neural-networks-for-attitude-extraction
ARElight
neural-networks-for-attitude-extraction | ARElight | |
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1 | 3 | |
2 | 35 | |
- | - | |
0.0 | 8.7 | |
over 1 year ago | about 1 month ago | |
Python | Python | |
MIT License | MIT License |
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neural-networks-for-attitude-extraction
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Show HN: ARElight – A Mass-Media Processing Application for Relation Extraction
https://github.com/nicolay-r/neural-networks-for-attitude-ex...
Among all the other personal projects, we adopt BRAT only in ARElight. For now, there is no paper specifically devoted to ARElight only.
ARElight
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Hacker News top posts: Jun 19, 2022
Show HN: ARElight – A Mass-Media Processing Application for Relation Extraction\ (8 comments)
- Show HN: ARElight – A Mass-Media Processing Application for Relation Extraction
What are some alternatives?
neural-networks-for-attitude-ex
zshot - Zero and Few shot named entity & relationships recognition
awesome-sentiment-attitude-extraction - A curated list of awesome sentiment analysis studies, in which attitude corresponds to the text position conveyed by Subject towards other Object mentioned in text such as: entities, events, etc.
DeepKE - [EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction
FinBERT-QA - Financial Domain Question Answering with pre-trained BERT Language Model
ABSA-PyTorch - Aspect Based Sentiment Analysis, PyTorch Implementations. 基于方面的情感分析,使用PyTorch实现。
typeish - A runtime type checker for bash.... in bash. I'm not sorry.
PIXIU - This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).
AREkit - Document level Attitude and Relation Extraction toolkit (AREkit) for sampling and processing large text collections with ML and for ML
BERT-Relation-Extraction - PyTorch implementation for "Matching the Blanks: Distributional Similarity for Relation Learning" paper