GATE, HLT and Machine Learning, Sheffield, July 2003Presentation Transcript
GATE, Human Language and Machine Learning
Hamish Cunningham, Valentin Tablan,
Kalina Bontcheva, Diana Maynard
9 th July/2003
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
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)
Identify domain-specific events and terms; e.g., if we’re processing football:
Relations: which team a player plays for
Events: goal, foul, etc
Information Extraction (2)
NE: Named Entity recognition and typing
CO: co-reference resolution
TE: Template Elements
TR: Template Relations
ST: Scenario Templates
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.
Human Language Formal Knowledge (ontologies and instance bases) (A)IE CLIE (M)NLG Controlled Language OIE Semantic Web; Semantic Grid; Semantic Web Services KEY MNLG : Multilingual Natural Language Generation OIE : Ontology-aware Information Extraction AIE : Adaptive IE CLIE : Controlled Language IE IE and Knowledge: Closing the Language Loop
Populating Ontologies with IE
Protégé and Ontology Management
IE: the bad news… Domain specificity vs. task complexity: complexity specificity “ acceptable” accuracy domain specific simple entities events and relations very general
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 hard-code the results for a small set of examples into the demonstrator, and have a fail-safe 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).
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/
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
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.
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.
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
At document level – annotation diff
At corpus level – corpus benchmark tool – tracking system’s performance over time
Regression Test – Corpus Benchmark Tool
Information Retrieval Based on the Lucene IR engine
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
Processing Multilingual Data All the visualisation and editing tools for ML LRs use enhanced Java facilities:
A bit of a nuisance (users)
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 e-science
Advanced Knowledge Technologies : €12m UK five site collaborative project
H-TechSight : knowledge portal for Chemicals Engineers
Framework 6 : SEKT, PrestoSpace, KnowledgeWeb
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...
3. Machine Learning in GATE
Uses classification .
[Attr 1 , Attr 2 , Attr 3 , … Attr n ] 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.
Attributes can be:
The [lack of] presence of an annotation of a particular type [partially] overlapping the referred instance annotation.
The value of a particular feature of the referred instance annotation. The complete set of acceptable values must be specified a-priori.
The numeric value (converted from String) of a particular feature of the referred instance annotation.