The document discusses the Extra software developed by IPTC for rules-based classification of news, emphasizing its advantages over statistical methods, such as consistency and ease of adaptation. It details the development process involving open-source tools and linguists creating rules in English and German, alongside the use of Elasticsearch for classification. Additionally, it covers the integration of machine learning to enhance rule suggestion and evaluation of content relevance.

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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