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📢 If you're working in relation extraction / character network domain, then the following post would be relevant.
Excited to share the most recent milestone on releasing the ARElight 0.25.0 🎊
Core library: https://github.com/nicolay-r/ARElight
Server: https://github.com/nicolay-r/ARElight-server
🔎 What is ARElight? It represents Granular Viewer of Sentiments Between Entities in Massively Large Documents and Collections of Texts.
Shortly speaking, it allows to extract contexts with mentioned object pairs for the related prompting / classification.
In the slides below we illsutrate the ARElight appliation for sentiment classification between object pairs in context.
We exploit DeepPavlov NER modes + GoogleTranslate + BERT-based classifier in the demo. The bash script for launching the quick demo illustrates the application of these components.
The new update provide a series of new features:
✅ SQlite support for storing all the extracted samples
✅ Support of the enhanced GUI for content investigation.
✅ Switch to external no-string projects for NER and Translator
Supplementiary materials:
📜 Paper: https://link.springer.com/chapter/10.1007/978-3-031-56069-9_23
Excited to share the most recent milestone on releasing the ARElight 0.25.0 🎊
Core library: https://github.com/nicolay-r/ARElight
Server: https://github.com/nicolay-r/ARElight-server
🔎 What is ARElight? It represents Granular Viewer of Sentiments Between Entities in Massively Large Documents and Collections of Texts.
Shortly speaking, it allows to extract contexts with mentioned object pairs for the related prompting / classification.
In the slides below we illsutrate the ARElight appliation for sentiment classification between object pairs in context.
We exploit DeepPavlov NER modes + GoogleTranslate + BERT-based classifier in the demo. The bash script for launching the quick demo illustrates the application of these components.
The new update provide a series of new features:
✅ SQlite support for storing all the extracted samples
✅ Support of the enhanced GUI for content investigation.
✅ Switch to external no-string projects for NER and Translator
Supplementiary materials:
📜 Paper: https://link.springer.com/chapter/10.1007/978-3-031-56069-9_23