Stars
The dataset contains 3 million attribute-value annotations across 1257 unique categories on 2.2 million cleaned Amazon product profiles. It is a large, multi-sourced, diverse dataset for product at…
Unofficial implementation for EMNLP-2020 paper: Multimodal Joint Attribute Prediction and Value Extraction for E-commerce Product
A Pytorch implementation of "Scaling Up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title" (ACL 2019).
Unofficial implementation of the paper "OpenTag: Open Attribute Value Extraction from Product Profiles"
Scaling Up Open Tagging from Tens to Thousands: Comprehension Empowered Attribute Value Extraction from Product Title
Streamlit UI for bert extractive summarization
📖 Extractive text summarizer
Bert extractive summarizer for vietnam's document
Easy to use extractive text summarization with BERT
Few-Shot Document-Level Event Argument Extraction: https://arxiv.org/abs/2209.02203
pkunlp-icler / TSAR
Forked from RunxinXu/TSARSource code for "A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction" @ NAACL 2022
Code for our NAACL-2022 paper DEGREE: A Data-Efficient Generation-Based Event Extraction Model.
CogIE: An Information Extraction Toolkit for Bridging Text and CogNet. ACL 2021
Official implementation of our work, GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction [AAAI 2021].
[EMNLP 2020] OpenUE: An Open Toolkit of Universal Extraction from Text
A package for fine-tuning Transformers with TPUs, written in Tensorflow2.0+
Code for the paper Biomedical Event Extraction with Hierarchical Knowledge Graphs
DeepEventMine: End-to-end Neural Nested Event Extraction from Biomedical Texts
Event Extraction by Answering (Almost) Natural Questions
Open-domain Event Extraction and Embedding for Natural Gas Market Prediction
Source code for EMNLP-IJCNLP 2019 paper "HMEAE: Hierarchical Modular Event Argument Extraction".
Extraction of the journalistic five W and one H questions (5W1H) from news articles: who did what, when, where, why, and how?