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FastNER

FastNER is an speed-optimized rule-base name entity recognition solution. It uses n-trie engine[1] internally to builds a hash-trie structure from rules and processing the input text without iterating through every rule.

Maven setup

<dependency>
    <groupId>edu.utah.bmi.nlp</groupId>
    <artifactId>fastner</artifactId>
    <version>1.3.1.8</version>
</dependency>

Examples

Use of FastNER is simple. Some example codes are here:

Examples for token-based rules

Examples for character-based rules

References

If you are using FastNER within your research work, please cite the following publication:

  1. Shi, Jianlin, and John F. Hurdle. “Trie-Based Rule Processing for Clinical NLP: A Use-Case Study of n-Trie, Making the ConText Algorithm More Efficient and Scalable.” Journal of Biomedical Informatics, August 6, 2018. https://doi.org/10.1016/j.jbi.2018.08.002.