The ODU Metadata Extraction Project March 28, 2007 Dr. Steven J. Zeil zeil@cs.odu

transcript

The ODU Metadata Extraction Project

March 28, 2007

Dr. Steven J. Zeil

zeil@cs.odu.edu

Outline

1. Overview2. Recent Developments

A. Independent Document ModelB. ValidationC. Diversifying – NASA & GPO

collections3. New Issues & Future Directions

A. Post-processingB. Image-Based Classification

1. OverviewInput

Documents

Input Processing &

Form Processing

Final Metadata

Output

XML model of document

Unresolved Documents

Extracted Metadata

CleanedMetadata

sf298_1 sf298_2 ...

Form Templates

au eagle ...

Nonform Templates

Post Processing

Nonform Processing

Extracted Metadata

Validation

trusted outputs

Untrusted Metadata Outputs

Human Review & Correction

correctedmetadata

Input Processing & OCR

• Select pages of interest

• Apply Off-The-Shelf OCR software

• Convert OCR output to XML model format

Form Processing

• Scan document for form names– Select form template

• Apply form extraction engine to document and template

Sample RDP

Sample RDP (cont.)

Metadata Extracted from Sample RDP (1/3)

<DescriptiveNote>Final Report</DescriptiveNote>

<DescriptiveNote>1 April 1996 - 31 August 2003</DescriptiveNote>

<UnclassifiedTitle>VALIDATION OF IONOSPHERIC MODELS</UnclassifiedTitle>

<PersonalAuthor>Patricia H. Doherty Leo F. McNamara

Susan H. Delay Neil J. Grossbard</PersonalAuthor>

<CorporateAuthor>Boston College / Institute for Scientific Research 140 Commonwealth Avenue Chestnut Hill, MA 02467-3862</CorporateAuthor>

<ReportNumber></ReportNumber> <MonitorNameAndAddress>Air Force Research Laboratory 29 Randolph Road

Hanscom AFB, MA 01731-3010</MonitorNameAndAddress> <MonitorAcronym>VSBP</MonitorAcronym> <MonitorSeries>AFRL-VS-TR-2003-1610</MonitorSeries> <DistributionStatement>Approved for public release; distribution

unlimited.</DistributionStatement> <Abstract>This document represents the final report for work performed under the Boston College contract F I9628-96C-0039. This contract was entitled Validation of Ionospheric Models. The objective of this contract was to obtain satellite and ground-based ionospheric measurements from a wide range of geographic locations and to utilize the resulting databases to validate the theoretical ionospheric models that are the basis of the Parameterized Real-time Ionospheric Specification Model (PRISM) and the Ionospheric Forecast Model (IFM). Thus our various efforts can be categorized as either observational databases or modeling studies.</Abstract>

<Identifier>Ionosphere, Total Electron Content (TEC), Scintillation, Electron density, Parameterized Real-time Ionospheric Specification Model (PRISM), Ionospheric Forecast Model (IFM), Paramaterized Ionosphere Model (PIM), Global Positioning System

(GPS)</Identifier> <ResponsiblePerson>John Retterer</ResponsiblePerson> <Phone>781-377-3891</Phone> <ReportClassification>U</ReportClassification>

Non-Form Processing

• Classification – compare document against known document layouts– Select template written for closest matching

layout

• Apply non-form extraction engine to document and template

Non-Form Sample (1/2)

Non-Form Sample (2/2)

Template Used for Sample Document

<structdef pagenumber="1" templateID="au"> <identifier min="1" max="1"> <begin inclusive="current"> <stringmatch case="yes" loc="beginwith">AU/</stringmatch> </begin> <end>onesection</end> </identifier> <CorporateAuthor min="1" max="1"> <begin inclusive="current"> <stringmatch case="no" loc="beginwith">

AIR COMMAND | AIR WAR </stringmatch> </begin> <end inclusive="current"> <stringmatch case="no" loc="beginwith">AIR UNIVERSITY</stringmatch> </end> </CorporateAuthor> <UnclassifiedTitle min="1" max="1"> <begin inclusive="after">CorporateAuthor</begin> <end inclusive="before"> <stringmatch case="no" loc="beginwith">by</stringmatch> </end> </UnclassifiedTitle>…

Metadata Extracted From the Title Page of the Sample Document

<paper templateid="au"> <identifier>AU/ACSC/012/1999-04</identifier> <CorporateAuthor>AIR COMMAND AND STAFF COLLEGE AIR UNIVERSITY</CorporateAuthor> <UnclassifiedTitle>INTEGRATING COMMERCIAL ELECTRONIC EQUIPMENT TO IMPROVE MILITARY CAPABILITIES </UnclassifiedTitle> <PersonalAuthor>Jeffrey A. Bohler LCDR, USN</PersonalAuthor> <advisor>Advisor: CDR Albert L. St.Clair</advisor> <ReportDate>April 1999</ReportDate></paper>

Post-Processing

• Coerce extracted values into standard formats

Validation

• Estimate quality of extracted metadata

• Untrusted outputs referred (to humans) for review and correction

Recent Developments

A. Independent Document Model

B. Validation

C. Diversifying – NASA and GPO Collections

A. Independent Document Model (IDM)

• Platform independent Document Model • Motivation

– Dramatic XML Schema Change between Omnipage 14 and 15

– Tie the template engine to stable specification– Protects from linking directly to specific OCR product– Allows us to include statistics for enhanced feature

usage• Statistics (i.e. avgDocFontSize, avgPageFontSize,

wordCount, avgDocWordCount, etc..)

