Journal ArticleDOI
Joint entity and relation extraction based on a hybrid neural network
TLDR
A hybrid neural network model is proposed to extract entities and their relationships without any handcrafted features to achieve the state-of-the-art results on entity and relation extraction task.About:
This article is published in Neurocomputing.The article was published on 2017-09-27. It has received 200 citations till now. The article focuses on the topics: Relationship extraction & Automatic Content Extraction.read more
Citations
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Journal ArticleDOI
A Survey of the Usages of Deep Learning for Natural Language Processing
TL;DR: The field of natural language processing has been propelled forward by an explosion in the use of deep learning models over the last several years as mentioned in this paper, which includes several core linguistic processing issues in addition to many applications of computational linguistics.
Journal ArticleDOI
Joint entity recognition and relation extraction as a multi-head selection problem
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.
Proceedings ArticleDOI
Entity-Relation Extraction as Multi-Turn Question Answering
TL;DR: This article cast the task of entity-relation extraction as a multi-turn question answering problem, i.e., the extraction of entities and elations is transformed to identifying answer spans from the context.
Proceedings ArticleDOI
Adversarial training for multi-context joint entity and relation extraction
TL;DR: Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data as mentioned in this paper.
Posted Content
A Survey of the Usages of Deep Learning in Natural Language Processing
TL;DR: An introduction to the field and a quick overview of deep learning architectures and methods is provided and a discussion of the current state of the art is provided along with recommendations for future research in the field.
References
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Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Proceedings Article
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
TL;DR: This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
Journal ArticleDOI
Maximum entropy modeling of species geographic distributions
TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
Posted Content
Convolutional Neural Networks for Sentence Classification
TL;DR: In this article, CNNs are trained on top of pre-trained word vectors for sentence-level classification tasks and a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks.