OpenUE: An Open Toolkit of Universal Extraction from Text

Natural language processing covers a wide variety of tasks with token-level or sentence-level understandings. In this paper, we provide a simple insight that most tasks can be represented in a single universal extraction format. We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks. OpenUE allows developers to train custom models to extract information from the text and supports quick model validation for researchers. Besides, OpenUE provides various functional modules to maintain sufficient modularity and extensibility. Except for the toolkit, we also deploy an online demo with restful APIs to support real-time extraction without training and deploying. Additionally, the online system can extract information in various tasks, including relational triple extraction, slot & intent detection, event extraction, and so on. We release the source code, datasets, and pre-trained models to promote future researches in http://github.com/zjunlp/openue.

[1]  Jun Zhao,et al.  Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism , 2018, ACL.

[2]  Zhifang Sui,et al.  Jointly Extracting Event Triggers and Arguments by Dependency-Bridge RNN and Tensor-Based Argument Interaction , 2018, AAAI.

[3]  Xi Chen,et al.  Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks , 2019, NAACL.

[4]  Lingfei Wu,et al.  Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward , 2020, ACL.

[5]  Tianyang Zhang,et al.  A Hierarchical Framework for Relation Extraction with Reinforcement Learning , 2018, AAAI.

[6]  Graham Neubig,et al.  Generalizing Natural Language Analysis through Span-relation Representations , 2020, ACL.

[7]  Dan Wu,et al.  Conditional Random Fields with High-Order Features for Sequence Labeling , 2009, NIPS.

[8]  Jiwei Li,et al.  A Unified MRC Framework for Named Entity Recognition , 2019, ACL.

[9]  Andrew McCallum,et al.  Modeling Relations and Their Mentions without Labeled Text , 2010, ECML/PKDD.

[10]  Gökhan Tür,et al.  What is left to be understood in ATIS? , 2010, 2010 IEEE Spoken Language Technology Workshop.

[11]  Chih-Li Huo,et al.  Slot-Gated Modeling for Joint Slot Filling and Intent Prediction , 2018, NAACL.

[12]  Wei Lu,et al.  Reasoning with Latent Structure Refinement for Document-Level Relation Extraction , 2020, ACL.

[13]  Wen Wang,et al.  BERT for Joint Intent Classification and Slot Filling , 2019, ArXiv.

[14]  Christopher D. Manning,et al.  Leveraging Linguistic Structure For Open Domain Information Extraction , 2015, ACL.

[15]  Xiao Liu,et al.  Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation , 2018, EMNLP.

[16]  Maosong Sun,et al.  OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction , 2019, EMNLP.

[17]  Ruhi Sarikaya,et al.  Convolutional neural network based triangular CRF for joint intent detection and slot filling , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[18]  Yiming Yang,et al.  XLNet: Generalized Autoregressive Pretraining for Language Understanding , 2019, NeurIPS.

[19]  Gökhan Tür,et al.  Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM , 2016, INTERSPEECH.

[20]  Wei Zhang,et al.  Can Fine-tuning Pre-trained Models Lead to Perfect NLP? A Study of the Generalizability of Relation Extraction , 2020, ArXiv.

[21]  Huajun Chen,et al.  Contrastive Triple Extraction with Generative Transformer , 2020, ArXiv.

[22]  Jun Zhao,et al.  Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks , 2015, ACL.

[23]  Huajun Chen,et al.  Relation Adversarial Network for Low Resource Knowledge Graph Completion , 2020, WWW.

[24]  Yue Wang,et al.  A Novel Cascade Binary Tagging Framework for Relational Triple Extraction , 2019, ACL.

[25]  Philip S. Yu,et al.  Joint Slot Filling and Intent Detection via Capsule Neural Networks , 2018, ACL.

[26]  Sampo Pyysalo,et al.  brat: a Web-based Tool for NLP-Assisted Text Annotation , 2012, EACL.

[27]  Wei Zhang,et al.  Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction , 2018, EMNLP.

[28]  Shashi Narayan,et al.  Creating Training Corpora for NLG Micro-Planners , 2017, ACL.

[29]  Bo Xu,et al.  Joint entity and relation extraction based on a hybrid neural network , 2017, Neurocomputing.

[30]  Xavier Carreras,et al.  Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling , 2005, CoNLL.

[31]  Zhiyuan Liu,et al.  OpenKE: An Open Toolkit for Knowledge Embedding , 2018, EMNLP.

[32]  Bing Liu,et al.  Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling , 2016, INTERSPEECH.

[33]  Bowen Zhou,et al.  Improved Neural Relation Detection for Knowledge Base Question Answering , 2017, ACL.

[34]  Paolo Ferragina,et al.  TAGME: on-the-fly annotation of short text fragments (by wikipedia entities) , 2010, CIKM.

[35]  Huajun Chen,et al.  Meta-Learning with Dynamic-Memory-Based Prototypical Network for Few-Shot Event Detection , 2020, WSDM.