as the foundation
* St Francis de Sales is the patron saint of writers and journalists
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