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Evaluating an ‘off-the-shelf’

POS-tagger on Early Modern German text

Silke Scheible, Richard Jason Whitt, Martin Durrell, and Paul Bennett

The GerManC projectSchool of Languages, Linguistics, and Cultures

University of Manchester (UK)

Evaluating an ‘off-the-shelf’ POS-tagger on Early Modern German text

Overview

• Motivation• The GerManC corpus• POS-tagger and tagset• Challenges• Results

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Motivation

• Goal: – POS-tagged version of GerManC corpus

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Motivation


• Problems:– No specialised tagger available for EMG– Limited funds: Manual annotation not

feasible for whole corpus

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Motivation


• Problems:– No specialised tagger available for EMG– Limited funds: Manual annotation not

feasible for whole corpus

• Question:– How well does an ‘off-the shelf’ tagger for

modern German perform on Early Modern German data?

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Motivation

• Tagger evaluation requires gold standard data

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Motivation


• Idea: – Develop gold-standard subcorpus of

GerManC – Use subcorpus to test and adapt modern

NLP tools– Create historical text processing pipeline

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Motivation


• Idea: – Develop gold-standard subcorpus of

GerManC – Use subcorpus to test and adapt modern NLP

tools– Create historical text processing pipeline

• Results useful for other small humanities-based projects wishing to add POS annotations to EMG data

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The GerManC corpus

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The GerManC corpus

• Purpose: Studies of development and standardisation of German language

• Texts published between 1650 and 1800

• Sample corpus (2,000 words per text)• Total corpus size: ca. 1 million words• Aims to be “representative”

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The GerManC corpus

• Eight genres

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Orally-oriented

Print-oriented

DramasNewspapersLettersSermons

Narrative proseHumanities textsScience & medicine textsLegal texts


The GerManC corpus

• Three periods

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1650-1700

1700-1750

1750-1800


The GerManC corpus

• Five regions

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North German

West Central German

East Central German

West Upper German

East Upper German


The GerManC corpus

• Three 2,000-word files per genre/period/region

• Total size: ca. 1 million words

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Gold-standard subcorpus: GerManC-GS

• One 2,000-word file per genre and period from North German region 24 files

• > 50,000 tokens• Annotated by two historical linguists• Gold standard POS tags, lemmas, and

normalised word forms

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POS-tagger

• TreeTagger (Schmid, 1994)• Statistical, decision tree-based POS

tagger• Parameter file for modern German

supplied with the tagger• Trained on German newspaper corpus• STTS tagset

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STTS-EMG

1. PIAT (merged with PIDAT): Indefinite determiner, as in ‘viele solche Bemerkungen’

(‘many such remarks’)

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STTS-EMG

2. NA: Adjectives used as nouns, as in ‘der Gesandte’ (‘the ambassador’)

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STTS-EMG

3. PAVREL: Pronominal adverb used as relative, as in ‘die Puppe, damit sie spielt’ (‘the doll with which she plays’)4. PTKREL: Indeclinable relative particle, as in‘die Fälle, so aus Schwachheit entstehen’ (‘the cases which arise from weakness’)

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STTS-EMG

5. PWAVREL: Interrogative adverb used as relative, as

in ‘der Zaun, worüber sie springt’(‘the fence over which she jumps’)6. PWREL: Interrogative pronoun used as relative,

as in ‘etwas, was er sieht’ (‘something which he sees’)

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POS-tagging in GerManC-GS

• New categories account for 2% of all tokens

• IAA on POS-tagging task: 91.6%

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Challenges: Tokenisation issues

• Clitics:– hastu: hast du

(‘have you’)- wirstu: wirst du

(‘will you’)

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• Clitics:– has|tu: hast du

(‘have you’)- wirs|tu: wirst du

(‘will you’)

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(‘will you’)

• Multi-word tokens:– obgleich vs. ob gleich

(‘even though’)

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(‘will you’)

• Multi-word tokens:– obgleich/KOUS vs. ob/KOUS gleich/ADV

(‘even though’)

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Challenges: Spelling variation

• Spelling not standardised:– Comet Komet– auff auf– nachdeme nachdem– ko�mpt kommt– Bothenbrodt Botenbrot– differiret differiert– beßer besser– kehme käme– trucken trockenen– gepressett gepreßt– büxen Büchsen

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• All spelling variants in GerManC-GS normalised to a modern standard Assess what effect spelling variation has on the performance of automatic tools Help improve automated processing?

• Important for:–Automatic tools (POS tagger!)–Accurate corpus search

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Proportion of normalised word tokens plotted against time28


Questions

• What is the “off-the-shelf” performance of the TreeTagger on historical data from the EMG period?

• Can the results be improved by running the tool on normalised data?

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Results

Original data Normalised data

Accuracy

69.6% 79.7%

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TreeTagger accuracy on original vs. normalised input


Improvement through normalisation over time

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Tagger performance plotted against publication date


Effects of spelling normalisation on POS tagger performance

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For normalised tokens: Effect of using original (O)/normalised (N) input on tagger accuracy

+: correctly tagged; -: incorrectly tagged


Comparison with “modern” results

• Performance of TreeTagger on modern data: ca. 97% (Schmid, 1995)

• Current results seem low

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• Current results seem low• But:– Modern accuracy figure: evaluation of

tagger on the text type it was developed on (newspaper text)

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• Current results seem low• But:– Modern accuracy figure: evaluation of

tagger on the text type it was developed on (newspaper text)

– IAA higher for modern German (98.6%)

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Conclusion

• Substantial amount of manual post-editing required

• Normalisation layer can improve results by 10%, but so far only half of all annotations have positive effect

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Future work

• Adapt normalisation scheme to account for more cases

• Automate normalisation (Jurish, 2010)• Retrain state-of-the-art POS taggers Evaluation?• Provide detailed information about

annotation quality to research community

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Thank you!

[email protected]

[email protected]@[email protected]

http://tinyurl.com/germanc