Qznan/SpanKL
Code for paper: A Neural Span-Based Continual Named Entity Recognition Model
This project offers a system for identifying and categorizing specific terms, like names, organizations, or locations, within text documents as new types of terms emerge over time. It takes in text with various named entities and, through a continuous learning process, outputs text where these entities are accurately recognized and labeled. This is useful for computational linguists, NLP researchers, or anyone building information extraction systems that need to adapt to evolving terminologies.
No commits in the last 6 months.
Use this if you need to build or evaluate a named entity recognition (NER) system that can incrementally learn to identify new types of entities without forgetting previously learned ones.
Not ideal if you are looking for a ready-to-use, pre-trained NER model for a fixed set of entity types without any need for continual adaptation.
Stars
18
Forks
3
Language
Python
License
Apache-2.0
Category
Last pushed
Dec 11, 2023
Commits (30d)
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