Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network

Wu, Yue; Zhang, Junyi

doi:10.3390/fi10100095

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…Zeng et al 14 uses a bidirectional recurrent neural network to extract sentence features and uses the CNN network to extract lexical features, which reduces the impact of Chinese word segmentation errors and improves the performance of Chinese event extraction. Wu et al 15 proposes a neural network model based on semantic features and attention mechanism, which uses word vector information and attention mechanism to generate word vectors. They combine external semantic features to improve the quality of word vectors and achieves good results in event extraction tasks.…”

Section: Related Workmentioning

confidence: 99%

Automatic work‐order assignment method for Chinese government hotline

Chen¹,

She²,

Chen³

et al. 2022

Engineering Reports

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Government hotline plays a significant role in meeting the demands of the people and resolving social conflicts in China. In this paper, we propose an automatic work‐order assignment method based on event extraction and external knowledge to address the problem of low efficiency with manual assignment for Chinese government hotline. Our proposed assignment method is composed of four parts: (1) Semantic encoding layer, which extracts semantic information from the work‐order text and obtains semantic representation vectors with contextual feature information. (2) Event extraction layer which extracts the local features and global features from the semantic representation vectors with the help of the CRF network to enhance event extraction effect. (3) External knowledge embedding layer, which integrates ‘rights and responsibilities lists’ with the historical information of the work‐order to assist assignment. (4) Assignment layer which completes work‐order assignment by combining two output vectors from event extraction layer and external knowledge embedding layer. Experimental results show our proposed method can achieve better assignment performance compared with several baseline methods.

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Section: Related Workmentioning

confidence: 99%

Automatic work‐order assignment method for Chinese government hotline

Chen¹,

She²,

Chen³

et al. 2022

Engineering Reports

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“…Many word-level attention mechanisms have been proposed to learn the importance of each word in a sentence, like distinguishing different argument words, word types, word relations [197]- [202]. They mainly differ in what elements should be given more attentions and how to train attention vectors.…”

Section: E Attention Mechanismmentioning

confidence: 99%

“…The basic idea is that the syntactic dependency can provide the connection in between two possibly nonconsecutive yet distant words; While the dependency type can help to distinguish the syntactic importance in between words. For Chinese event extraction, as there has no explicit word segmentation like English, Wu et al [202] also proposed a character-level attention mechanism to distinguish each character importance in a Chinese word.…”

Section: E Attention Mechanismmentioning

confidence: 99%

A Survey of Event Extraction From Text

2019

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Numerous important events happen everyday and everywhere but are reported in different media sources with different narrative styles. How to detect whether real-world events have been reported in articles and posts is one of the main tasks of event extraction. Other tasks include extracting event arguments and identifying their roles, as well as clustering and tracking similar events from different texts. As one of the most important research themes in natural language processing and understanding, event extraction has a wide range of applications in diverse domains and has been intensively researched for decades. This article provides a comprehensive yet up-to-date survey for event extraction from text. We not only summarize the task definitions, data sources and performance evaluations for event extraction, but also provide a taxonomy for its solution approaches. In each solution group, we provide detailed analysis for the most representative methods, especially their origins, basics, strengths and weaknesses. Last, we also present our envisions about future research directions. INDEX TERMS Event extraction, event extraction tasks, event corpus, natural language processing.

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“…They combined the distributed word vector, the part-of-speech feature POS of the medical text and the named entity feature NE as the input of the model. Wu and Zhang [23] used binary representation model to construct word vectors based on part of speech, dependencies and distance from core words. The BiLSTM-CRF model was used to implement Chinese event extraction and achieved good results.…”

Section: B Word Representationmentioning

confidence: 99%

Chinese Event Detection Based on Multi-Feature Fusion and BiLSTM

Meng

Zhou

et al. 2019

IEEE Access

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With the rapid development of the Internet, the number of Internet users has grown rapidly, and the Internet has become more and more influential on people's lives. As a result, the amount of network text is increasing rapidly, and it is difficult to extract interested event information from it only by manual reading. Therefore, event extraction technique automatically extracting useful information from a large amount of unstructured texts becomes increasingly important. Event detection is the first step of event extraction task and plays a vital role in it. However, current event detection research lacks comprehensive consideration of the context of the trigger words. A Chinese event detection method based on multi-feature fusion and BiLSTM is proposed in this paper. The contextual information of word is divided into sentence-level and document-level in the method. The contextual information is captured based on BiLSTM model. At the same time, a word representation method suitable for trigger word classification tasks is proposed in this paper. The word representation incorporates semantic information, grammar information, and document-level context information of word. The word vectors in the sentence are sequentially inputted into BiLSTM model to obtain output vectors containing sentence-level contextual information. Finally, output vectors of BiLSTM are inputted into the Softmax classifier to realize the identification of the trigger words. The experimental results show that Chinese Event Detection Based on Multi-feature Fusion and BiLSTM method proposed in this paper has high accuracy.

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Chinese Event Extraction Based on Attention and Semantic Features: A Bidirectional Circular Neural Network

Cited by 9 publications

References 9 publications

Automatic work‐order assignment method for Chinese government hotline

Automatic work‐order assignment method for Chinese government hotline

A Survey of Event Extraction From Text

Chinese Event Detection Based on Multi-Feature Fusion and BiLSTM

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