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

2. University of Sheffield NLP 2GATE Summer School - July 27-31, 2009 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

3. University of Sheffield NLP 3GATE Summer School - July 27-31, 2009 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

4. University of Sheffield NLP 4GATE Summer School - July 27-31, 2009 Semantic Annotation

5. University of Sheffield NLP 5GATE Summer School - July 27-31, 2009 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.

6. University of Sheffield NLP 6GATE Summer School - July 27-31, 2009 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.

7. University of Sheffield NLP 7GATE Summer School - July 27-31, 2009 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

8. University of Sheffield NLP 8GATE Summer School - July 27-31, 2009 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)

9. University of Sheffield NLP 9GATE Summer School - July 27-31, 2009 Technologies Teamware GATEOWLIM TRREE JBPM, etc… Data Enrichment (Semantic Annotation) KIM Knowledge Management GATEOWLIM TRREE Lucene, etc… Data Access (Search/Browsing) GATEORDI TRREE MG4J, etc… Large Scale Hybrid Index SheffieldOntotextOther

10. University of Sheffield NLP 10GATE Summer School - July 27-31, 2009 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.

11. University of Sheffield NLP 11GATE Summer School - July 27-31, 2009 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.

12. University of Sheffield NLP 12GATE Summer School - July 27-31, 2009 Data Model

13. University of Sheffield NLP 13GATE Summer School - July 27-31, 2009 Example Bibliographic Data

14. University of Sheffield NLP 14GATE Summer School - July 27-31, 2009 Example measurements

15. University of Sheffield NLP 15GATE Summer School - July 27-31, 2009 Example References

16. University of Sheffield NLP 16GATE Summer School - July 27-31, 2009 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

17. University of Sheffield NLP 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]...... [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. University of Sheffield NLP 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 deposition rates up to 20 [nm/sec] a fatigue life of 400 MM [cycles] ratio is approximately 9[:]7 Annotating Measurement Units

19. University of Sheffield NLP Annotation Schemas: Measurements Example

20. University of Sheffield NLP 20GATE Summer School - July 27-31, 2009 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

21. University of Sheffield NLP 21GATE Summer School - July 27-31, 2009 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}

22. University of Sheffield NLP 22GATE Summer School - July 27-31, 2009 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"}

23. University of Sheffield NLP 23GATE Summer School - July 27-31, 2009 The IE Annotation Pipeline

24. University of Sheffield NLP 24GATE Summer School - July 27-31, 2009 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”

25. University of Sheffield NLP 25GATE Summer School - July 27-31, 2009 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

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

27. University of Sheffield NLP 27GATE Summer School - July 27-31, 2009 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

28. University of Sheffield NLP 28GATE Summer School - July 27-31, 2009 The Evaluation Gold Standard

29. University of Sheffield NLP 29GATE Summer School - July 27-31, 2009 Preliminary Results

30. University of Sheffield NLP 30GATE Summer School - July 27-31, 2009 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.

31. University of Sheffield NLP 31GATE Summer School - July 27-31, 2009 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

32. University of Sheffield NLP 32GATE Summer School - July 27-31, 2009 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

33. University of Sheffield NLP 33GATE Summer School - July 27-31, 2009 Optimisation Results

34. University of Sheffield NLP 34GATE Summer School - July 27-31, 2009 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)

35. University of Sheffield NLP 35GATE Summer School - July 27-31, 2009

36. University of Sheffield NLP 36GATE Summer School - July 27-31, 2009

37. University of Sheffield NLP 37GATE Summer School - July 27-31, 2009