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Relation Extraction

112 papers with code · Natural Language Processing

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OpenNRE: An Open and Extensible Toolkit for Neural Relation Extraction

28 Sep 2019thunlp/OpenNRE

OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement neural models for relation extraction (RE).

INFORMATION RETRIEVAL QUESTION ANSWERING RELATION EXTRACTION

A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks

14 Nov 2018huggingface/hmtl

The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model.

MULTI-TASK LEARNING NAMED ENTITY RECOGNITION RELATION EXTRACTION

ERNIE: Enhanced Language Representation with Informative Entities

ACL 2019 thunlp/ERNIE

Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.

ENTITY TYPING KNOWLEDGE GRAPHS NATURAL LANGUAGE INFERENCE SENTIMENT ANALYSIS

Simplifying Graph Convolutional Networks

19 Feb 2019Tiiiger/SGC

Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.

GRAPH REGRESSION IMAGE CLASSIFICATION RELATION EXTRACTION SENTIMENT ANALYSIS SKELETON BASED ACTION RECOGNITION TEXT CLASSIFICATION

Indirect Supervision for Relation Extraction using Question-Answer Pairs

30 Oct 2017shanzhenren/CoType

However, due to the incompleteness of knowledge bases and the context-agnostic labeling, the training data collected via distant supervision (DS) can be very noisy.

QUESTION ANSWERING RELATION EXTRACTION

CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases

27 Oct 2016shanzhenren/CoType

We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations.

RELATION EXTRACTION