The DOI matches its Crossref record; 121 of the 121 references it cites were found in a registry or an index.
Recent Named Entity Recognition and Classification techniques: A systematic review
Abstract
Abstract Textual information is becoming available in abundance on the web, arising the requirement of techniques and tools to extract the meaningful information. One of such an important information extraction task is Named Entity Recognition and Classification. It is the problem of finding the members of various predetermined classes, such as person, organization, location, date/time, quantities, numbers etc. The concept of named entity extraction was first proposed in Sixth Message Understanding Conference in 1996. Since then, a number of techniques have been developed by many researchers for extracting diversity of entities from different languages and genres of text. Still, there is a growing interest among research community to develop more new approaches to extract diverse named entities which are helpful in various natural language applications. Here we present a survey of developments and progresses made in Named Entity Recognition and Classification research.
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Cites
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Metadata
- Authors
- Goyal, A., Gupta, V., & Kumar, M.
- Venue
- Computer Science Review
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 29
- Pages
- 21-43
- ISSN
- 1574-0137 (ISSN-L)
- OpenAlex
- W2807800730
- Cited by
- 309
- References
- 121
- FWCI
- 5.35
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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