GATE, Human Language and
Machine Learning
http://gate.ac.uk/
http://nlp.shef.ac.uk/
Hamish Cunningham, Valentin Tablan,
Kalina Bontcheva, Diana Maynard
9th July/2003
1.
2.
3.
The Knowledge Economy and Human Language Technology
GATE: a General Architecture for Text Engineering
GATE, Information Extraction and Machine Learning
1. The Knowledge Economy and
Human Language
Gartner, December 2002:
•
•
taxonomic and hierachical knowledge mapping and indexing will be prevalent
in almost all information-rich applications
through 2012 more than 95% of human-to-computer information input will
involve textual language
A contradiction: formal knowledge in semantics-based systems vs. ambiguous
informal natural language
The challenge: to reconcile these two opposing tendencies
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Information Extraction (1):
from text to structured data
Two trends in the early 1990s:
• NLU: too difficult! Restrict the task and increase the performance
• Quantitative measurement (MUC – Message Understanding Conference, ACE –
Advanced Content Extraction, TREC – Text Retrieval Conference...) means good
estimation of accuracy
Types of extraction:
• Identify named entities (domain independent)
• Persons
• Dates
• Numbers
• Organizations
• Identify domain-specific events and terms; e.g., if we’re processing football:
• Relations: which team a player plays for
• Events: goal, foul, etc
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Information Extraction (2)
MUC-7 tasks
• NE: Named Entity recognition and typing
• CO: co-reference resolution
• TE: Template Elements
• TR: Template Relations
• ST: Scenario Templates
Example:
The shiny red rocket was fired on Tuesday. It is the brainchild of Dr. Big Head. Dr.
Head is a staff scientist at We Build Rockets Inc.
•
•
•
•
•
NE: entities are "rocket", "Tuesday", "Dr. Head" and "We Build Rockets"
CO: "it" refers to the rocket; "Dr. Head" and "Dr. Big Head“ are the same
TE: the rocket is "shiny red" and Head's "brainchild".
TR: Dr. Head works for We Build Rockets Inc.
ST: a rocket launching event occurred with the various participants.
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KEY
MNLG: Multilingual Natural Language Generation
OIE: Ontology-aware Information Extraction
AIE: Adaptive IE
CLIE: Controlled Language IE
(M)NLG
Human
Language
Formal Knowledge
(ontologies and
instance bases)
OIE
(A)IE
Controlled
Language
CLIE
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Semantic
Web;
Semantic
Grid;
Semantic
Web
Services
IE and
Knowledge:
Closing the
Language
Loop
Populating Ontologies with IE
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Protégé and Ontology Management
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IE: the bad news…
Domain specificity vs. task complexity:
very
general
specificity
simple entities
“acceptable” accuracy
events and relations
domain
specific
complexity
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2. GATE: Software Architecure for HLT
Software lifecycle in collaborative research
Project Proposal: We love each other. We can work so well together. We can hold
workshops on Santorini together. We will solve all the problems of AI that our
predecessors were too stupid to.
Analysis and Design: Stop work entirely, for a period of reflection and
recuperation following the stress of attending the kick-off meeting in Luxembourg.
Implementation: Each developer partner tries to convince the others that program
X that they just happen to have lying around on a dusty disk-drive meets the
project objectives exactly and should form the centrepiece of the demonstrator.
Integration and Testing: The lead partner gets desperate and decides to hardcode the results for a small set of examples into the demonstrator, and have a failsafe crash facility for unknown input ("well, you know, it's still a prototype...").
Evaluation: Everyone says how nice it is, how it solves all sorts of terribly hard
problems, and how if we had another grant we could go on to transform information
processing the World over (or at least the European business travel industry).
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GATE, a General Architecture for Text Engineering
• An architecture
A macro-level organisational picture for LE software systems.
• A framework
For programmers, GATE is an object-oriented class library that implements the
architecture.
• A development environment
For language engineers, computational linguists et al, GATE is a graphical
development environment bundled with a set of tools for doing e.g. Information
Extraction.
• Some free components... ...and wrappers for other people's components
• Tools for: evaluation; visualise/edit; persistence; IR; IE; dialogue; ontologies; etc.
• Free software (LGPL). Download at http://gate.ac.uk/download/
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Architectural principles
• Non-prescriptive, theory neutral (strength and weakness)
• Re-use, interoperation, not reimplementation (e.g. diverse XML support,
integration of tools like Protégé, Jena and Weka)
• (Almost) everything is a component, and component sets are user-extendable
Component-based development
• An OO way of chunking software: Java Beans
• GATE components: CREOLE = modified Java Beans (Collection of REusable
Objects for Language Engineering)
• The minimal component = 10 lines of Java, 10 lines of XML, 1 URL.
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GATE Language Resources
GATE LRs are documents, ontologies, corpora, lexicons, ……
Documents / corpora:
• GATE documents loaded from local files or the web...
• Diverse document formats: text, html, XML, email, RTF, SGML.
Processing Resources
Algorithmic components knows as PRs – beans with execute methods.
• All PRs can handle Unicode data by default.
• Clear distinction between code and data (simple repurposing).