Documents in IDM

• A document consists of pages• pages are divided into regions• regions may be divided into

– blocks of vertical whitespace– paragraphs– tables– images

• paragraphs are divided into lines• lines are divided into wordsAll of these carry standard attributes for size,

position, font, etc.

Generating IDM

• Use XSLT 2.0 stylesheets to transform– Supporting new OCR schema only requires

generation of new XSLT stylesheet. -- Engine does not change

IDM Usage

OmniPage 14 XML Doc

OmniPage 15 XML Doc

Other OCR Output XML Doc

IDM XML Doc

Form Based Extraction

Non Form ExtractiondocTreeModelOther.xsl

docTreeModelOmni15.xsl

docTreeModelOmni14.xsl

IDM Tool Status

• Converters completed to generate IDM from Omnipage 14 and 15 XML– Omnipage 15 proved to have numerous errors in its

representation of an OCR’d document

– Consequently, not recommended

• Form-based extraction engine revised to work from IDM• Non-form engine still works from our older “CleanXML”

– convertor from IDM to CleanXML completed as stop-gap measure

– direct use of IDM deferred pending review of other engine modifications

B. Validation

• Given a set of extracted metadata– mark each field with a confidence value indicating how

trustworthy the extracted value is– mark the set with a composite confidence score

• Fields and Sets with low confidence scores may be referred for additional processing– automated post-processing– human intervention and correction

Validating Extracted Metadata

• Techniques must be independent of the extraction method

• A validation specification is written for each collection, combining

• Field-specific validation rules– statistical models derived for each field of

• text length• % of words from English dictionary• % of phrases from knowledge base prepared for

that field– pattern matching

Sample Validation Specification

• Combines results from multiple fields<val:validate collection="dtic"

xmlns:val="jelly:edu.odu.cs.dtic.validation.ValidationTagLibrary">

<val:average>

<val:field name="UnclassifiedTitle">...</val:field>

<val:field name="PersonalAuthor">...</val:field>

<val:field name="CorporateAuthor">...</val:field>

<val:field name="ReportDate">...</val:field>

</val:average>

</val:validate>

Validation Spec: Field Tests

• Each field is subjected to one or more tests…<val:field name="PersonalAuthor"> <val:average> <val:length/> <val:max>

<val:phrases length="1"/> <val:phrases length="2"/> <val:phrases length="3"/>

</val:max> </val:average> </val:field><val:field name="ReportDate"> <val:reportFormat/></val:field>...

Sample Input Metadata Set

<UnclassifiedTitle>Thesis Title: The Military Extraterritorial Jurisdiction Act</UnclassifiedTitle>

<PersonalAuthor>Name of Candidate: LCDR Kathleen A. Kerrigan</PersonalAuthor>

<ReportDate>Accepted this 18th day of June 2004 by:</ReportDate>

</metadata>

Sample Validator Output

<metadata confidence="0.522"><UnclassifiedTitle confidence="0.943">Thesis Title: The Military Extraterritorial Jurisdiction Act</UnclassifiedTitle>

<PersonalAuthor confidence="0.622">Name of Candidate: LCDR Kathleen A. Kerrigan</PersonalAuthor>

<ReportDate confidence="0.0" warning="ReportDate field does not match required pattern">Accepted this 18th day of June 2004 by:</ReportDate>

</metadata>

Classification (a priori)

Classify (select best template)

Final Nonform Output

CleanXML

Extracted Metadata

au eagle ...

Nonform Templates

Unresolved Document

Extract Metadata

selectedtemplate

• Previously, we had attempted various schemes for a priori classification– x-y trees– bin classification

• Still investigating some– image-based recognition

Post-Hoc Classification

• Apply all templates to document– results in multiple candidate sets of metadata

• Score each candidate using the validator– Select the best-scoring set

Extract Metadata

Final Nonform Output

CleanXML

Selected Metadata

au eagle ...

