Cross-Lingual Part-of-Speech Tagging through Ambiguous Learning
نویسندگان
چکیده
When Part-of-Speech annotated data is scarce, e.g. for under-resourced languages, one can turn to cross-lingual transfer and crawled dictionaries to collect partially supervised data. We cast this problem in the framework of ambiguous learning and show how to learn an accurate history-based model. Experiments on ten languages show significant improvements over prior state of the art performance.
منابع مشابه
Cross-Lingual POS Tagging through Ambiguous Learning: First Experiments (Apprentissage partiellement supervisé d'un étiqueteur morpho-syntaxique par transfert cross-lingue) [in French]
When Part-of-Speech annotated data is scarce, e.g. for under resourced languages, one can turn to crosslingual transfer and crawled dictionaries to collect partially supervised data. We cast this problem in the framework of ambiguous learning and show how to learn an accurate history-based model. This method is evaluated on four languages and yields improvements over state-of-the-art for three ...
متن کاملPredicting Linguistic Structure with Incomplete and Cross-Lingual Supervision
Täckström, O. 2013. Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision. Acta Universitatis Upsaliensis. Studia Linguistica Upsaliensia 14. xii+215 pp. Uppsala. ISBN 978-91-554-8631-0. Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the ling...
متن کاملCross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources
Training a POS tagging model with crosslingual transfer learning usually requires linguistic knowledge and resources about the relation between the source language and the target language. In this paper, we introduce a cross-lingual transfer learning model for POS tagging without ancillary resources such as parallel corpora. The proposed cross-lingual model utilizes a common BLSTM that enables ...
متن کاملCross-Lingual Discriminative Learning of Sequence Models with Posterior Regularization
We present a framework for cross-lingual transfer of sequence information from a resource-rich source language to a resourceimpoverished target language that incorporates soft constraints via posterior regularization. To this end, we use automatically word aligned bitext between the source and target language pair, and learn a discriminative conditional random field model on the target side. Ou...
متن کاملErratum for Token and Type Constraints for Cross-Lingual Part-of-Speech Tagging
This is an erratum for the paper titled “Token and Type Constraints for Cross-Lingual Part-ofSpeech Tagging” by Täckström et al. (2013). It revises the results with the coupled constraints presented in the last three columns of Table 2 in the aforementioned paper.
متن کامل