• 20-30 freebies with GATE
• e.g. Named entity recognition; WordNet; Protégé; Ontology; OntoGazetteer;
DAML+OIL export; Information Retrieval based on Lucene
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Visual Resources
Performance Evaluation
• At document level – annotation diff
• At corpus level – corpus benchmark tool – tracking system’s performance over time
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Regression Test – Corpus Benchmark Tool
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Information Retrieval
Based on the Lucene IR engine
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Editing Multilingual Data
GATE Unicode Kit (GUK)
Java provides no special support for text input (this may change)
• Support for defining additional
Input Methods (IMs)
• currently 30 IMs
for 17 languages
• Pluggable in other
applications
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Processing Multilingual Data
All the visualisation and editing tools for ML LRs use enhanced Java facilities:
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A bit of a nuisance (users)
A representative fraction of GATE users:
• IBM TJ Watson, US
• the American National Corpus
project, US
• the Perseus Digital Library project,
Tufts University, US
• Longman Pearson publishing, UK
• Merck KgAa, Germany
• Canon Europe, UK
• Knight Ridder (the second biggest
US news publisher)
• BBN (leading HLT research lab), US
• SMEs in Sirma AI Ltd., Bulgaria
• Imperial College, London, the
University of Manchester, the
University of Karlsruhe, Vassar
College, the University of Southern
California and a large number of
other UK, US and EU Universities
• UK and EU projects inc. MyGrid,
CLEF, dotkom, AMITIES, Cub
Reporter, EMILLE, MUSE, Poesia...
GATE team projects:
• Conceptual indexing: MUMIS:
automatic semantic indices for sports
video
• MUSE, cross-genre entitiy finder
• HSL, Health-and-safety IE
• ETCSL: collaboration with IOAS
Oxford on Sumerian
• Old Bailey: collaboration with HRI
on 17th century court reports
• Multiflora: plant taxonomy text
analysis for biodiversity research escience
• Advanced Knowledge Technologies:
€12m UK five site collaborative
project
• H-TechSight: knowledge portal for
Chemicals Engineers
• Framework 6: SEKT, PrestoSpace,
KnowledgeWeb
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3. Machine Learning in GATE
• Uses classification.
[Attr1, Attr2, Attr3, … Attrn]  Class
• Classifies annotations.
(Documents can be classified as well using a simple
trick.)
• Annotations of a particular type are selected as
instances.
• Attributes refer to instance annotations.
• Attributes have a position relative to the
instance annotation they refer to.
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Attributes
Attributes can be:
– Boolean
The [lack of] presence of an annotation of a particular type [partially]
overlapping the referred instance annotation.
– Nominal
The value of a particular feature of the referred instance annotation.
The complete set of acceptable values must be specified a-priori.
– Numeric
The numeric value (converted from String) of a particular feature of
the referred instance annotation.
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Implementation
Machine Learning PR in GATE.
Has two functioning modes:
– training
– application
Uses an XML file for configuration:
<?xml version="1.0" encoding="windows-1252"?>
<ML-CONFIG>
<DATASET> … </DATASET>
<ENGINE>…</ENGINE>
<ML-CONFIG>
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<DATASET>
<DATASET>
<INSTANCE-TYPE>Token</INSTANCE-TYPE>
<ATTRIBUTE>
<NAME>POS_category(0)</NAME>
<TYPE>Token</TYPE>
<FEATURE>category</FEATURE>
<POSITION>0</POSITION>
<VALUES>
<VALUE>NN</VALUE>
<VALUE>NNP</VALUE>
<VALUE>NNPS</VALUE>
…
</VALUES>
[<CLASS/>]
</ATTRIBUTE>
…
</DATASET>
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<ENGINE>
<ENGINE>
<WRAPPER>gate.creole.ml.weka.Wrapper</WRAPPER>
<OPTIONS>
<CLASSIFIER>weka.classifiers.j48.J48</CLASSIFIER>
<CLASSIFIER-OPTIONS>-K 3</CLASSIFIER-OPTIONS>
<CONFIDENCE-THRESHOLD>0.85</CONFIDENCE-THRESHOLD>
</OPTIONS>
</ENGINE>
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Attributes Position
Instances type: Token
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Machine Learning PR
• Can save a learnt model to an external file for
later use.
Saves the actual model and the collected dataset.
• Can export the collected dataset in .arff format.
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Standard Use Scenario
Training
• Prepare training data by enriching
the documents with annotation for
attributes. (e.g. run Tokeniser,
POS tagger, Gazetteer, etc).
• Run the ML PR in training mode.
• Export the dataset as .arff and
perform experiments using the
WEKA interface in order to find
the best attribute set / algorithm /
algorithm options.
• Update the configuration file
accordingly.
• Run the ML PR again to collect
the actual data.
• [ Save the learnt model. ]
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Application
• Prepare data by enriching the
documents with annotation for
attributes. (e.g. run Tokeniser,
POS tagger, Gazetteer, etc).
• [ Load the previously saved
model. ]
• Run the ML PR in application
mode.
• [ Save the learnt model. ]
An Example
Learn POS category from POS context.
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Using Other ML Libraries
The MLEngine Interface
Method Summary
• void addTrainingInstance(List attributes)
Adds a new training instance to the dataset.
• Object classifyInstance(List attributes)
Classifies a new instance.
• void init()
This method will be called after an engine is created and has its dataset and
options set.
• void setDatasetDefinition(DatasetDefintion definition)
Sets the definition for the dataset used.
• void setOptions(org.jdom.Element options)
Sets the options from an XML JDom element.
• void setOwnerPR(ProcessingResource pr)
Registers the PR using the engine with the engine.
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Conclusion
GATE is:
• Addressing the need for scalable, reusable, and portable HLT solutions
• Supporting large data, in multiple media, languages, formats, and locations
• Lowering the cost of creation of new language processing components
• Promoting quantitative evaluation metrics via tools and a level playing field
• Promoting experimental repeatability by developing and supporting free
software
Perhaps it may become:
• A vehicle for the spread of collaborative experiments in ML and HLT?
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