Transfer learning for Turkish named entity recognition on noisy text. (28th January 2021)

• Record Type: Journal Article
• Authors: Kağan Akkaya, Emre; Can, Burcu
• Is Part Of: Natural language engineering. Volume 27:Part 1(2021)
• Page Start: 35
• Page End: 64
• Publication Date: 2021-01-28
• Subjects: Named entity recognition, -- Transfer learning, -- Recurrent neural networks, -- Low-resource language, -- Noisy text; Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S1351324919000627
• Languages: English
• ISSNs: 1351-3249

Abstract

Abstract: 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.