morphological and part of speech tagging of historical
play

Morphological and Part-of-Speech Tagging of Historical Language - PowerPoint PPT Presentation

Dec 19, 2023 •380 likes •994 views

Morphological and Part-of-Speech Tagging of Historical Language Data: A Comparison Stefanie Dipper Linguistics Department Ruhr-University Bochum 5 January, 2012 Workshop on Annotation of Corpora for Research in the Humanities Heidelberg

  1. Morphological and Part-of-Speech Tagging of Historical Language Data: A Comparison Stefanie Dipper Linguistics Department Ruhr-University Bochum 5 January, 2012 Workshop on Annotation of Corpora for Research in the Humanities Heidelberg Stefanie Dipper Morphological and POS Tagging 5.1.2012 1 / 27

  2. Goals A morphological and a part-of-speech (POS) tagger for texts from Middle High German (MHG, 1050–1350) Ex: sach ‘saw’: 3.Sg.Past.Ind (Morph) (POS) VVFIN Stefanie Dipper Morphological and POS Tagging 5.1.2012 2 / 27

  3. Goals A morphological and a part-of-speech (POS) tagger for texts from Middle High German (MHG, 1050–1350) Ex: sach ‘saw’: 3.Sg.Past.Ind (Morph) (POS) VVFIN Use of available state-of-the-art taggers without any adaption some preprocessing of the annotated training data Stefanie Dipper Morphological and POS Tagging 5.1.2012 2 / 27

  4. Goals A morphological and a part-of-speech (POS) tagger for texts from Middle High German (MHG, 1050–1350) Ex: sach ‘saw’: 3.Sg.Past.Ind (Morph) (POS) VVFIN Use of available state-of-the-art taggers without any adaption some preprocessing of the annotated training data Challenge: highly variant data no spelling conventions, e.g. sitzen, sizzen ‘sit’ different dialects, e.g. bruoder, pruder ‘brother’ → Data sparseness Stefanie Dipper Morphological and POS Tagging 5.1.2012 2 / 27

  5. Goals A morphological and a part-of-speech (POS) tagger for texts from Middle High German (MHG, 1050–1350) Ex: sach ‘saw’: 3.Sg.Past.Ind (Morph) (POS) VVFIN Use of available state-of-the-art taggers without any adaption some preprocessing of the annotated training data Challenge: highly variant data no spelling conventions, e.g. sitzen, sizzen ‘sit’ different dialects, e.g. bruoder, pruder ‘brother’ → Data sparseness Questions: (how much) does normalization help? – original (“diplomatic”) vs. normalized wordforms does POS preprocessing help morphological tagging? Stefanie Dipper Morphological and POS Tagging 5.1.2012 2 / 27

  6. Outline The corpus 1 Training experiments 2 Stefanie Dipper Morphological and POS Tagging 5.1.2012 3 / 27

  7. The corpus Outline The corpus 1 Training experiments 2 Stefanie Dipper Morphological and POS Tagging 5.1.2012 4 / 27

  8. The corpus Project context Projects “Mittelhochdeutsche Grammatik” and “Referenzkorpus Mittelhochdeutsch” (Universities of Bochum and Bonn) Stefanie Dipper Morphological and POS Tagging 5.1.2012 5 / 27

  9. The corpus Project context Projects “Mittelhochdeutsche Grammatik” and “Referenzkorpus Mittelhochdeutsch” (Universities of Bochum and Bonn) Goals a balanced, annotated reference corpus of MHG diplomatic transcriptions final size: 300 texts, 2 million wordforms available via the internet (ANNIS) Stefanie Dipper Morphological and POS Tagging 5.1.2012 5 / 27

  10. The corpus Project context Projects “Mittelhochdeutsche Grammatik” and “Referenzkorpus Mittelhochdeutsch” (Universities of Bochum and Bonn) Goals a balanced, annotated reference corpus of MHG diplomatic transcriptions final size: 300 texts, 2 million wordforms available via the internet (ANNIS) Annotations parts of speech (POS) morphological tags lemma normalized wordform Stefanie Dipper Morphological and POS Tagging 5.1.2012 5 / 27

  11. The corpus Project context Projects “Mittelhochdeutsche Grammatik” and “Referenzkorpus Mittelhochdeutsch” (Universities of Bochum and Bonn) Goals a balanced, annotated reference corpus of MHG diplomatic transcriptions final size: 300 texts, 2 million wordforms available via the internet (ANNIS) Annotations parts of speech (POS) morphological tags lemma normalized wordform Currently: semi-automatic annotation tools by Thomas Klein, Bonn (2001) require a lot of human intervention Stefanie Dipper Morphological and POS Tagging 5.1.2012 5 / 27

