Next Article in Journal
Short-Term Solar Irradiance Forecasting Based on a Hybrid Deep Learning Methodology
Previous Article in Journal
Named-Entity Recognition in Sports Field Based on a Character-Level Graph Convolutional Network
Open AccessArticle

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

1
School of Information Science and Engineering, Xinjiang University, Urumqi 830046, Xinjiang, China
2
Multilingual Information Technology Laboratory of Xinjiang University, Urumqi 830046, Xinjiang, China
*
Author to whom correspondence should be addressed.
Information 2020, 11(1), 31; https://doi.org/10.3390/info11010031
Received: 26 November 2019 / Revised: 31 December 2019 / Accepted: 4 January 2020 / Published: 6 January 2020
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. View Full-Text
Keywords: relation extraction; named entity; hybrid neural network; conditional random field; Uyghur relation extraction; named entity; hybrid neural network; conditional random field; Uyghur
Show Figures

Figure 1

MDPI and ACS Style

Halike, A.; Abiderexiti, K.; Yibulayin, T. Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method. Information 2020, 11, 31.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop