1. Martin Volk Universität Zürich Eurospider Information Technology AG
Corpus Annotation II
Martin Volk
Universität Zürich
Eurospider Information Technology AG

2. Overview Clean-Up and Text Structure Recognition
Sentence Boundary Recognition
Proper Name Recognition and Classification
Part-of-Speech Tagging
Tagging Correction and Sentence Boundary Corr.
Lemmatisation and Lemma Filtering
NP/PP Chunk Recognition
Recognition of Local and Temporal PPs
Clause Boundary Recognition
Martin Volk
4 December 2018

3. Part-of-Speech Tagging
Was done with the Tree-Tagger (Helmut Schmid, IMS-Stuttgart).
The Tree-Tagger
Is a Statistical Tagger.
uses the STTS tag set (50 PoS tags and 3 tags for punctuation).
assigns 1 tag to each word.
preserves pre-set tags.
Martin Volk
4 December 2018

4. Tagging Correction Correction of observed tagger problems:
Sentence-initial adjectives
are often tagged as noun (NN)
'...liche[nr]' or '...ische[nr]'  ADJA
Verb group patterns
the verb in front of 'worden' must be perfect participle
VVXXX + 'worden'  VVPP
if verb + modal verb then the verb must be infinitive
VVXXX + VMYYY  VVINF
Unknown prepositions (a, via, innert, ennet)
Martin Volk
4 December 2018

5. Correction of sentence boundaries
E.g.: suspected ordinal number followed by a capitalized
determiner or
pronoun or
preposition or
adverb
 insert sentence boundary.
Open question: Could all sentence boundary detection be done after PoS tagging?
Martin Volk
4 December 2018

6. Lemmatisation Was done with Gertwol (von Lingsoft Oy, Helsinki)
for adjectives, nouns, prepositions, and verbs.
Gertwol
is a two-level morphology analyzer for German
is lexicon-based
returns all possible interpretations for each word form
segments compound words dynamically
analyzes hyphenated compounds only if all parts are known (e.g. Software-Aktien but not Informix-Aktien)  feed last element to Gertwol
Martin Volk
4 December 2018

7. Lemma Filtering (a project by Julian Käser)
After lemmatisation: Merging of Gertwol and tagger information
Case 1: The lemma was prespecified during proper name recognition (IBMs  IBM)
Case 2: Gertwol does not find a lemma  insert the word form as lemma (with '?')
Martin Volk
4 December 2018

8. Lemma Filtering
Case 3: Gertwol finds exactly one lemma for the given PoS  insert the lemma
Case 4: Gertwol finds multiple lemmas for the given PoS  disambiguate and insert the best lemma
Disambiguation weights the segmentation symbols:
Strong compound segment boundary: 4 points
Weak compound segment boundary: 2 points
Derivational segment boundary: 1 point
the lemma with the lowest score wins!
Example: Abteilungen 
Abt~ei#lunge (5 points) vs. Ab|teil~ung (3 points)
Martin Volk
4 December 2018

9. Lemma Filtering
Case 5: Gertwol finds a lemma but not for the given PoS
 this indicates a tagger error (Gertwol is more reliable than the tagger.)
Case 5.1: Gertwol finds a lemma for exactly one PoS  insert the lemma and exchange the PoS tag
Case 5.2: Gertwol finds lemmas for more than one PoS  find closest PoS tag, or guess
Martin Volk
4 December 2018

10. Lemma Filtering
0.74% of all PoS tags were exchanged (2% of Adj, N, V tags).
In other words: ~ 14'000 tags / annual volume of the ComputerZeitung were exchanged.
85% are cases with exactly one Gertwol tag, 15% are guesses.
Martin Volk
4 December 2018

11. Limitations of Gertwol
Compounds are lemmatized only if all parts are known.
Idea: Use corpus for lemmatizing remaining compounds:
Example: kaputtreden, Waferfabriken
Solution:
If first part occurs standing alone AND
second part occurs standing alone with lemma,
then segment and lemmatize!
and store first part as lemma (of itself)! !!
Martin Volk
4 December 2018

12. NP/PP Chunk Recognition (a project by Dominik A. Merz)
Pattern matcher with patterns over PoS-tags
Example patterns:
ADV ADJA --> AP
APPR ART ADJA NN --> PP
APPR ART AP NN --> PP
Note: The morphological information provided by Gertwol (e.g. grammatical case, number, gender) was not used!!
Martin Volk
4 December 2018

13. Representation Format
The NEGRA export format
is a line based format
works with pointers for tree structure
comprises node labels (constituents) and edge labels (grammatical functions)
has no provision for semantic information. Therefore: We use the comment field.
Martin Volk
4 December 2018