Nonform Templates

Unresolved Document

Select Best Metadata

CandidateMetadata

Validation Spec.

validation rules

Experimental Results

Manually Assigned

Number of Documents

Validator Preferred Total

Au 86 0 0 0 86

Eagle 0 8 33 4 45

Rand 0 0 8 4 12

Title 0 0 1 23 24

Interpretation of Results

• Validator agreed with human on 125 out of 167 cases

• Of 42 cases where they disagreed– 37 were due to “extra” words in extracted metadata

(e.g., military ranks in author names)• highlights need for post-processing to clean up metadata

– 2 were mistakes by template– 2 were due to garbled characters by OCR– 1 due to a bug in the validator

C. Diversifying – NASA and GPO Collections

Document collections differ in

• whether forms are used and form layout

• document layout

• what metadata fields are present & which ones are collected

Changing Collections

• Porting to a new document collection– identify pages of interest– training classifiers to recognize new document layouts

(?)– templates for forms & document layouts– new validation scripts

• collect statistics for collection model

– new post-processing rules

• No changes required to core engines & other software

NASA Technical Reports

• Different layouts than DTIC– fewer total– tend to be visually more similar– mixture with and without RDPs

NASA Sample Document

Extracted Metadata for NASA Sample

<paper templateid="singleAuthor"> <metadata> <UnclassifiedTitle> A Computationally Efficient Meshless Local Petrov-Galerkin Method for Axisymmetric Problems </UnclassifiedTitle> <PersonalAuthor> I.S. Raju* and T. Chen? </PersonalAuthor> <CorporateAuthor> NASA Langley Research Center Hampton, VA 23681 </CorporateAuthor> <Abstract> The Meshless Local Petrov-Galerkin (MLPG) method is one of the recently developed element-free …

Govt. Printing Office

• Congressional acts & reports• EPA reports Preliminary study with Acts

of Congress and EPA reports

• samples suggest layouts are more diverse than DTIC or NASA– metadata actually present in document varies

widely

GPO Sample – Act of Congress

Metadata Extracted for Act of Congress

<paper> <metadata> <public_law_report_num> 118 STAT. 3984 PUBLIC LAW 108?493?DEC. 23, 2004 </public_law_report_num> <bill_number>[H.R. 5394 ] components.</bill_number> <congress_num>108th Congress</congress_num> <type>An Act</type> <acttype> Dec. 23, 2004 To amend the Internal Revenue Code of 1986 to modify the

taxation of arrow [H.R. 5394 ] components. </acttype> </metadata></paper>

GPO sample report

Metadata Extracted from GPO Sample Report

<paper> <metadata> <title> CHINA?S PROLIFERATION PRACTICES AND ROLE IN THE NORTH KOREA CRISIS </title> <type> HEARING BEFORE THE U.S.-CHINA ECONOMIC AND SECURITY REVIEW COMMISSION </type> <session>ONE HUNDRED NINTH CONGRESS FIRST SESSION</session> <date>MARCH 10, 2005</date> <use> Printed for the use of the U.S.-China Economic and Security Review Commission </use> <online>Available via the World Wide Web: http://www.uscc.gov</online> </metadata></paper>

3. New Issues and Future Directions

A. Post-Processing

B. Image-Based Classification

Post-processing

• WYSIWYG

• WYG != WYW

Post-processing

• WYSIWYG– What You See is What You Get

• WYG != WYW

Post-processing

• WYSIWYG– What You See is What You Get

• WYG != WYW– What You Get is not What You Want

Example – DTIC Date Format

• Document may contain:– March 28, 2007– 3/28/2007– 3/28/07

• DTIC requires:– 28 MAR 2007

Example – Personal Authors

Example – Personal Authors (cont.)

• We extract: <PersonalAuthor>Patricia H. Doherty Leo F. McNamara Susan H.

Delay Neil J. Grossbard</PersonalAuthor>

• DTIC requires:<PersonalAuthor>Patricia H. Doherty ;Leo F. McNamara ;Susan H.

Delay ;Neil J. Grossbard</PersonalAuthor>

• NASA requires<author>Patricia H. Doherty</author><author>Leo F. McNamara</author> <author>Susan H. Delay</author><author>Neil J. Grossbard</author>

Post-Processing Requirements

• Post-processing rules must vary by– metadata field

– collection

Post-Processing Architecture

D TI CPo s tPro ce s s o r

M e ta da ta Po s tPro ce s s o r

re g is t e r (t a g , p ro c e s s o r)

NA S APo s tPro ce s s o r

G POPo s tPro ce s s o r

reg is te re d

Ta g Pro ce s s o rs

D TI CPe rs o n a lA u th o rs

NA S APe rs o n a lA u th o rs

D TI C D a te s

Image-Based Classification

• filter to find likely candidates for validator-based selection of template

• Looking at a variety of techaniques inspired by work in image recognition

Example: Image-Based Classification

• Example: represent a page using various colors to denote images, text, bold text, etc.

• find visually most similar pages in documents of known classes– “vote” based on 5 most similar

documents

Visual Matching Example (1/2)

Visual Matching Example (2/2)

Conclusions

• Automated metadata extraction can be performed effectively on a wide variety of documents– Coping with heterogeneous collections is a

major challenge

• Much attention must be paid to “support” issues– validation, post-processing, etc.