Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging

Maimaiti, Maihemuti; Wumaier, Aishan; Abiderexiti, Kahaerjiang; Yibulayin, Tuergen

doi:10.3390/info8040157

Cited by 14 publications

(7 citation statements)

References 6 publications

(5 reference statements)

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“…The above methods [4,[20][21][22] improve the accuracy of POS tagging by enriching the input information. To further improve the accuracy of POS tagging, some works [26][27][28] combine Bi-LSTM with CRF since CRF can learn sentence-level tag information. In addition to CRF, Bi-LSTM can be integrated with adversarial neural networks to extract better features [29,30], which also can improve the accuracy.…”

Section: Pos Taggingmentioning

confidence: 99%

Part-of-Speech Tagging with Rule-Based Data Preprocessing and Transformer

Mao

Wang

2021

Electronics

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Part-of-Speech (POS) tagging is one of the most important tasks in the field of natural language processing (NLP). POS tagging for a word depends not only on the word itself but also on its position, its surrounding words, and their POS tags. POS tagging can be an upstream task for other NLP tasks, further improving their performance. Therefore, it is important to improve the accuracy of POS tagging. In POS tagging, bidirectional Long Short-Term Memory (Bi-LSTM) is commonly used and achieves good performance. However, Bi-LSTM is not as powerful as Transformer in leveraging contextual information, since Bi-LSTM simply concatenates the contextual information from left-to-right and right-to-left. In this study, we propose a novel approach for POS tagging to improve the accuracy. For each token, all possible POS tags are obtained without considering context, and then rules are applied to prune out these possible POS tags, which we call rule-based data preprocessing. In this way, the number of possible POS tags of most tokens can be reduced to one, and they are considered to be correctly tagged. Finally, POS tags of the remaining tokens are masked, and a model based on Transformer is used to only predict the masked POS tags, which enables it to leverage bidirectional contexts. Our experimental result shows that our approach leads to better performance than other methods using Bi-LSTM.

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Section: Pos Taggingmentioning

confidence: 99%

Part-of-Speech Tagging with Rule-Based Data Preprocessing and Transformer

Mao

Wang

2021

Electronics

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“…The inherent syllable structure is (initial sound) + nucleus sound + (final sound), where the nucleus sound must be a vowel. There can be no initial sound or final sound in the syllable, but there must be a nucleus sound [26][27][28]. The present-day Uyghur is classified into 12 syllabic types.…”

Section: Syllables Of Uyghurmentioning

confidence: 99%

A Syllable-Based Technique for Uyghur Text Compression

Abliz

Maimaiti

et al. 2020

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To improve utilization of text storage resources and efficiency of data transmission, we proposed two syllable-based Uyghur text compression coding schemes. First, according to the statistics of syllable coverage of the corpus text, we constructed a 12-bit and 16-bit syllable code tables and added commonly used symbols—such as punctuation marks and ASCII characters—to the code tables. To enable the coding scheme to process Uyghur texts mixed with other language symbols, we introduced a flag code in the compression process to distinguish the Unicode encodings that were not in the code table. The experiments showed that the 12-bit coding scheme had an average compression ratio of 0.3 on Uyghur text less than 4 KB in size and that the 16-bit coding scheme had an average compression ratio of 0.5 on text less than 2 KB in size. Our compression schemes outperformed GZip, BZip2, and the LZW algorithm on short text and could be effectively applied to the compression of Uyghur short text for storage and applications.

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“…The authors of [36] constructed a contemporary Uyghur grammatical information dictionary that provided extensive grammatical information and collocation features and is presently a primary resource for NLP research specific to the Uyghur language. The Uyghur Dependency Treebank was built from a public reading corpus [37,38], and also presently serves as an important tool for Uyghur linguistic studies. A standard scheme for tagging Uyghur sentences to construct a typical knowledge graph is illustrated in Table 1.…”

Section: Related Workmentioning

confidence: 99%

Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method

2020

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Relation extraction is an important task with many applications in natural language processing, such as structured knowledge extraction, knowledge graph construction, and automatic question answering system construction. However, relatively little past work has focused on the construction of the corpus and extraction of Uyghur-named entity relations, resulting in a very limited availability of relation extraction research and a deficiency of annotated relation data. This issue is addressed in the present article by proposing a hybrid Uyghur-named entity relation extraction method that combines a conditional random field model for making suggestions regarding annotation based on extracted relations with a set of rules applied by human annotators to rapidly increase the size of the Uyghur corpus. We integrate our relation extraction method into an existing annotation tool, and, with the help of human correction, we implement Uyghur relation extraction and expand the existing corpus. The effectiveness of our proposed approach is demonstrated based on experimental results by using an existing Uyghur corpus, and our method achieves a maximum weighted average between precision and recall of 61.34%. The method we proposed achieves state-of-the-art results on entity and relation extraction tasks in Uyghur.

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Bidirectional Long Short-Term Memory Network with a Conditional Random Field Layer for Uyghur Part-Of-Speech Tagging

Cited by 14 publications

References 6 publications

Part-of-Speech Tagging with Rule-Based Data Preprocessing and Transformer

Part-of-Speech Tagging with Rule-Based Data Preprocessing and Transformer

A Syllable-Based Technique for Uyghur Text Compression

Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method

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