  12. The corpus The data 51 texts with 211,000 tokens from the MHG Reference Corpus From two dialect regions: Upper (UG) and Central German (CG) Stefanie Dipper Morphological and POS Tagging 5.1.2012 6 / 27

  13. The corpus Upper and Central (and Lower) German Source: Wikimedia Stefanie Dipper Morphological and POS Tagging 5.1.2012 7 / 27

  14. The corpus Spelling variation E.g. (normalized) wolte ‘wanted’ – wolt – wolta – wolte – woltt – wolti – wolthe – walde – uuolde – volde – . . . Stefanie Dipper Morphological and POS Tagging 5.1.2012 8 / 27

  15. The corpus Spelling variation E.g. (normalized) wolte ‘wanted’ – wolt – wolta – wolte – woltt – wolti – wolthe – walde – uuolde – volde – . . . Normalization: mapping to a virtual, idealized historical wordform Stefanie Dipper Morphological and POS Tagging 5.1.2012 8 / 27

  16. The corpus The data: some statistics Texts Tokens Types diplomatic normalized [ NHG ] 51 total 211,000 40,500 20,500 .19 .10 [.14] 27 CG 91,000 22,000 13,000 .24 .14 [.18] 20 UG 67,000 15,000 8,500 .22 .13 4 mixed 53,000 Stefanie Dipper Morphological and POS Tagging 5.1.2012 9 / 27

  17. The corpus The data: some statistics Texts Tokens Types diplomatic normalized [ NHG ] 51 total 211,000 40,500 20,500 .19 .10 [.14] 27 CG 91,000 22,000 13,000 .24 .14 [.18] 20 UG 67,000 15,000 8,500 .22 .13 4 mixed 53,000 On average: roughly 2 spelling variants (diplomatic) per wordform (normalized) Stefanie Dipper Morphological and POS Tagging 5.1.2012 9 / 27

  18. The corpus The data: some statistics Texts Tokens Types diplomatic normalized [ NHG ] 51 total 211,000 40,500 20,500 .19 .10 [.14] 27 CG 91,000 22,000 13,000 .24 .14 [.18] 20 UG 67,000 15,000 8,500 .22 .13 4 mixed 53,000 On average: roughly 2 spelling variants (diplomatic) per wordform (normalized) Type-token ratio: higher ratio → more diverse data – CG more diverse than UG [ – cf. modern German (NHG): TTR = .14/.18] Stefanie Dipper Morphological and POS Tagging 5.1.2012 9 / 27

  19. The corpus Predictions I 1. Normalized vs. diplomatic: tagging normalized data should be easier Stefanie Dipper Morphological and POS Tagging 5.1.2012 10 / 27

  20. The corpus Predictions I 1. Normalized vs. diplomatic: tagging normalized data should be easier 2. CG vs. UG vs. NHG: rather unclear a) pro CG: more training data available b) pro UG: less diverse (lower type/token ratio) c) pro MHG (normalized, equal size): less diverse than NHG Stefanie Dipper Morphological and POS Tagging 5.1.2012 10 / 27

  21. The corpus Tagsets POS based on the STTS tagset (standard German tagset) NN, NE “normal noun, proper noun” VVFIN, VVINF, VVIMP, VVPP “finite full verb, infinitive, imperative, past participle” Stefanie Dipper Morphological and POS Tagging 5.1.2012 11 / 27

  22. The corpus Tagsets POS based on the STTS tagset (standard German tagset) NN, NE “normal noun, proper noun” VVFIN, VVINF, VVIMP, VVPP “finite full verb, infinitive, imperative, past participle” Morphology “large” STTS tagset Comp.Fem.Acc.Sg “(adjective:) comparative form, feminine, accusative, singular” 3.Sg.Past.* “(verb:) 3rd singular past tense, unspecified for mood” Stefanie Dipper Morphological and POS Tagging 5.1.2012 11 / 27

  23. The corpus Underspecification POS and morph: more underspecified tags in MHG than in NHG (no native speakers) Gender of nouns: not yet as fixed as nowadays Example: slange ‘snake’: masc/fem daz si slangen bizzen - *.Acc.Pl MascFem.Nom.Pl 3.Pl.Past.* that them snakes bit ‘that snakes bit them’ Stefanie Dipper Morphological and POS Tagging 5.1.2012 12 / 27

