Natural Language Processing and Event Extraction for Big Data
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).
Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 2344
Special Issue Editors
Interests: NLP; semantic data; word embedding; knowledge graph; question answering; big data analysis; digital twins; smart city; IoT; road traffic; air quality; sensor data; anomaly detection; sensor calibration
Special Issue Information
Dear Colleagues,
The amount of textual data shared on the Web is overwhelming, and specific techniques are required to manage it and gather knowledge. Event Extraction (EE) is a sub-task of Information Retrieval (IR) whose scope is to extract events automatically from the text, understand what is happening around the world and identify information about where and when it happened and who was involved.
EE has received considerable attention and has seen great progress in recent years. Several approaches have been developed distinguishing two types of EE: Sentence-level Event Extraction and Document-level Event Extraction. Hence, Natural Language Processing (NLP) techniques play a key role in this challenge allowing the extraction of structured information from freeform text. However, the works currently in the literature are not mature enough to allow for the identification of a definitive approach to the challenge of EE, each study addresses only a part of the problem and very often the proposed methodologies are language specific. The research field of EE is still open to different margins for improvement.
The scope of this Special Issue is to collect recent advances in NLP in the field of EE, focusing on techniques that are able to process text published on the Web, e.g., social media and online newspapers, and identify event descriptions (participants, location, and time).
The areas of interest of this Special Issue include (but are not limited to):
- Sentence-level Event Extraction;
- Document-level Event Extraction;
- Question answering;
- Named entity recognition and linking;
- Semantic technologies;
- Text categorization;
- Natural Language Understanding;
- Temporal annotation;
- Knowledge graph;
- Graph analysis;
- Event deduplication;
- Text similarity.
Dr. Federica Rollo
Dr. John Blake
Guest Editors
Manuscript Submission Information
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Keywords
- natural language processing
- text analysis
- event detection
- event extraction
- event analysis
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