Cost-Effective Information Extraction from Lists in OCRed Historical Documents Thomas Packer and David W. Embley Brigham Young University FamilySearch.

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Presentation transcript:

Cost-Effective Information Extraction from Lists in OCRed Historical Documents Thomas Packer and David W. Embley Brigham Young University FamilySearch International

Information Extraction Issues 150K scanned books + 25K/yr ~ 7.5B fact assertions 12M Jiapu images ~ 0.5B fact assertions + many more + …

Information Extraction Issues 150K scanned books + 25K/yr ~ 7.5B fact assertions 12M Jiapu images ~ 0.5B fact assertions + many more + …

ListReader Scott, Archibald, par. of Largs, and Elizabeth …

ListReader Scott, Archibald, par. of Largs, and Elizabeth …

ListReader Scott, Archibald, par. of Largs, and Elizabeth …

ListReader Scott, Archibald, par. of Largs, and Elizabeth …

Text Abstraction [\n][UpLo],[Sp][Dg][Sp][UpLo].[Sp][DgDgDgDg].[\n] … [\n][  ][\n][DgDg][Sp][UpLo][Sp][of][Sp][UpLo].[\n][-][\n] [UpLo],[Sp][UpLo],[Sp][in][Sp][UpLo][\n][UpLo],[Sp][Dg][Sp] [UpLo].[Sp][DgDgDgDg].[\n][UpLo],[Sp][DgDg][Sp][UpLo].[Sp] [DgDgDgDg].[\n][UpLo],[Sp][UpLo],[Sp][in][Sp][UpLo][Sp] [UpLo][\n][UpLo],[Sp][DgDg][Sp][UpLo][Sp][DgDgDgDg].[\n] [UpLo],[Sp][UpLo],[Sp][of][Sp][UpLo],[Sp][and][Sp][UpLo] …

Candidate Record Clusters [\n][Sp][UpLo],[Sp][DgDg][Sp][UpLo].[Sp][DgDgDgDg].[Sp][\n] Record instance count: 1296 \nJames, 15 Dec \n \nRobert, 15 Oct \n... [\n][Sp][UpLo+],[Sp][DgDg][Sp][UpLo+][Sp][DgDgDgDg].[Sp][\n] Record instance count: 710 \nJoan, 25 April 1651.\n \nJohn, 30 May 1652.\n... [\n][Sp][UpLo],[Sp][born][Sp][DgDg][Sp][UpLo].[Sp][DgDgDgDg].[Sp][\n] Record instance count: 441 \nWilliam, born 10 Dec \n \nJames, born 24 Oct \n... [\n][Sp][UpLo],[Sp][UpLo],[Sp][and][Sp][UpLo][Sp][m].[Sp][DgDg][Sp][UpLo].[Sp][DgDgDgDg][Sp][\n] Record instance count: 61 \nAiken, David, and Janet Stevenson m. 29 Sept. 1691\n \nAitkine, Thomas, and Geills Ore m. 21 Dec. 1661\n...

Record and Field Group Templates [[\n-Segment][\n-End-Segment]] [\n-Segment] \n[UpLo],[Sp][DgDg][Sp][UpLo][Sp][DgDgDgDg] \nRobert, 12 May 1661 \n[UpLo],[Sp][UpLo] \nAllasoun, Richard \n[UpLo] \nLochwinnoch [\n-End-Segment].\n :.\n \n : \n [[\n-Segment][born-Segment][\n-End-Segment]] [\n-Segment] \n[UpLo] \nJanet [born-Segment],[Sp][born][Sp][DgDg][Sp][UpLo].[Sp][DgDgDgDg], born 23 Oct [\n-End-Segment].\n :.\n \n : \n

HMM Fragment

Full HMM

Labeling First labeling Second labeling (only the period) Third labeling... Fourth labeling...

Test Set Characteristics CharacteristicShaverKilbarchan Pages Labeled pages683 Labeled tokens14, Labeled field instances13, Record instances2, Field types4612 Ground Truth 3,284Records 14,516Instance predicates 11,232Relationship predicates 25,748Predicates

Learning Curve Results KilbarchanShaver PrecisionRecall F1F1 Precision Recall F1F1

PrecisionRecall 1F11F1 CRF ListReader (Regex) ListReader (HMM) Area under Learning Curve Metrics (%) Kilbarchan Parish Record Shaver-Doughterty Genealogy PrecisionRecall 1F11F1 CRF ListReader (Regex) ListReader (HMM) Results statistically significant at p<0.05 Except Recall of ListReader-Regex & CRF Except Precision of ListReader Regex & HMM and Recall of ListReader-Regex & CRF

Space / Time Characteristics ListReader HMM Regex CRF Extractor Size # states# chars.# states Shaver2,015319,09628 Kilbarchan25554,60015 Running Time Shaver59m 18s2m 47s52s Kilbarchan2m 11s26s9s

ListReader Status Limitations Only semi-structured text No nested record structures Future Work Pragmatic adjustments Ensemble integration Text abstraction wrt ontological concepts Reuse discovered patterns from one book to another Discovery of nested-record patterns

Conclusion Unsupervised HMM construction Cost minimization of labeling Good performance: Accuracy Labeling cost Time and space complexity Required knowledge engineering

Conclusion Unsupervised HMM construction Cost minimization of labeling Good performance: Accuracy Labeling cost Time and space complexity Required knowledge engineering BYU Data Extraction Research Group

Fe6: 1. Prepare 2. Extract 3. Split & Merge 4. Check & Correct 5. Generate 6. Convert FROntIER ListReader OntoSoar GreenFIE COMET

Fe6: 1. Prepare 2. Extract 3. Split & Merge 4. Check & Correct 5. Generate 6. Convert FROntIER ListReader OntoSoar GreenFIE Feedback Loop Automated Check & Correct “Sanity” Check Name, Date, Place Standardization Administrative and Batch-Processing Management System COMET

Fe6: 1. Prepare 2. Extract 3. Split & Merge 4. Check & Correct 5. Generate 6. Convert FROntIER ListReader OntoSoar GreenFIE Feedback Loop Automated Check & Correct “Sanity” Check Name, Date, Place Standardization Administrative and Batch-Processing Management System Bootstrapping, Ever-learning, Feedback Loop Extraction Tools: Layout Machine Learning Non-English Languages COMET