Annotation schemes for constructing Uyghur named entity relation corpus

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

doi:10.1109/ialp.2016.7875945

Cited by 4 publications

(7 citation statements)

References 14 publications

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“…The raw statistics pertaining to the initial annotated Uyghur language corpus and expanded corpus are listed in Table 7. Reference [14] was focused on the construction of the corpus of Uyghur-named entity relations, resulting in the very limited availability of relation extraction research and a deficiency of annotated relation data. There are 571 documents, 6173 sentences, and 4098 useful sentences.…”

Section: Methodsmentioning

confidence: 99%

“…For NLP applications specific to the Uyghur language, a few studies have focused on corpus construction. Here, the authors of [14] proposed a method for constructing a Uyghur-named entity and relation corpus to expand the size of the existing corpus. 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.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, relatively few annotated named Uyghur entity relation data are available on the internet. In addition, we note that the construction of knowledge graphs is quite language-dependent [14]. Currently, most knowledge graph systems are developed in the English language and then directly translated into the language of interest.…”

Section: Introductionmentioning

confidence: 99%

“…Uyghur is a type of morphologically-rich agglutinative language that is used by approximately 10 million people in the Xinjiang Uyghur autonomous region of China. Uyghur words are formed by a root followed by suffixes [14,15]; therefore, the size of the vocabulary is huge. Officially, Arabic and Latin scripts are used in Uyghur, while Latin scripts are also widely used on social networks and mobile communications.…”

Section: Introductionmentioning

confidence: 99%

“…Here, the 9103 sentences listed represent filtered sentences that were obtained from a total number of 17,765 sentences. This can address the first and second problems in the research of [14].…”

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confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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

2020

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Construction of an English-Uyghur WordNet Dataset

Abiderexiti

Sun

2019

Lecture Notes in Computer Science

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Constructing Uyghur Named Entity Recognition System Using Neural Machine Translation Tag Projection

Anwar

Xiao

Yang

et al. 2020

Lecture Notes in Computer Science

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Although named entity recognition achieved great success by introducing the neural networks, it is challenging to apply these models to low resource languages including Uyghur while it depends on a large amount of annotated training data. Constructing a well-annotated named entity corpus manually is very time-consuming and labor-intensive. Most existing methods based on the parallel corpus combined with the word alignment tools. However, word alignment methods introduce alignment errors inevitably. In this paper, we address this problem by a named entity tag transfer method based on the common neural machine translation. The proposed method marks the entity boundaries in Chinese sentence and translates the sentences to Uyghur by neural machine translation system, hope that neural machine translation will align the source and target entity by the self-attention mechanism. The experimental results show that the Uyghur named entity recognition system trained by the constructed corpus achieve good performance on the test set, with 73.80% F1 score(3.79% improvement by baseline).

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Annotation schemes for constructing Uyghur named entity relation corpus

Cited by 4 publications

References 14 publications

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

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

Construction of an English-Uyghur WordNet Dataset

Constructing Uyghur Named Entity Recognition System Using Neural Machine Translation Tag Projection

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