Transfer learning for Turkish named entity recognition on noisy text
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Issue Date
2020-01-28
Metadata
Show full item recordAbstract
© Cambridge University Press 2020. In this article, we investigate using deep neural networks with different word representation techniques for named entity recognition (NER) on Turkish noisy text. We argue that valuable latent features for NER can, in fact, be learned without using any hand-crafted features and/or domain-specific resources such as gazetteers and lexicons. In this regard, we utilize character-level, character n-gram-level, morpheme-level, and orthographic character-level word representations. Since noisy data with NER annotation are scarce for Turkish, we introduce a transfer learning model in order to learn infrequent entity types as an extension to the Bi-LSTM-CRF architecture by incorporating an additional conditional random field (CRF) layer that is trained on a larger (but formal) text and a noisy text simultaneously. This allows us to learn from both formal and informal/noisy text, thus improving the performance of our model further for rarely seen entity types. We experimented on Turkish as a morphologically rich language and English as a relatively morphologically poor language. We obtained an entity-level F1 score of 67.39% on Turkish noisy data and 45.30% on English noisy data, which outperforms the current state-of-art models on noisy text. The English scores are lower compared to Turkish scores because of the intense sparsity in the data introduced by the user writing styles. The results prove that using subword information significantly contributes to learning latent features for morphologically rich languages.Citation
Kağan Akkaya, E., & Can, B. (2020). Transfer learning for Turkish named entity recognition on noisy text. Natural Language Engineering, 1-30. doi:10.1017/S1351324919000627Publisher
Cambridge University Press (CUP)Journal
Natural Language EngineeringType
Journal articleLanguage
enDescription
This is an accepted manuscript of an article published by Cambridge University Press in Natural Language Engineering on 28/01/2020, available online: https://doi.org/10.1017/S1351324919000627 The accepted version of the publication may differ from the final published version.ISSN
1351-3249EISSN
1469-8110ae974a485f413a2113503eed53cd6c53
10.1017/S1351324919000627
Scopus Count
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/