KRNNT: Polish Recurrent Neural Network Tagger Extended

Wróbel, Krzysztof

doi:10.1007/978-3-030-66527-2_8

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2022

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“…First step required to train a model is to gather enough data of sufficiently high quality. We used datasets from both task 2 and 3 of PolEval'19 competition 4 . Task 2 data is specific to lemmatisation however it is not very big.…”

Section: Datasetmentioning

confidence: 99%

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Context based lemmatizer for Polish language

Karwatowski¹,

Pietroń²

2022

Preprint

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Lemmatization is the process of grouping together the inflected forms of a word so they can be analysed as a single item, identified by the word's lemma, or dictionary form. In computational linguistics, lemmatisation is the algorithmic process of determining the lemma of a word based on its intended meaning. Unlike stemming, lemmatisation depends on correctly identifying the intended part of speech and meaning of a word in a sentence, as well as within the larger context surrounding that sentence. As a result, developing efficient lemmatisation algorithm is the complex task. In recent years it can be observed that deep learning models used for this task outperform other methods including machine learning algorithms. In this paper the polish lemmatizer based on Google T5 model is presented. The training was run with different context lengths. The model achieves the best results for polish language lemmatisation process.

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Section: Datasetmentioning

confidence: 99%

“…There were a few approaches to Polish language lemmatisation, for example KRNNT tagger [4] based on recurrent neural network. In 2019 lemmatisation was a part of a PolEval competition 2 .…”

Section: Introductionmentioning

confidence: 99%

Context based lemmatizer for Polish language

Karwatowski¹,

Pietroń²

2022

Preprint

View full text Add to dashboard Cite

show abstract

KRNNT: Polish Recurrent Neural Network Tagger Extended

Cited by 1 publication

References 9 publications

Context based lemmatizer for Polish language

Context based lemmatizer for Polish language

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