Konferenzbeitrag
Data point selection for self-training
Problems for parsing morphologically rich languages are, amongst others, caused by the higher variability in structure due to less rigid word order constraints and by the higher number of different lexical forms. Both properties can result in sparse data problems for statistical parsing. We present a simple approach for addressing these issues. Our approach makes use of self-training on instances selected with regard to their similarity to the annotated data. Our similarity measure is based on the perplexity of part-of-speech trigrams of new instances measured against the annotated training data. Preliminary results show that our method outperforms a self-training setting where instances are simply selected by order of occurrence in the corpus and argue that selftraining is a cheap and effective method for improving parsing accuracy for morphologically rich languages.
- Language
-
Englisch
- Subject
-
Satzanalyse
Automatische Sprachanalyse
Sprache
- Event
-
Geistige Schöpfung
- (who)
-
Rehbein, Ines
- Event
-
Veröffentlichung
- (who)
-
Stroudsburg, PA : Association for Computational
- (when)
-
2016-11-21
- URN
-
urn:nbn:de:bsz:mh39-56043
- Last update
-
06.03.2025, 9:00 AM CET
Data provider
Leibniz-Institut für Deutsche Sprache - Bibliothek. If you have any questions about the object, please contact the data provider.
Object type
- Konferenzbeitrag
Associated
- Rehbein, Ines
- Stroudsburg, PA : Association for Computational
Time of origin
- 2016-11-21
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Data point selection for genre-aware parsing
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Data point selection for genre-aware parsing
Treebank-Based Grammar Acquisition for German
Der Einfluss der Dependenzgrammatik auf die Computerlinguistik
POS error detection in automatically annotated corpora
Der Einfluss der Dependenzgrammatik auf die Computerlinguistik
Semantic frames as an anchor representation for sentiment analysis
STTS goes Kiez – Experiments on Annotating and Tagging Urban Youth Language