Introduction to ANNIE

1. Introduction to ANNIE http://gate.ac.uk/http://nlp.shef.ac.uk/ Diana Maynard University of Sheffield March 2004 1/(20)

2. What is ANNIE? • ANNIE is a vanilla information extraction system comprising a set of core PRs: • Tokeniser • Sentence Splitter • POS tagger • Gazetteers • Semantic tagger (JAPE transducer) • Orthomatcher (orthographic coreference) 2/(20)

3. ANNIE Pipeline 3/(20)

4. Other Processing Resources • There are also lots of additional processing resources which are not part of ANNIE itself but which come with the default installation of GATE • Gazetteer collector • PRs for Machine Learning • Various exporters • Annotation set transfer etc…. 4/(20)

5. Creating a new application from ANNIE • Typically a new application will use most of the core components from ANNIE • The tokeniser, sentence splitter and orthomatcher are basically language, domain and application-independent • The POS tagger is language dependent but domain and application-independent • The gazetteer lists and JAPE grammars may act as a starting point but will almost certainly need to be modified • You may also require additional PRs (either existing or new ones) 5/(20)

6. Modifying gazetteers • Gazetteers are plain text files containing lists of names • Each gazetteer set has an index file listing all the lists, plus features of each list (majorType, minorType and language) • Lists can be modified either internally using Gaze, or externally in your favourite editor • Gazetteers can also be mapped to ontologies • To use Gaze and the ontology editor, you need to download the relevant creole files 6/(20)

7. JAPE grammars • A semantic tagger consists of a set of rule-based JAPE grammars run sequentially • JAPE is a pattern-matching language • The LHS of each rule contains patterns to be matched • The RHS contains details of annotations (and optionally features) to be created • More complex rules can also be created 7/(20)

8. Input specifications • The head of each grammar phase needs to contain certain information • Phase name • Inputs • Matching style e.g. Phase: location Input: Token Lookup Number Control: appelt 8/(20)

9. Matching algorithms and Rule Priority • 3 styles of matching: • Brill (fire every rule that applies) • First (shortest rule fires) • Appelt (use of priorities) • Appelt priority is applied in the following order • Starting point of a pattern • Longest pattern • Explicit priority (default = -1) 9/(20)

10. NE Rule in JAPE Rule: Company1 Priority: 25 ( ( {Token.orthography == upperInitial} )+ //from tokeniser {Lookup.kind == companyDesignator} //from gazetteer lists ):match --> :match.NamedEntity = { kind=company, rule=“Company1” } 10/(20)

11. LHS of the rule • LHS is expressed in terms of existing annotations, and optionally features and their values • Any annotation to be used must be included in the input header • Any annotation not included in the input header will be ignored (e.g. whitespace) • Each annotation is enclosed in curly braces • Each pattern to be matched is enclosed in round brackets and has a label attached 11/(20)

12. Macros • Macros look like the LHS of a rule but have no label Macro: NUMBER (({Digit})+) • They are used in rules by enclosing the macro name in round brackets ( (NUMBER)+):match • Conventional to name macros in uppercase letters • Macros hold across an entire set of grammar phases 12/(20)

13. Contextual information • Contextual information can be specified in the same way, but has no label • Contextual information will be consumed by the rule ({Annotation1}) ({Annotation2}):match ({Annotation3})  13/(20)

14. RHS of the rule • LHS and RHS are separated by  • Label matches that on the LHS • Annotation to be created follows the label (Annotation1):match  :match.NE = {feature1 = value1, feature2 = value2} 14/(20)

15. Using phases • Grammars usually consist of several phases, run sequentially • Only one rule within a single phase can fire • Temporary annotations may be created in early phases and used as input for later phases • Annotations from earlier phases may need to be combined or modified • A definition phase (conventionally called main.jape) lists the phases to be used, in order • Only the definition phase needs to be loaded 15/(20)

16. More complex JAPE rules • Any Java code can be used on the RHS of a rule • This is useful for e.g. feature percolation, ontology population, accessing information not readily available, comparing feature values, deleting existing annotations etc. • There are examples of these in the user guide and in the ANNIE NE grammars • Most JAPE rules end up being complex! 16/(20)

17. Using JAPE for other tasks • JAPE grammars are not just useful for NE annotation • They can be a quick and easy way of performing any kind of task where patterns can be easily recognised and a finite-state approach is possible, e.g. transforming one style of markup into another, deriving features for the learning algorithms 17/(20)

18. Example rule for deriving features Rule: Entity( {Gpe}| {Organization}| {Person}| {Location}| {Facility} ):entity-->{gate.AnnotationSet entityAS = (gate.AnnotationSet)bindings.get("entity"); gate.Annotation entityAnn = (gate.Annotation)entityAS.iterator().next(); gate.FeatureMap features = Factory.newFeatureMap(); features.put("type", entityAnn.getType());outputAS.add(entityAnn.getStartNode(), entityAnn.getEndNode(), "Entity“, features);} 18/(20)

19. Finding Examples • ANNIE for default NE rules: gate/src/gate/resources/creole/NEtransducer/NE/ • MUSE for more complex NE rules: muse/src/muse/resources/grammar/main • h-TechSight for ontology population: htechsight/application/grammar • Various other applications generally follow the format: projectname/application/grammar/ 19/(20)

20. Conclusion This talk: http://gate.ac.uk/sale/talks/annie-tutorial.ppt More information: http://gate.ac.uk/ 20/(20)