ARElight
awesome-sentiment-attitude-extraction
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| ARElight | awesome-sentiment-attitude-extraction | |
|---|---|---|
| 3 | 1 | |
| 35 | 15 | |
| - | - | |
| 8.9 | 5.0 | |
| 13 days ago | about 1 month ago | |
| Python | ||
| MIT License | - |
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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
awesome-sentiment-attitude-extraction
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Show HN: ARElight – A Mass-Media Processing Application for Relation Extraction
After looking at the BERT paper linked here https://github.com/nicolay-r/awesome-sentiment-attitude-extr... and at https://github.com/nicolay-r/AREkit
I was able to fully understand the purpose. I can read enough of the language in the screenshots to get a basic idea before reading those, but most English speakers probably aren't even going to know what США is :).
Here's what this project is from my understanding:
What are some alternatives?
zshot - Zero and Few shot named entity & relationships recognition
contract-discovery - Data and additional information regarding the paper: Contract Discovery. Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines (to appear in Findings of EMNLP).
DeepKE - [EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction
neural-networks-for-attitude-extraction - Source code for ISPRAS-2021 journal paper "Language Models Application in Sentiment Attitude Extraction Task" (in Russian)
FinBERT-QA - Financial Domain Question Answering with pre-trained BERT Language Model
neural-networks-for-attitude-ex
ABSA-PyTorch - Aspect Based Sentiment Analysis, PyTorch Implementations. 基于方面的情感分析，使用PyTorch实现。
AREkit - Document level Attitude and Relation Extraction toolkit (AREkit) for sampling and processing large text collections with ML and for ML
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).