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Patent Processing with GATE

Patent Processing with GATE. Kalina Bontcheva, Valentin Tablan University of Sheffield. Outline. Why patent annotation? The data model The annotation guidelines Building the IE pipeline Evaluation Scaling up and optimisation Find the needle in the annotation (hay)stack.

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Patent Processing with GATE

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  1. Patent Processing with GATE Kalina Bontcheva, Valentin TablanUniversity of Sheffield

  2. Outline • Why patent annotation? • The data model • The annotation guidelines • Building the IE pipeline • Evaluation • Scaling up and optimisation • Find the needle in the annotation (hay)stack GATE Summer School - July 27-31, 2009

  3. What is Semantic Annotation? • Semantic Annotation: • Is about attaching tags and/or ontology classes to text segments; • Creates a richer data space and can allow conceptual search; • Suitable for high-value content • Can be: • Fully automatic, semi-automatic, manual • Social • Learned GATE Summer School - July 27-31, 2009

  4. Semantic Annotation GATE Summer School - July 27-31, 2009

  5. Why annotate patents? • Simple text search works well for the Web, but, • patent searchers require high recall (web search requires high precision); • patents don't contain hyperlinks; • patent searchers need richer semantics than offered by simple text search; • patent text amenable to HLT due to regularities and sub-language effects. GATE Summer School - July 27-31, 2009

  6. How can annotation help? • Format irregularities • “Fig. 3”, “FIG 3”, “Figure 3”, etc. • Data normalisation • “Figures. 3 to 5” -> FIG. 2, FIG 4, FIG 5. • “23rd Oct 1998” -> 19981023 • Text mining – discovery of: • product names and materials; • references to other patents, publications and prior art; • measurements. • etc. GATE Summer School - July 27-31, 2009

  7. Manual vs. Automatic • Manual SA • high quality • very expensive • requires small data or many users (e.g flickr, del.icio.us). • Automatic SA • inexpensive • medium quality • can only do simple tasks • Patent data • too large to annotate manually • too difficult to annotate fully automatically GATE Summer School - July 27-31, 2009

  8. The SAM Projects • Collaboration between Matrixware, Sheffield GATE team, and Ontotext • Started in 2007 and ongoing • Pilot study for applicability of Semantic Annotation to patents • GATE Teamware: Infrastructure for collaborative semantic annotation • Large scale experiments • Mimir: Large scale indexing infrastructure supporting hybrid search (text, annotations, meaning) GATE Summer School - July 27-31, 2009

  9. Technologies Data Enrichment (Semantic Annotation) Knowledge Management Data Access (Search/Browsing) Teamware KIM Large Scale Hybrid Index GATE OWLIM GATE OWLIM GATE ORDI JBPM, etc… TRREE Lucene, etc… TRREE MG4J, etc… TRREE Sheffield Ontotext Other GATE Summer School - July 27-31, 2009

  10. Teamware revisited: A Key SAM Infrastructure Collaborative Semantic Annotation Environment • Tools for semi-automatic annotation; • Scalable distributed text analytics processing; • Data curation; • User/role management; • Web-based user interface. GATE Summer School - July 27-31, 2009

  11. Semantic Annotation Experiments Wide Annotation • Cover a range of generally useful concepts: Documents, document parts, references • High level detail. Deep Annotation • Cover a narrow range of concepts Measurements • As much detail as possible. GATE Summer School - July 27-31, 2009

  12. Data Model GATE Summer School - July 27-31, 2009

  13. Example Bibliographic Data GATE Summer School - July 27-31, 2009

  14. Example measurements GATE Summer School - July 27-31, 2009

  15. Example References GATE Summer School - July 27-31, 2009

  16. The Patent Annotation Guidelines • 11 pages (10 point font), with concrete examples, general rules, specific guidelines per type, lists of exceptions, etc. • The section on annotating measurements is 2 pages long! • The clearer the guidelines – the better Inter-Annotator Agreement you’re likely to achieve • The higher the IAA – the better automatic results can be obtained (less noise!) • The lengthier the annotations – the more scope for error there is, e.g., references to other papers had the lowest IAA GATE Summer School - July 27-31, 2009

  17. Annotating Scalar Measurements • numeric value including formulae • always related to a unit • more than one value can be related to the same unit • ... [80]% of them measure less than [6] um [2] ... • [2x10 -7] Torr • [29G×½]” needle • [3], [5], [6] cm • turbulence intensity may be greater than [0.055], [0.06] ...

