patent processing with gate kalina bontcheva, valentin tablan university of sheffield

Patent Processing with GATE

Kalina Bontcheva, Valentin Tablan University of Sheffield

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University of Sheffield NLP

GATE 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

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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

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Semantic Annotation

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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.

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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.

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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

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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)

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Technologies

Teamware

GATE OWLIM

TRREEJBPM, etc…

Data Enrichment(Semantic Annotation)

KIM

Knowledge Management

GATE OWLIM

TRREELucene,

etc…

Data Access(Search/Browsing)

GATE ORDI

TRREEMG4J,etc…

Large ScaleHybrid Index

Sheffield Ontotext Other

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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.

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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.

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Data Model

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Example Bibliographic Data

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Example measurements

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Example References

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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



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] ...



• 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



<?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>

Annotation Schemas: Measurements Example

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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

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Example JAPEs for References

Macro: FIGNUMBER //Numbers 3, 45, also 3a, 3b( {Token.kind == "number"} ({Token.length == "1",Token.kind == "word"})?)

Rule:IgnoreFigRefsIfTherePriority: 1000( {Reference.type == "Figure"} )--> {}

Rule:FindFigRefsPriority: 50( (

({Token.root == "figure"} | {Token.root == "fig"}) ({Token.string == "."})? ((FIGNUMBER) | (FIGNUMBERBRACKETS) ):number ):figref)-->

:figref.Reference = {type = "Figure", id = :number.Token.string}

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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"}

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The IE Annotation Pipeline

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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”

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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

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Measuring IAA with Teamware

• Open Teamware

• Find corpus patents-double-annotation

• Measure IAA with the respective tool

• Analyse the disagreements with the AnnDiff tool

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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

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The Evaluation Gold Standard

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Preliminary Results

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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.

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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

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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

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Optimisation Results

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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)

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patent processing with gate kalina bontcheva, valentin tablan university of sheffield

Documents

semantic annotation

university of sheffield

gate summer school

patent annotation

annotation haystack

sheffield gate team

annotation guidelines

annotation help