DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

• URL: http://arxiv.org/abs/2201.03335v6
• Date: Mon, 18 Sep 2023 16:42:06 GMT
• Title: DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
• Authors: Ningyu Zhang, Xin Xu, Liankuan Tao, Haiyang Yu, Hongbin Ye, Shuofei Qiao, Xin Xie, Xiang Chen, Zhoubo Li, Lei Li, Xiaozhuan Liang, Yunzhi Yao, Shumin Deng, Peng Wang, Wen Zhang, Zhenru Zhang, Chuanqi Tan, Qiang Chen, Feiyu Xiong, Fei Huang, Guozhou Zheng, Huajun Chen
• Abstract summary: DeepKE implements various information extraction tasks, including named entity recognition, relation extraction and attribute extraction. DeepKE allows developers and researchers to customize datasets and models to extract information from unstructured data according to their requirements.
• Score: 95.0099875111663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base population. DeepKE implements various information extraction tasks, including named entity recognition, relation extraction and attribute extraction. With a unified framework, DeepKE allows developers and researchers to customize datasets and models to extract information from unstructured data according to their requirements. Specifically, DeepKE not only provides various functional modules and model implementation for different tasks and scenarios but also organizes all components by consistent frameworks to maintain sufficient modularity and extensibility. We release the source code at GitHub in https://github.com/zjunlp/DeepKE with Google Colab tutorials and comprehensive documents for beginners. Besides, we present an online system in http://deepke.openkg.cn/EN/re_doc_show.html for real-time extraction of various tasks, and a demo video.

Related papers

• Deep Learning with CNNs: A Compact Holistic Tutorial with Focus on Supervised Regression (Preprint) [0.0]
  This tutorial focuses on Convolutional Neural Networks (CNNs) and supervised regression. It not only summarizes the most relevant concepts but also provides an in-depth exploration of each, offering a complete yet agile set of ideas. We aim for this tutorial to serve as an optimal resource for students, professors, and anyone interested in understanding the foundations of Deep Learning.
  arXiv  Detail & Related papers  (2024-08-22T11:34:34Z)
• How to Understand Whole Software Repository? [64.19431011897515]
  An excellent understanding of the whole repository will be the critical path to Automatic Software Engineering (ASE) We develop a novel method named RepoUnderstander by guiding agents to comprehensively understand the whole repositories. To better utilize the repository-level knowledge, we guide the agents to summarize, analyze, and plan.
  arXiv  Detail & Related papers  (2024-06-03T15:20:06Z)
• PyTorch-IE: Fast and Reproducible Prototyping for Information Extraction [6.308539010172309]
  PyTorch-IE is a framework designed to enable swift, reproducible, and reusable implementations of Information Extraction models. We propose task modules to decouple the concerns of data representation and model-specific representations. PyTorch-IE also extends support for widely used libraries such as PyTorch-Lightning for training, HuggingFace datasets for dataset reading, and Hydra for experiment configuration.
  arXiv  Detail & Related papers  (2024-05-16T12:23:37Z)
• Instruct and Extract: Instruction Tuning for On-Demand Information Extraction [86.29491354355356]
  On-Demand Information Extraction aims to fulfill the personalized demands of real-world users. We present a benchmark named InstructIE, inclusive of both automatically generated training data, as well as the human-annotated test set. Building on InstructIE, we further develop an On-Demand Information Extractor, ODIE.
  arXiv  Detail & Related papers  (2023-10-24T17:54:25Z)
• Deep learning for table detection and structure recognition: A survey [49.09628624903334]
  The goal of this survey is to provide a profound comprehension of the major developments in the field of Table Detection. We provide an analysis of both classic and new applications in the field. The datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature.
  arXiv  Detail & Related papers  (2022-11-15T19:42:27Z)
• Adding Context to Source Code Representations for Deep Learning [13.676416860721877]
  We argue that it is beneficial for deep learning models to have access to additional contextual information about the code being analysed. We present preliminary evidence that encoding context from the call hierarchy along with information from the code itself can improve the performance of a state-of-the-art deep learning model.
  arXiv  Detail & Related papers  (2022-07-30T12:47:32Z)
• DeepAL: Deep Active Learning in Python [0.16317061277456998]
  DeepAL is a Python library that implements several common strategies for active learning. DeepAL is open-source on Github and welcome any contribution.
  arXiv  Detail & Related papers  (2021-11-30T10:17:58Z)
• KILT: a Benchmark for Knowledge Intensive Language Tasks [102.33046195554886]
  We present a benchmark for knowledge-intensive language tasks (KILT) All tasks in KILT are grounded in the same snapshot of Wikipedia. We find that a shared dense vector index coupled with a seq2seq model is a strong baseline.
  arXiv  Detail & Related papers  (2020-09-04T15:32:19Z)
• Deep Multimodal Neural Architecture Search [178.35131768344246]
  We devise a generalized deep multimodal neural architecture search (MMnas) framework for various multimodal learning tasks. Given multimodal input, we first define a set of primitive operations, and then construct a deep encoder-decoder based unified backbone. On top of the unified backbone, we attach task-specific heads to tackle different multimodal learning tasks.
  arXiv  Detail & Related papers  (2020-04-25T07:00:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.