  18. Annotating Measurement Units • including compound unit • always related to at least one scalarValue • do not include a final dot • %, :, / should be annotated as unit • deposition rates up to 20 [nm/sec] • a fatigue life of 400 MM [cycles] • ratio is approximately 9[:]7

  19. Annotation Schemas: Measurements Example <?xml version="1.0"?> <schema xmlns="http://www.w3.org/2000/10/XMLSchema"> <element name="Measurement"> <complexType> <attribute name="type" use="required"> <simpleType> <restriction base="string"> <enumeration value="scalarValue"/> <enumeration value="unit"/> </restriction> </simpleType> </attribute> <attribute name="requires-attention" use="optional"> <simpleType> <restriction base="string"> <enumeration value="true"/> <enumeration value="false"/> </restriction> </simpleType> </attribute>

  20. The IE Pipeline • JAPE Rules vs Machine Learning • Moving the goal posts: dealing with unstable annotation guidelines • JAPE – just change a few rules hopefully • ML – could require significant manual re-annotation effort of the training data • Bootstrapping training data creation with JAPE patterns – significantly reduces the manual effort • For ML to be successful, we need IAA to be as high as possible – noisy data problem otherwise • Insufficient training data initially, so chose JAPE approach GATE Summer School - July 27-31, 2009

  21. Example JAPEs for References Macro: FIGNUMBER //Numbers 3, 45, also 3a, 3b ( {Token.kind == "number"} ({Token.length == "1",Token.kind == "word"})? ) Rule:IgnoreFigRefsIfThere Priority: 1000 ( {Reference.type == "Figure"} )--> {} Rule:FindFigRefs Priority: 50 ( ( ({Token.root == "figure"} | {Token.root == "fig"}) ({Token.string == "."})? ((FIGNUMBER) | (FIGNUMBERBRACKETS) ):number ):figref )--> :figref.Reference = {type = "Figure", id = :number.Token.string} GATE Summer School - July 27-31, 2009

  22. Example Rule for Measurements Rule: SimpleMeasure /* * Number followed by a unit. */ ( ({Token.kind == "number"}) ):amount ({Lookup.majorType == "unit"}):unit --> :amount.Measurement = {type = scalarValue, rule = "measurement.SimpleMeasure"}, :unit.Measurement = {type = unit, rule = "measurement.SimpleMeasure"} GATE Summer School - July 27-31, 2009

  23. The IE Annotation Pipeline GATE Summer School - July 27-31, 2009

  24. Hands-on: Identify More Patterns • Open Teamware and login • Find corpus patents-sample • Run ANNIC to identify some patterns for references to tables and figures and measurements • There are already POS tags, Lookup annotations, morphological ones • Units for measurements are Lookup.majorType == “unit” GATE Summer School - July 27-31, 2009

  25. The Teamware Annotation Project • Iterated between JAPE grammar development, manual annotation for gold-standard creation, measuring IAA and precision/recall for JAPE improvements • Initially gold standard doubly annotated until good IAA is obtained, then moved to 1 annotator per document • Had 15 annotators working at the same time GATE Summer School - July 27-31, 2009

  26. Measuring IAA with Teamware • Open Teamware • Find corpus patents-double-annotation • Measure IAA with the respective tool • Analyse the disagreements with the AnnDiff tool GATE Summer School - July 27-31, 2009

  27. Producing the Gold Standard • Selected patents from two very different fields: mechanical engineering and biomedical technology • 51 patents, 2.5 million characters • 15 annotators, 1 curator reconciling the differences GATE Summer School - July 27-31, 2009

  28. The Evaluation Gold Standard GATE Summer School - July 27-31, 2009

  29. Preliminary Results GATE Summer School - July 27-31, 2009

  30. Running GATE Apps on Millions of Documents • Processed 1.3 million patents in 6 days with 12 parallel processes. • Data sets from Matrixware: • American patents (USPTO): 1.3 million, 108 GB, average file size - 85KB. • European patents (EPO): 27 thousand, 780MB, average file size - 29KB. GATE Summer School - July 27-31, 2009

  31. Large-scale Parallel IE • Our experiments were carried out on the IRF’s supercomputer with Java (jrockit-R27.4.0-jdk1.5.0 12) with up to 12 processes • SGI Altix 4700 system comprising 20 nodes each with four 1.4GHz Itanium cores and 18GB RAM • In comparison, we found it 4x faster on Intel Core 2 2.4GHz GATE Summer School - July 27-31, 2009

  32. Large-Scale, Parallel IE (2) • GATE Cloud (A3): dispatches documents to process in parallel; does not stop on error • Ongoing project, moving towards Hadoop • Contact Hamish for further details • Benchmarking facilities: generate time stamps for each resource and display charts from them • Help optimising the IE pipelines, esp. JAPE rules • Doubled the speed of the patent processing pipeline • For a similar third-party GATE-based application we achieved a 10-fold improvement GATE Summer School - July 27-31, 2009

  33. Optimisation Results GATE Summer School - July 27-31, 2009

  34. MIMIR: Accessing the Text and the Semantic Annotations • Documents: 981,315 • Tokens: 7,228,889,715 (> 7 billion) • Distinct tokens: 18,539,315 (> 18m) • Annotation occurrences: 151,775,533 (> 151m) GATE Summer School - July 27-31, 2009

  35. GATE Summer School - July 27-31, 2009

  36. GATE Summer School - July 27-31, 2009

  37. GATE Summer School - July 27-31, 2009

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