Illustration of closed-domain event extraction.
Abstract:
Numerous important events happen everyday and everywhere but are reported in different media sources with different narrative styles. How to detect whether real-world eve...Show MoreMetadata
Abstract:
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.
Illustration of closed-domain event extraction.
Published in: IEEE Access ( Volume: 7)
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- IEEE Keywords
- Index Terms
- Event Extraction ,
- Natural Language ,
- Applicability Domain ,
- Real-world Effectiveness ,
- Clustering Of Events ,
- Task Definition ,
- Neural Network ,
- Convolutional Neural Network ,
- Neural Model ,
- Recurrent Neural Network ,
- Pattern Of Effects ,
- Convolutional Neural Network Model ,
- Event Detection ,
- News Articles ,
- Pattern Matching ,
- Word Embedding ,
- Triggering Event ,
- Stress Triggers ,
- Class Position ,
- Named Entity Recognition ,
- Argument Role ,
- Recurrent Neural Network Structure ,
- Natural Language Processing Tasks ,
- Lexical Features ,
- Relation Extraction ,
- Linguistic Patterns ,
- Deep Learning ,
- Biomedical Domain ,
- Neural Network Structure ,
- Attention Mechanism
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Event Extraction ,
- Natural Language ,
- Applicability Domain ,
- Real-world Effectiveness ,
- Clustering Of Events ,
- Task Definition ,
- Neural Network ,
- Convolutional Neural Network ,
- Neural Model ,
- Recurrent Neural Network ,
- Pattern Of Effects ,
- Convolutional Neural Network Model ,
- Event Detection ,
- News Articles ,
- Pattern Matching ,
- Word Embedding ,
- Triggering Event ,
- Stress Triggers ,
- Class Position ,
- Named Entity Recognition ,
- Argument Role ,
- Recurrent Neural Network Structure ,
- Natural Language Processing Tasks ,
- Lexical Features ,
- Relation Extraction ,
- Linguistic Patterns ,
- Deep Learning ,
- Biomedical Domain ,
- Neural Network Structure ,
- Attention Mechanism
- Author Keywords