POS Tagging for Historical Texts with Sparse Training Data

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1 Motivation POS Tagging for Historical Texts with Sparse Training Data Department of Linguistics Ruhr-University Bochum, Germany The 7th Linguistic Annotation Workshop & Interoperability with Discourse August 8 9, 2013, Sofia, Bulgaria

2 Motivation Motivation Goal (Semi-)Automatic annotation of historical texts The (main) problem with historical data... High variance in spelling None or very little training data to retrain existing tools woman vrowe vrouwe fraw frouw frauwe

3 Motivation Motivation Goal (Semi-)Automatic annotation of historical texts The (main) problem with historical data... High variance in spelling None or very little training data to retrain existing tools A possible solution... Spelling normalization as a preprocessing step woman vrowe vrouwe fraw frouw frauwe frau

4 Outline Data Motivation 1 Data Anselm Corpus GerManC-GS Corpus 2 Method & Procedure Results 3 Capitalization & Punctuation Results 4

5 Anselm Corpus GerManC-GS Corpus Anselm Corpus Collection of Early New High German (ENHG) texts Interrogatio Sancti Anselmi de Passione Domini (Questions by Saint Anselm about the Lord s Passion) More than 50 manuscripts and prints (in German) 14 th 16 th centuries Various German dialects Sample from an Anselm manuscript

6 Anselm Corpus Data Anselm Corpus GerManC-GS Corpus ENHG 1 do meín chind híet geezzen... ENHG 2 Do my kynt hatte geſzen... ENHG 3 do mín kínt hatt geſſen... Norm da mein kind hatte gegessen... as my child had eaten

7 Anselm Corpus GerManC-GS Corpus GerManC-GS Corpus GerManC Created at the University of Manchester Representative corpus of historical, written German from 1650 to 1800 Different dialectal regions and text genres GerManC-GS Subcorpus of GerManC with gold standard annotations, lemmatization, POS

8 GerManC-GS Corpus Anselm Corpus GerManC-GS Corpus Serm 1 es ist ein k e ostlich Ding, Dir dancken Norm es ist ein köstliches Ding, dir (zu) danken it is an exquisite thing to thank you Serm 2 Norm gieb meinen Worten das Feuer, das die Herzen entz e undet gib meinen Worten das Feuer, das die Herzen entzündet give my words the fire to ignite hearts

9 Texts used for the evaluation Anselm Corpus GerManC-GS Corpus Corpus Date Name Tokens Anselm GerManC-GS 15c Berlin 5,399 15c Melk 4, LeichSermon 2, JubelFeste 2, Gottesdienst 2,292

10 Method & Procedure Results methods Described previously in Bollmann (2012) Combination of different normalization methods 1 Wordlist mapping 2 Rule-based normalization Character rewrite rules 3 Distance-based normalization Weighted Levenshtein distance

11 procedure Method & Procedure Results Training & evaluation parts as subsets from the same text How much training data is needed? Different sizes of the training parts Random sub-sampling n tokens for training 1,000 tokens for evaluation Average of 10 random training & evaluation sets

12 accuracy Method & Procedure Results Text Baseline s ,000 Berlin 23.05% 68.99% 75.02% 79.14% 81.83% Melk 39.32% 69.10% 74.39% 75.74% 77.98% LeichSermon 72.71% 77.96% 80.51% 82.85% 87.23% JubelFeste 79.47% 88.50% 89.98% 91.87% 93.13% Gottesdienst 83.41% 93.77% 95.24% 95.27% 95.56%

13 accuracy Method & Procedure Results Text Baseline s ,000 Berlin 23.05% 68.99% 75.02% 79.14% 81.83% Melk 39.32% 69.10% 74.39% 75.74% 77.98% LeichSermon 72.71% 77.96% 80.51% 82.85% 87.23% JubelFeste 79.47% 88.50% 89.98% 91.87% 93.13% Gottesdienst 83.41% 93.77% 95.24% 95.27% 95.56%

14 accuracy Method & Procedure Results Text Baseline s ,000 Berlin 23.05% 68.99% 75.02% 79.14% 81.83% Melk 39.32% 69.10% 74.39% 75.74% 77.98% LeichSermon 72.71% 77.96% 80.51% 82.85% 87.23% JubelFeste 79.47% 88.50% 89.98% 91.87% 93.13% Gottesdienst 83.41% 93.77% 95.24% 95.27% 95.56%

15 Capitalization & Punctuation Results How good is POS tagging on spelling-normalized data?

16 Capitalization & Punctuation Results Spelling variation is not the only problem... Inconsistent or missing capitalization Inconsistent or missing punctuation marks Extinct wordforms Syntactic pecularities...?

17 Capitalization & Punctuation Results Spelling variation is not the only problem... Inconsistent or missing capitalization Inconsistent or missing punctuation marks Extinct wordforms Syntactic pecularities...?