14. Recognition of temporal PPs (a project by Stefan Höfler)
A second step towards semantic annotation.
Starting point:
Prepositions (3) that always introduce a temporal PP: binnen, während, zeit
Prepositions (30) that may introduce a temporal PP: ab, an, auf, bis, additional evidence
Additional evidence:
Temporal adverb in PP: heute, niemals, wann, ...
Temporal noun in PP: Minute, Stunde, Jahr, Anfang, ...
Martin Volk
4 December 2018

15. Recognition of temporal PPs
Evaluation corpus: 990 sentences with manually checked 263 temporal PPs
Result:
Precision: 81%
Recall: 76%
Martin Volk
4 December 2018

16. Recognition of local PPs
Starting point:
Prepositions (3) that always introduce a local PP: fern, oberhalb, südlich von
Prepositions (30) that may introduce a local PP: ab, auf, bei, additional evidence
Additional evidence:
Local adverb in PP: dort, hier, oben, rechts, ...
Local noun in PP: Strasse, Quartier, Land, Norden, <GEO>, ...
Martin Volk
4 December 2018

17. Recognition of temporal and local PPs
Martin Volk
4 December 2018

18. A Word on Recall and Precision
The focus varies with the application!
For my project: Precision is more important than Recall!
Idea: If I annotate something, then I want to be 'sure' that it is correct.
Martin Volk
4 December 2018

19. Clause Boundary Recognition (a project by Gaudenz Lügstenmann)
Definition: A clause is a unit consisting of a full verb together with its (non-clausal) complements and adjuncts.
A sentence consists of one or more clauses, and a clause consists of one or more phrases.
Clauses are important for determining the cooccurrence of verbs and PPs (among other things).
Martin Volk
4 December 2018

20. Clauses Boundary Recognition
Exceptions from the definition: Clauses with more than one verb:
Coordinated verbs (e.g. Daten können überführt und verarbeitet werden)
Perception verb + infinitive verb (=AcI) (e.g. die den Markt wachsen sehen.)
'lassen' + infinitive verb (e.g. lässt die Handbücher übertragen)
Martin Volk
4 December 2018

21. Clauses Boundary Recognition
Exceptions from the definition: Clauses without a verb:
Elliptical clauses (e.g. in coordinated structures)
Example: Er beobachtet den Markt und seine Mitarbeiter die Konkurrenz.
Martin Volk
4 December 2018

22. Clauses Boundary Recognition
The CB recognizer is realized as a pattern matcher over PoS tags. (34 patterns)
Example:
Comma + Relative Pronoun
Finite verb Conjunction Finite Verb
Most difficult: CB without overt punctuation symbol or trigger word
Example: Simple Budgetreduzierungen in der IT in den Vordergrund zu stellen <CB> ist der falsche Ansatz.
Martin Volk
4 December 2018

23. Clauses Boundary Recognition
Evaluation corpus: 1150 sentences with 754 intra-sentential CBs.
Results (counting all CBs)
Precision: 95.8%
Recall: 84.9%
Results (counting only intra-sentential CBs)
Precision: 90.5%
Recall: 61.1%
Martin Volk
4 December 2018

24. Using a PoS Tagger for Clause Boundary Recognition
A CB recognizer can be seen as a disambiguator over commas and CB trigger tokens (if we disregard the CBs without trigger).
A tagger may serve the same purpose.
Example:
... schrieb der Präsident,<Co> Michael Eisner,<Co> im Jahresbericht.
... schrieb der Präsident,<CB> der Michael Eisner kannte,<CB> im Jahresbericht.
Martin Volk
4 December 2018

25. Using a PoS Tagger for Clause Boundary Recognition
Evaluation corpus: 1150 sentences with 754 intra-sentential CBs.
Training the Brill-Tagger on 75% and applying it on the remaining 25%
Results:
93% Precision
91% Recall
Caution: very small evaluation corpus!!
Martin Volk
4 December 2018

26. Clause Boundary Recognition vs. Clause Recognition
CB recognition marks only the boundaries. It does not identify discontinuous parts of clauses.
Example:
Nur ein Projekt der Volkswagen AG, <CB> die ihre europäischen Vertragswerkstätten per Satellit vernetzen will, <CB> stößt in ähnliche Dimensionen vor.
<C> Nur ein Projekt der Volkswagen AG, <C> die ihre europäischen Vertragswerkstätten per Satellit vernetzen will, </C> stößt in ähnliche Dimensionen vor. </C>
Clause Recognition should be done with a recursive parsing approach because of clause nesting.
Martin Volk
4 December 2018