Joint entity recognition and relation extraction as a multi-head selection problem (2018-04-20T00:00:00.000000Z)

TL;DR

The proposed joint neural model outperforms the previous neural models that use automatically extracted features, while it performs within a reasonable margin of feature-based neural models, or even beats them.

Authors

Johannes Deleu

4 papers

Chris Develder

4 papers

Thomas Demeester

4 papers

Giannis Bekoulis

3 papers

TL;DR

Authors

References57 items

An attentive neural architecture for joint segmentation and parsing and its application to real estate ads

Joint entity and relation extraction based on a hybrid neural network

Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees

Global Normalization of Convolutional Neural Networks for Joint Entity and Relation Classification

Reconstructing the house from the ad: Structured prediction on real estate classifieds

A neural joint model for entity and relation extraction from biomedical text

Bon-EV: an improved multiple testing procedure for controlling false discovery rates

Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction

Joint Models for Extracting Adverse Drug Events from Biomedical Text

Dependency Parsing as Head Selection

Combining Recurrent and Convolutional Neural Networks for Relation Classification

Neural Architectures for Named Entity Recognition

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures

Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths

Bidirectional LSTM-CRF Models for Sequence Tagging

Relation Classification via Recurrent Neural Network

Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling

Classifying Relations by Ranking with Convolutional Neural Networks

Adam: A Method for Stochastic Optimization

Modeling Joint Entity and Relation Extraction with Table Representation

Sequence to Sequence Learning with Neural Networks

Relation Classification via Convolutional Deep Neural Network

Incremental Joint Extraction of Entity Mentions and Relations

Reasoning With Neural Tensor Networks for Knowledge Base Completion

A Review of Relation Extraction

Distributed Representations of Words and Phrases and their Compositionality

Joint Inference for Fine-grained Opinion Extraction

Extraction of potential adverse drug events from medical case reports

On the difficulty of training recurrent neural networks

Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports

Semantic Compositionality through Recursive Matrix-Vector Spaces

Natural Language Processing (Almost) from Scratch

UTD: Classifying Semantic Relations by Combining Lexical and Semantic Resources

Joint Entity and Relation Extraction Using Card-Pyramid Parsing

Combining Lexical, Syntactic, and Semantic Features with Maximum Entropy Models for Information Extraction

Dependency Tree Kernels for Relation Extraction

Support vector machine learning for interdependent and structured output spaces

A Linear Programming Formulation for Global Inference in Natural Language Tasks

Max-Margin Markov Networks

Kernel Methods for Relation Extraction

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

Learning long-term dependencies with gradient descent is difficult

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Model performance for different embeddings on the ACE04 dataset. Bold entries indicate the result reported

Dropout: a simple way to prevent neural networks from overfitting

Distributional Semantics Resources for Biomedical Text Processing

A survey of named entity recognition and classification

Learning Long-Term Dependencies with

A Review of Relation Extraction

BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm115 Data and text mining

BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm261 Data and text mining

The Automatic Content Extraction (ACE) Program – Tasks, Data, and Evaluation

Empirical Methods in Natural Language Processing

Optimum branchings

On shortest arborescence of a directed graph

This Paper Is Included in the Proceedings of the 12th Usenix Symposium on Operating Systems Design and Implementation (osdi '16). Tensorflow: a System for Large-scale Machine Learning Tensorflow: a System for Large-scale Machine Learning

Field of Study

Journal Information

Name

Volume

Venue Information

Name

Type

URL

Alternate Names