18 Capitalization & Punctuation Results Tagging with handicaps on modern data Combination of two modern German corpora: TIGER corpus (Brants et al., 2002) Tüba-D/Z version 6 (Telljohann et al, 2004) Original 96.85% Lowercased 96.50% No punctuation and SB 96.22% Lowercased + no punctuation and SB 95.74% Tagging accuracy with 10-fold CV, using RFTagger (Schmid and Laws, 2008)

19 Capitalization & Punctuation Results Tagging with handicaps on modern data Combination of two modern German corpora: TIGER corpus (Brants et al., 2002) Tüba-D/Z version 6 (Telljohann et al, 2004) Original 96.85% Lowercased 96.50% No punctuation and SB 96.22% Lowercased + no punctuation and SB 95.74% Tagging without capitalization/punctuation is viable

20 Tagging on historical data Capitalization & Punctuation Results Text Orig. Automatically normalized Gold ,000 Berlin 28.65% 58.68% 74.89% 75.95% 78.03% 87.07% Melk 44.70% 69.63% 74.02% 76.24% 78.66% 87.74% LeichSermon 67.95% 72.87% 74.63% 75.85% 78.01% 81.04% JubelFeste 82.26% 82.64% 83.62% 86.52% 87.74% 90.03% Gottesdienst 88.07% 88.84% 90.27% 91.30% 91.65% 92.27%

21 Tagging on historical data Capitalization & Punctuation Results Average accuracy (%) POS Tagging Average accuracy (%) POS Tagging Size of training part (Tokens) Size of training part (Tokens) Melk JubelFeste

22 Problems that remain... Capitalization & Punctuation Results Extinct wordforms vn machot in zehant geſvnt. und macht ihn sofort gesund and cures him immediately

23 Problems that remain... Capitalization & Punctuation Results Extinct wordforms vn machot in zehant geſvnt. und macht ihn sofort gesund and cures him immediately Syntactic/semantic variation die faelle so aus schwacheit geschehen die fälle so/die? aus schwachheit geschehen the cases that occur out of weakness

24 Problems that remain... Capitalization & Punctuation Results Domain adaptation sieh anselm Look, Anselm! Imperative verb forms rare in modern corpora TIGER/Tüba: 0.02% Berlin text: 0.91% Religious vocabulary

25 Conclusion Data Conclusion Automatic annotation of historical data Dealing with spelling variation via normalization Small amounts of training data already very beneficial, e.g. from 23% to 69% accuracy with 100 tokens for training POS tagging on data without capitalization, punctuation, and sentence boundaries Only minor impact on accuracy (1.1% on modern data) Syntactic/semantic variation and domain adaptation remain obstacles for improving the results

26 Conclusion Thank you for listening!

27 References Data References Details Bollmann, M. (2012). (Semi-)automatic normalization of historical texts using distance measures and the Norma tool. In Proceedings of ACRH-2, Lisbon, Portugal. Brants, S., Dipper, S., Hansen, S., Lezius, W., & Smith, G. (2002). The TIGER treebank. In E. Hinrichs & K. Simov (Eds.), Proceedings of TLT 2002, Sozopol, Bulgaria. Schmid, J., & Laws, F. (2008). Estimation of conditional probabilities with decision trees and an application to fine-grained POS tagging. In Proceedings of COLING 08, Manchester, UK. Telljohann, H., Hinrichs, E., & Kübler, S. (2004). The Tüba-D/Z treebank: annotating German with a context-free backbone. In Proceedings of LREC 2004, Lisbon, Portugal.

28 References Details Methods Wordlist Mapping Word-to-word mappings Learned from an aligned corpus Chooses most frequent candidate wordform No knowledge about spelling variation Example do da 50 meín mein 30 myn mein 30 mín mein 30. hatt hatte 50 hatt hat 20 hatt hut 1

29 References Details Methods Rule-Based Context-aware character rewrite rules v u / # _ n v n d u n d Learned from aligned training corpus Levenshtein distance: Minimum number of edit operations to transform string a into string b Modified algorithm: Outputs the actual edit operations

30 References Details Methods Rule-Based Substitution rules v u / # _ n Identity rules n n / e _ # Insertion rules ε l / o _ l Deletion rules f ε / u _ f Additional lexicon lookup to prevent nonsense words

31 References Details Methods Rule-Based Substitution rules v u / # _ n Identity rules n n / e _ # Insertion rules ε l / o _ l Deletion rules f ε / u _ f Identity and non-identity rules intended to compete Additional lexicon lookup to prevent nonsense words

32 References Details Methods Distance-Based Levenshtein distance: Count number of edit operations myn mein d = 2

33 References Details Methods Distance-Based Levenshtein distance: Count number of edit operations myn mein d = 2 Weighted Levenshtein distance Assigns weights to edit operations e.g., d( y, ei ) = 0.8 Edit operations are directed/asymmetric Edit operations may span multiple characters myn mein d = 0.8

34 References Details Methods Distance-Based Find lexicon entry with lowest distance to input string myn... main mein meine meins mine mini mimik...

35 References Details Methods Distance-Based Find lexicon entry with lowest distance to input string myn... main mein meine meins mine mini mimik...

36 References Details Methods Distance-Based Find lexicon entry with lowest distance to input string myn... main mein meine meins mine mini mimik...

37 References Details Combining Methods Combining methods shown to be beneficial Chain combination of normalizers 1 Wordlist mapping 2 Rule-based normalization 3 Weighted Levenshtein distance Better than other orderings Better than majority-vote approach

38 References Details Combining Methods Wordlist Mapping Success? yes no Rule-Based Done! Success? yes no Weighted Levenshtein Distance

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