  24. The corpus Tagsets: some statistics (normalized data) POS # Tags Ø Tags/wofo Median (max) CG norm 44 1 . 10 ± 0 . 37 1 (7) UG norm 41 1 . 10 ± 0 . 35 1 (6) NHG (210K) 53 1 . 05 ± 0 . 23 1 (6) (90K) 51 1 . 04 ± 0 . 21 1 (6) Stefanie Dipper Morphological and POS Tagging 5.1.2012 13 / 27

  25. The corpus Tagsets: some statistics (normalized data) POS # Tags Ø Tags/wofo Median (max) CG norm 44 1 . 10 ± 0 . 37 1 (7) UG norm 41 1 . 10 ± 0 . 35 1 (6) NHG (210K) 53 1 . 05 ± 0 . 23 1 (6) (90K) 51 1 . 04 ± 0 . 21 1 (6) Morphology # Tags Ø Tags/wofo Median (max) CG norm 245 1 . 40 ± 1 . 16 1 (23) UG norm 219 1 . 46 ± 1 . 28 1 (33) NHG (210K) 230 1 . 37 ± 0 . 97 1 (26) (90K) 205 1 . 32 ± 0 . 86 1 (18) Stefanie Dipper Morphological and POS Tagging 5.1.2012 13 / 27

  26. The corpus Predictions II 1. Normalized vs. diplomatic: tagging normalized data should be easier 2. CG vs. UG vs. NHG: rather unclear a) pro CG: more training data available b) pro UG: less diverse (lower type/token ratio) c) pro MHG (normalized, equal size): less diverse than NHG 3. Morphology vs. POS: tagging POS should be easier (lower ambiguity rate) Stefanie Dipper Morphological and POS Tagging 5.1.2012 14 / 27

  27. The corpus Predictions II 1. Normalized vs. diplomatic: tagging normalized data should be easier 2. CG vs. UG vs. NHG: rather unclear a) pro CG: more training data available b) pro UG: less diverse (lower type/token ratio) c) pro MHG (normalized, equal size): less diverse than NHG 3. Morphology vs. POS: tagging POS should be easier (lower ambiguity rate) 4. CG vs. UG vs. NHG: again, rather unclear a) CG and UG rather similar b) POS: pro UG, morph: pro CG (lower maxima) c) pro NHG: lower ambiguity rates Stefanie Dipper Morphological and POS Tagging 5.1.2012 14 / 27

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Part-of-Speech Tagging Part-of-Speech Tagging Berlin Chen 2003 References: 1. Speech and

Part-of-Speech Tagging Part-of-Speech Tagging Berlin Chen 2003 References: 1. Speech and

Part-of-Speech Tagging Part-of-Speech Tagging Berlin Chen 2003 References: 1. Speech and Language Processing, chapter 8 2. Foundations of Statistical Natural Language Processing, chapter 10 1 Review Tagging (part-of-speech tagging)

671 views • 38 slides
Part-of-Speech Tagging for Historical English Yi Yang and Jacob Eisenstein Georgia Tech

Part-of-Speech Tagging for Historical English Yi Yang and Jacob Eisenstein Georgia Tech

Part-of-Speech Tagging for Historical English Yi Yang and Jacob Eisenstein Georgia Tech Digital humaniEes research How does the portrayal of men and women differ in Shakespeares plays? Whats the language use paMerns in North

1.07k views • 81 slides
Part-of-Speech Tagging Part-of-Speech Tagging Berlin Chen 2005 References: 1. Speech and

Part-of-Speech Tagging Part-of-Speech Tagging Berlin Chen 2005 References: 1. Speech and

Part-of-Speech Tagging Part-of-Speech Tagging Berlin Chen 2005 References: 1. Speech and Language Processing , chapter 8 2. Foundations of Statistical Natural Language Processing , chapter 10 NLP-Berlin Chen 1 Review Tagging

1k views • 54 slides
Feature-Based Tagging The Task, Again Recall: tagging ~ morphological disambiguation

Feature-Based Tagging The Task, Again Recall: tagging ~ morphological disambiguation

Feature-Based Tagging The Task, Again Recall: tagging ~ morphological disambiguation tagset V T (C 1 ,C 2 ,...C n ) C i - morphological categories, such as POS, NUMBER, CASE, PERSON, TENSE, GENDER, ... mapping w {t

418 views • 16 slides
Part Of Speech (POS) Tagging Based on Foundations of Statistical NLP by C. Manning & H.

