Adapting a State-of-the-Art Tagger for South Slavic Languages to
            Non-Standard Text

Ljubešić, Nikola; Erjavec, Tomaž; Fišer, Darja

doi:10.18653/v1/w17-1410

Cited by 10 publications

(8 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because of that, our results should be compared to the performance of taggers trained on written data from languages with rich morphology applied to spoken data as test files. As is well known and has been demonstrated numerous times, the use of standard training data for tagging nonstandard, more specifically oral, test data inevitably leads to worse results as compared to standard test data (Ljubešić, Erjavec, & Fišer, 2017).…”

Section: Experiments and Resultsmentioning

confidence: 99%

Neural morphosyntactic tagging for Rusyn

Scherrer

Rabus

2019

Nat. Lang. Eng.

View full text Add to dashboard Cite

The paper presents experiments on part-of-speech and full morphological tagging of the Slavic minority language Rusyn. The proposed approach relies on transfer learning and uses only annotated resources from related Slavic languages, namely Russian, Ukrainian, Slovak, Polish, and Czech. It does not require any annotated Rusyn training data, nor parallel data or bilingual dictionaries involving Rusyn. Compared to earlier work, we improve tagging performance by using a neural network tagger and larger training data from the neighboring Slavic languages. We experiment with various data preprocessing and sampling strategies and evaluate the impact of multitask learning strategies and of pretrained word embeddings. Overall, while genre discrepancies between training and test data have a negative impact, we improve full morphological tagging by 9% absolute micro-averaged F1 as compared to previous research.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Neural morphosyntactic tagging for Rusyn

Scherrer

Rabus

2019

Nat. Lang. Eng.

View full text Add to dashboard Cite

show abstract

“…For cases when Brown clustering information is provided to the tagger, the tagger encodes additional features of all binary paths from the root that have even length (Ljubešić et al 2017).…”

Section: Methodsmentioning

confidence: 99%

“…In particular, on standard training and testing data ReLDI-tagger obtained 94.27% accuracy on the fine-grained tagset with full morphosyntactic descriptions (Ljubešić and Erjavec 2016) while Bilty (on the same data and a large non-annotated data set used for embedding initialisation) obtained an accuracy of 94.29%. Similarly, when applied to non-standard data, an adapted ReLDI-tagger obtained an accuracy of 87.70% (Ljubešić, Erjavec, and Fišer 2017), while Bilty achieves 88.15%.…”

Section: Introductionmentioning

confidence: 99%

How to tag non-standard language: Normalisation versus domain adaptation for Slovene historical and user-generated texts

2019

Self Cite

View full text Add to dashboard Cite

Part-of-speech (PoS) tagging of non-standard language with models developed for standard language is known to suffer from a significant decrease in accuracy. Two methods are typically used to improve it: word normalisation, which decreases the out-of-vocabulary rate of the PoS tagger, and domain adaptation where the tagger is made aware of the non-standard language variation, either through supervision via non-standard data being added to the tagger’s training set, or via distributional information calculated from raw texts. This paper investigates the two approaches, normalisation and domain adaptation, on carefully constructed data sets encompassing historical and user-generated Slovene texts, in particular focusing on the amount of labour necessary to produce the manually annotated data sets for each approach and comparing the resulting PoS accuracy. We give quantitative as well as qualitative analyses of the tagger performance in various settings, showing that on our data set closed and open class words exhibit significantly different behaviours, and that even small inconsistencies in the PoS tags in the data have an impact on the accuracy. We also show that to improve tagging accuracy, it is best to concentrate on obtaining manually annotated normalisation training data for short annotation campaigns, while manually producing in-domain training sets for PoS tagging is better when a more substantial annotation campaign can be undertaken. Finally, unsupervised adaptation via Brown clustering is similarly useful regardless of the size of the training data available, but improvements tend to be bigger when adaptation is performed via in-domain tagging data.

show abstract

“…Whereas South Slavic languages are generally less supported in the NLP, they have been investigated in terms of user-generated content. For example, sentiment classification of Croatian Game reviews and Tweets is investigated in (Rotim anď Snajder, 2017), and (Ljubešić et al, 2017) proposes adapting a standard-text Slovenian POS tagger to tweets, forum posts, and user comments on blog posts and news articles. These languages have been dealt with in machine translation research as well.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

2019

View full text Add to dashboard Cite

The paper presents an unsupervised method for quickly extending a Ukrainian lexicon by generating paradigms and morphological feature structures for new proper names and neologisms, which are not covered by existing static morphological resources. This approach addresses a practical problem of modelling paradigms for entities created by the dynamic processes in the lexicon: this problem is especially serious for highly-inflected languages in domains with specialised or quickly changing lexicon. The method uses an unannotated Ukrainian corpus and a small fixed set of inflection tables, which can be found in traditional grammar textbooks. The advantage of the proposed approach is that updating the morphological lexicon does not require training or linguistic annotation, allowing fast knowledge-light extension of an existing static lexicon to improve morphological coverage on a specific corpus. The method is implemented in an open-source package on a GitHub repository. It can be applied to other low-resourced inflectional languages which have internet corpora and linguistic descriptions of their inflection system, following the example of inflection tables for Ukrainian. Evaluation results show consistent improvements in coverage for Ukrainian corpora of different corpus types.

show abstract

Adapting a State-of-the-Art Tagger for South Slavic Languages to Non-Standard Text

Cited by 10 publications

References 10 publications

Neural morphosyntactic tagging for Rusyn

Neural morphosyntactic tagging for Rusyn

How to tag non-standard language: Normalisation versus domain adaptation for Slovene historical and user-generated texts

Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing

Contact Info

Product

Resources

About