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| A full week of learning [http://en.wikipedia.org/wiki/General_Architecture_for_Text_Engineering GATE] text mining/information extraction language processing and talks. [http://gate.ac.uk/wiki/TrainingCourseAug2010/ Session wiki]
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| [[File:GATE_screenshot.png|900px|GATE developer screenshot]] | | [[GPS Location::{{ #geocode: Montreal, Canada}}]] |
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| 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.
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| = Using GATE developer =
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| * GATE developer is used to process sets of Language Resources in Corpus using Processing Resources. They are typically saved to a serialized Datastore.
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| * ANNIE, VG (verb group) processors.
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| * Preserve formatting embeds tags in HTML or XML.
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| ** Different strengths using GATE's graph (node/offset) based XML vs. preserved formatting (original xml/html)
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| = Information Extraction =
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| * IR - retrieve docs
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| * IE - retreive structured data
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| * Knowledge Engineering - rule based
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| * Learning Systems - statistical
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| Old Bailey IE project - old english (Online)
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| * POS - assigned in Token (noun, verb, etc)
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| * Gazateer - gotcha, have to set initialization parameter listsURL before it's loaded. Must also "save and reinitialize."
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| * Gazeteer creates Lookups, then transducer creaties named entities
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| * Then orthomatcher (spelling features in common) coreference associates those
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| * Annotation Key sets and annotation comparing
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| ** Need setToKeep key in Document Reset for any pre annotated texts
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| == Evaluation / Metrics ==
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| * Evaluation metric - mathematically against human annotated
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| * Scoring - performance measures for annotation types
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| * Precision = correct / correct + spurious
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| * Recall = correct / correct + missing
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| * F-measure is precision and recall (harmonic mean)
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| * F=2⋅(precision⋅recall / precision+recall)
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| * GATE supports average, strict, lenient
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| * Result types - Correct, missing, spurious, partially correct (overlapped)
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| * Tools > Annotations Diff - comparing human vs machine annotation
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| * Corpus > Corpus quality assurance - compare by type
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| * (B has to be the generated set)
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| * Annotation set transfer (in tools) - transfer between docs in pipeline
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| ** useful for eg HTML that has boilerplate
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| = Other notes =
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| == Lucene data store and ANIC ==
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| * Use <null> for default set
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| * Go to Datastore for queries
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| ** eg {Person}({Token})+{Money}
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| * Useful for debugging JAPE and results
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| [[File:GATE-lucene-person-money.png|800px]] | |
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| == To investigate ==
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| * markupAware for HTML/XML (keeps tags in editor)
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| * AnnotationStack
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| * Advanced Options
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| = JAPE =
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| * Rules based on tokens and lookups
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| == To review, gotchas ==
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| * Rule types : first takes only first match, excludes compound
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| ** a? b for "a b" will match "a b"
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| * multiplexor tranducers
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| * multi-constraint statements
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| * macros
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| * To reuse created annotations has to be a separate rule
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| = To follow up = | |
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| * WebSphinx crawler CREOLE plugin
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| = Demos =
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| * Mímir for querying large volumes of data (uses MG4J)
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| * Translating parts of speech between languages using Compound editor and Alignment editor
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| * Predicate extractor (MultiPaX)
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| ** Mixed results at best
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| * OwlExporter
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| ** NLP ontology
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| = Conclusions =
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| 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. :)
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| {{Blikied|Aug 30, 2010}}
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| [[Category:SemWeb]]
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