Part Of Speech (POS) Tagging Based on Foundations of Statistical NLP by C. Manning & H.

0. Part Of Speech (POS) Tagging Based on Foundations of Statistical NLP by C. Manning & H. Sch utze, ch. 10 MIT Press, 2002 1. 1. POS Tagging: Overview Task: labeling (tagging) each word in a sentence with the appropriate POS

671 views • 36 slides
Part-of-speech Tagging for Middle English through Alignment and Projection of Parallel Diachronic

Part-of-speech Tagging for Middle English through Alignment and Projection of Parallel Diachronic

Text Mining for Historical Documents Non-Standard Language Adapting NLP Tools Part-of-speech Tagging for Middle English through Alignment and Projection of Parallel Diachronic Texts Taesun Moon and Jason Baldridge Presenter: Yevgeni Berzak

430 views • 26 slides
Part of Speech Tagging Informatics 2A: Lecture 15 Mirella Lapata School of Informatics

Part of Speech Tagging Informatics 2A: Lecture 15 Mirella Lapata School of Informatics

Automatic POS Tagging HMM Part-of-Speech Tagging Part of Speech Tagging Informatics 2A: Lecture 15 Mirella Lapata School of Informatics University of Edinburgh 21 October 2011 Informatics 2A: Lecture 15 Part of Speech Tagging 1 Automatic

1.82k views • 53 slides
Part of Speech Tagging Informatics 2A: Lecture 16 John Longley School of Informatics University

Part of Speech Tagging Informatics 2A: Lecture 16 John Longley School of Informatics University

Automatic POS tagging: the problem Methods for tagging Part of Speech Tagging Informatics 2A: Lecture 16 John Longley School of Informatics University of Edinburgh 23 October 2014 Informatics 2A: Lecture 16 Part of Speech Tagging 1

663 views • 26 slides
Natural Language Processing Parts of Speech Part of Speech Tagging Dan Klein UC

Natural Language Processing Parts of Speech Part of Speech Tagging Dan Klein UC

Natural Language Processing Parts of Speech Part of Speech Tagging Dan Klein UC Berkeley Parts of Speech (English) CC conjunction, coordinating and both but either or CD numeral, cardinal mid-1890 nine-thirty 0.5 one DT

231 views • 7 slides
Part-of-speech tagging A simple but useful form of

Part-of-speech tagging A simple but useful form of

Part-of-speech tagging A simple but useful form of linguis1c analysis Many slides adapted from slides by Chris Manning Parts of Speech Perhaps star1ng with

532 views • 18 slides
Natural Language Processing Part of Speech Tagging Dan Klein UC Berkeley 1 2 Parts of

Natural Language Processing Part of Speech Tagging Dan Klein UC Berkeley 1 2 Parts of

Natural Language Processing Part of Speech Tagging Dan Klein UC Berkeley 1 2 Parts of Speech Parts of Speech (English) One basic kind of linguistic structure: syntactic word classes Open class (lexical) words Nouns

529 views • 42 slides
Efficiency in Part-of-Speech Tagging Naghmeh Fazeli summer semester 2016 Supervisor: Dr.

Efficiency in Part-of-Speech Tagging Naghmeh Fazeli summer semester 2016 Supervisor: Dr.

Efficiency in Part-of-Speech Tagging Naghmeh Fazeli summer semester 2016 Supervisor: Dr. Alexis Palmer Learning a Part-of-Speech Tagger from Two Hours of Annotation -2013 Dan Garrette Department of Computer Science The University of

627 views • 48 slides
Part-of-Speech Tagging COSI 114 Computational Linguistics James Pustejovsky February 24,

Part-of-Speech Tagging COSI 114 Computational Linguistics James Pustejovsky February 24,

Part-of-Speech Tagging COSI 114 Computational Linguistics James Pustejovsky February 24, 2015 Brandeis University Parts of Speech Perhaps starting with Aristotle in the West (384322 BCE) the idea of having parts of speech

663 views • 54 slides
Part-of-Speech Tagging Jimmy Lin Jimmy Lin The iSchool University of Maryland Wednesday,

Part-of-Speech Tagging Jimmy Lin Jimmy Lin The iSchool University of Maryland Wednesday,

CMSC 723: Computational Linguistics I Session #4 Part-of-Speech Tagging Jimmy Lin Jimmy Lin The iSchool University of Maryland Wednesday, September 23, 2009 Source: Calvin and Hobbs Todays Agenda What are parts of speech (POS)?

