Named entity extraction tools for raw OCR text

Named entity extraction tools for raw OCR text
Kepa J. Rodriguez
GCDH-colloquium
04.07.2012

Outline

• Context of the experiments at the EHRI project
• Description of the experiment
• Corpus data
• Creation and composition of the corpus
• Results of the NE extraction
• Conclusions

GCDH Colloquium – 11.07.2012

Context in the EHRI project

• Archival institutions have bigs amount of non digitized documents and
descriptions
• EHRI will provide its partners an OCR service that:
– Extracts text from image files of the documents
– Text can be used to index the documents and improve the quality of
the search
– Indexes can be later validated and improved by collection and
archive specialists
• What kind of indexes can be obtained from this noisy text?
• Quality of OCR transcripts in very low for humans, but … is it useful for
machines?


Experiment

• Evaluation of four existing NE extraction tools:
– Stanford NER
– OpenCalais
– OpenNLP
– Alchemy

• Extracted entity types: PER, LOC, ORG
– Good coverage by the selected tools.
– Highly relevant for Shoah research and contemporary historical
research in general.


Experiment

• Different tools use different annotation tagsets.
• Output has to be normalized
• Stanford NER and OpenNLP use Person, Location and Organization as
annotation categories.
– Direct mapping to PER, LOC and ORG
• OpenCalais:
– Country, City and NaturalFeature merged into LOC
– Organization and Facility into ORG
• Alchemy
– Organization, Facility and Company into ORG
– City and Continent into LOC


Corpus data

• Two datasets of type-writting monospaced text
• Wiener Library
– 17 pages of testimonies of Shoah survivors
– OCR word accuracy 93%
• King College London's Serving Soldier Archive
– 33 newsletters written for the crew of the warship H.M.S. Kelly
– OCR word accuracy 92.5%


Corpus data (WL)


Corpus data (WL)

¢3o
had been sold, and we dependedgxhe last night of our stay on the
friendliness of this neighbour. III!! The landlord Mr.and Mrs.
Wolkewitz, who had always gone out of their way to be kind to us,
had a collection arranged to us, and_wn finally left - on the
night of July 4-5, 1939 - all the tenqnts or the house had
assembled, and we all cried.
All people mentioned so for have either been friends or
acqndintanoes. There were others e.g. the grocer and the laundry
who refused payment before our departure, end there are two
indidente with German officials which I would like to tell:


Corpus data (KCL)


Corpus data (KCL)

:_
» I |“- _
li; A 1 U g _:__ L, £g!g;“' »
“K” D. F. NEws.,p
No. 24,~ "Monday, 18th September, 1959.
KELLY at Sea. _ ' P
KINGSTQN at portsmouth, Remainder of "K" Flotilla building.
THE "K" D.E. NEwS IS NCT To EE TAKEN ASHCRE NCR ARE ANY or ITS
CONTENTS To EE CCNRUNICATED CUTSIEE THE SHIP UNTIL THE MAR IS
OVER, wHEN ARRANGEMENTS CAN EE MADE To SUPPLY BACE CCPIES PCR
THE PRICE CR THE PAPER oN WHICH THEY ARE PRINTED.
`________________________as--sauna-__-as-_un-_._-»_.__--.`¢___.-_-
n__________..¢.__
THE KELLY'S HUNT - SEPTENEER Ietn/Ivtn,


Corpus data (KCL)
Although the events of Saturday night and Sunday
morning are Weil known to the KELLY shipis Company. they are
included here as being of interest to the rest of the Flotilla. `
Shortly after dark information was received which enabled
Course to be altered to close a German submarine on the surface.
Before the KELLY could arrive the submarine had dived, but a
Pemarkably good contact was obtained, and an att
C0ntact was maintained all night in order that the final attack
Sh0uld be carried out by daylight- Unfortunately no Oil, wreckage
'OP Survivors came to the surface, but air bUbb1€S appeared after the
1&St attack, which makes it possible, although by no means certain,
that the submarine was destroyed. - _
THE KINGSTON’S PROGRAIME. ~ -
Today the KINGSTON will be inspected by the Commander-
in~Chief, Portsmouth, and will then proceed to sea for acceptance


Construction of the corpus

• Generate two copies of each datasets
• Manual correction of one of the copies
– Used to evaluated the impact of the noise in the NE extraction
• Tokenization and POS tagging using TreeTagger
• Conversion of the TreeTagger output into stand-off standard XML.
• Import of the data into the MMAX2 annotation tool
• Manual annotation of the named entities
• Control of reliability of the annotation using the Kappa coeficient

• K = 0.93
• K > 0.8 is considered as reliable


Corpus data (KCL)

Wiener Library KCL

RAW Corrected Raw Corrected

Files 17 17 33 33
Words 4415 4398 16982 15693
PER 75 83 82 80
LOC 60 63 170 178
ORG 13 13 52 60
Total 148 159 305 319


Results of the NE extraction



Raw Corrected

P R F1 P R F1

AL 0.61 0.38 0.47 0.63 0.38 0.48
OC 0.75 0.29 0.41 0.69 0.30 0.42
ON 0.42 0.12 0.19 0.53 0.13 0.21
ST 0.57 0.52 0.54 0.60 0.61 0.60



• Low performance of the tools in corrected and raw text
• Our data and data used for training and evaluation of tools are quite
different.
• PER: non standard forms as
– [Last name, First name]
• “Wa1ter, Klaus”
– Parenthesis together with initials of the name
• “Captain (D)
– Some cases can be resolved using easy heuristics in preprocessing
• Names of persons and locations are used for other kind of entities:
• Warships have been annotated as PER



• Performance of extraction of entities of type ORG is very low
– F1 = between 0.11 & 0.32
– Name of organizations appear in non-standard forms
– Some of the organization don't exists and are not part of the
knowledge used to train the system.
• SS and other relevant nazi organizations have not be detected.
• Spelling errors and typos in the original files:
– OpenCalais used general knowledge to resolve this problem
– Use of general knowledge my be problematic.
• “Klan, Walter” → “Ku Klux Klan”


Conclusions

• Manual correction of OCR output does not improve significantly the
performance.
– Raw output is enough to obtain provisional index candidates
• Focus in near tearm:
– Identify most habitual patterns of error
– Implement preprocessing pipeline using simple heuristics and
pattern matching tools
• Focus in longer term:
– Use domain specific knowledge in form of authority files to validate
and correct the output of NE extraction tools.
– Explore the possibility of combining different NE extraction tools
and select output using a voting algorithm


Thanks


Named entity extraction tools for raw OCR text

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