Open AccessJournal Article
Natural Language Processing (Almost) from Scratch
Reads0
Chats0
TLDR
A unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling is proposed.Abstract:
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including part-of-speech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid task-specific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting man-made input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.read more
Citations
More filters
Posted Content
On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach
Weizhong Yan,Lijie Yu +1 more
TL;DR: This paper uses recently-developed deep learning in machine learning to hierarchically learn features from the sensor measurements of exhaust gas temperatures and uses the learned features as the input to a neural network classifier for performing combustor anomaly detection.
Journal ArticleDOI
Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions
TL;DR: A general overview of AI, including its definitions, history and state-of-the-art methodologies, and a comprehensive review of its applications to security assessment, stability assessment, fault diagnosis, and stability control in smart grids are presented.
Posted Content
Combining Neural Networks and Log-linear Models to Improve Relation Extraction
Thien Huu Nguyen,Ralph Grishman +1 more
TL;DR: This paper proposes to combine the traditional feature-based method, the convolutional and recurrent neural networks to simultaneously benefit from their advantages and results in the state-of-the-art performance on the ACE 2005 and SemEval dataset.
Posted Content
Improved Relation Extraction with Feature-Rich Compositional Embedding Models
TL;DR: A Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement that outperforms both previous compositional models and traditional feature rich models.
Proceedings Article
Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network
TL;DR: A deep LSTM structure is applied to classify dialogue acts (DAs) in open-domain conversations and it is found that the word embeddings parameters, dropout regularization, decay rate and number of layers are the parameters that have the largest effect on the final system accuracy.
References
More filters
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.
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Journal ArticleDOI
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.