1.14k views • 74 slides
POS Tagging & Sequence Labeling Tasks CMSC 470 Marine Carpuat Parts of Speech

POS Tagging & Sequence Labeling Tasks CMSC 470 Marine Carpuat Parts of Speech

POS Tagging & Sequence Labeling Tasks CMSC 470 Marine Carpuat Parts of Speech Equivalence class of linguistic entities Categories or types of words that occur in similar morphological and syntactic contexts

456 views • 31 slides
Part-of-Speech Tagging COSI 114 Computational Linguistics James Pustejovsky March 17, 2017

Part-of-Speech Tagging COSI 114 Computational Linguistics James Pustejovsky March 17, 2017

Part-of-Speech Tagging COSI 114 Computational Linguistics James Pustejovsky March 17, 2017 Brandeis University Parts of Speech Perhaps starting with Aristotle in the West (384322 BCE) the idea of having parts of speech lexical

554 views • 54 slides
Context 1. Observation (before light pollution) 2. Explanation 3. Model Phaenomena " the

Context 1. Observation (before light pollution) 2. Explanation 3. Model Phaenomena " the

Context 1. Observation (before light pollution) 2. Explanation 3. Model Phaenomena " the things that appear " regular, observable patterns explanation and model: geometric mechanism cause? Observations Time-lapse photograph:

423 views • 19 slides
Quantum Schur algebras and their affine and super counterparts Jie Du University of New South

Quantum Schur algebras and their affine and super counterparts Jie Du University of New South

Quantum Schur algebras and their affine and super counterparts Jie Du University of New South Wales via University of Virginia UBath Seminar, October 25, 2016 1 / 23 1. Introductionthe SchurWeyl Duality Wedderburns Theorem: A

892 views • 67 slides
Dredging Methods in the US and South Korea Pierre Y. Julien Colorado State University World

Dredging Methods in the US and South Korea Pierre Y. Julien Colorado State University World

4/9/2015 Dredging Methods in the US and South Korea Pierre Y. Julien Colorado State University World Water Forum Daegu, April 2015 Dipper Dredge, NY, USACE Clamshell Dredge 1 4/9/2015 Mystic River Clamshell Dredge, New England

537 views • 8 slides
Safety Management & Site Establishment Unit 11 - Site Plant Management Learning Outcomes

Safety Management & Site Establishment Unit 11 - Site Plant Management Learning Outcomes

Safety Management & Site Establishment Unit 11 - Site Plant Management Learning Outcomes This lecture aims to introduce the following; The importance of plant for site construction works Points to consider during negotiation of plant

691 views • 65 slides
Cellularity and the Jones basic construction Cellularity definition Ponidicherry Conference,

Cellularity and the Jones basic construction Cellularity definition Ponidicherry Conference,

Cellularity and the Jones basic construction Frederick Goodman Introduction The Jones basic construction Cellularity and the Jones basic construction Cellularity definition Ponidicherry Conference, 2010 weaker, better definition basis

330 views • 31 slides
Antecedent and referent types of abstract pronominal anaphora Costanza Navarretta University of

Antecedent and referent types of abstract pronominal anaphora Costanza Navarretta University of

Centre for Language Technology Antecedent and referent types of abstract pronominal anaphora Costanza Navarretta University of Copenhagen costanza@hum.ku.dk Beyond Semantics, Gttingen February 23-25 2011 Centre for Language Technology

593 views • 22 slides
Using support vector machines and state-of-the-art algorithms for phonetic alignment to identify

Using support vector machines and state-of-the-art algorithms for phonetic alignment to identify

Using support vector machines and state-of-the-art algorithms for phonetic alignment to identify cognates in multi-lingual wordlists Gerhard Jger, Johann-Mattis List & Pavel Sofroniev Tbingen University & MPI Jena

886 views • 51 slides
Tutorial Outline Tutorial Outline XLE: XLE: What is a deep grammar and why would you want

Tutorial Outline Tutorial Outline XLE: XLE: What is a deep grammar and why would you want

Tutorial Outline Tutorial Outline XLE: XLE: What is a deep grammar and why would you want Grammar Development Platform Grammar Development Platform one? Parser/Generator XLE: A First Walkthrough Parser/Generator Robustness

495 views • 34 slides

More recommend