GATE track 1 session

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A full week of learning GATE text mining/information extraction language processing and talks. Session wiki

GATE developer screenshot

GATE is written in Java and very Java centric. This makes it portable, fast, and heavyweight. A programming library is available. It's 14 years old and has many users and contributors.

Using GATE developer

  • GATE developer is used to process sets of Language Resources in Corpus using Processing Resources. They are typically saved to a serialized Datastore.
  • ANNIE, VG (verb group) processors.
  • Preserve formatting embeds tags in HTML or XML.
    • Different strengths using GATE's graph (node/offset) based XML vs. preserved formatting (original xml/html)

Information Extraction

  • IR - retrieve docs
  • IE - retreive structured data
  • Knowledge Engineering - rule based
  • Learning Systems - statistical

Old Bailey IE project - 17th century english (Online)

  • POS - assigned in Token (noun, verb, etc)
  • Gazateer - gotcha, have to set initialization parameter listsURL before it's loaded. Must also "save and reinitialize."
  • Gazeteer creates Lookups, then transducer creaties named entities
  • Then orthomatcher (spelling features in common) coreference associates those
  • Annotation Key sets and annotation comparing
    • Need setToKeep key in Document Reset for any pre annotated texts

Evaluation / Metrics

  • Evaluation metric - mathematically against human annotated
  • Scoring - performance measures for annotation types
  • Precision = correct / correct + spurious
  • Recall = correct / correct + missing
  • F-measure is precision and recall (harmonic mean)
  • F=2⋅(precision⋅recall / precision+recall)
  • GATE supports average, strict, lenient
  • Result types - Correct, missing, spurious, partially correct (overlapped)
  • Tools > Annotations Diff - comparing human vs machine annotation
  • Corpus > Corpus quality assurance - compare by type
  • (B has to be the generated set)
  • Annotation set transfer (in tools) - transfer between docs in pipeline
    • useful for eg HTML that has boilerplate

To investigate

  • markupAware for HTML/XML (keeps tags in editor)
  • AnnotationStack
  • Advanced Options


  • Rules based on tokens and lookups
Phase: MatchingStyles
Input: Lookup
Options: control = appelt
Rule: Test1
({Lookup.majorType == location})?
{Lookup.majorType == loc_key}
:match.Location = {rule=Test1}

Copying features: :match.Location = { type = :match.Lookup.minorType}

To review, gotchas

  • Rule types : first takes only first match, excludes compound
    • a? b for "a b" will match "a b"
  • multiplexor tranducers
  • multi-constraint statements
  • macros
  • To reuse created annotations has to be a separate rule

Matching types

Matching styles for JAPE

To follow up

Other notes

Lucene data store and ANNIC

  • Use <null> for default set
  • Go to Datastore for queries
    • eg {Person}({Token})+{Money}
  • Useful for debugging JAPE and results



  • Mímir for querying large volumes of data (uses MG4J)
  • Translating parts of speech between languages using Compound editor and Alignment editor
  • Predicate extractor (MultiPaX)
    • Mixed results at best
  • OwlExporter
    • NLP ontology


While it can do a lot out of the box and benefits from development time and breadth of connectivity, to be useful to more than patient specialists, it needs usability testing. A lot of things are inobvious and too domain specific that with a bit of work could be more broadly useful. Interaction could include a lot more immediate, useful and interesting looking displays. A web based version could have these features. However the team seems somewhat ambivalent about development. :)

Looking forward to learning about programming using GATE libraries.


Blikied on Aug 30, 2010