ChocoWu/LasUIE
Universal Information Extraction, codes for the NeurIPS-2022 paper: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model.
This project helps natural language processing practitioners quickly and accurately extract specific pieces of information from unstructured text. It takes raw text documents as input and outputs structured information, such as identified entities (like names or locations), relationships between them (like 'person works at company'), or even more complex event structures. A data analyst, content manager, or researcher working with large volumes of text could use this to automate data extraction.
No commits in the last 6 months.
Use this if you need to precisely identify and extract specific spans of text, relationships between them, or complex event structures from various kinds of documents.
Not ideal if your primary goal is general text summarization, sentiment classification without specific entity extraction, or tasks that don't involve pulling out structured facts.
Stars
55
Forks
5
Language
Python
License
Apache-2.0
Category
Last pushed
Jun 14, 2023
Commits (30d)
0
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