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
Proceedings ArticleDOI
Distributed Representations of Geographically Situated Language
TL;DR: In a quantitative evaluation on the task of judging geographically informed semantic similarity between representations learned from 1.1 billion words of geo-located tweets, the joint model outperforms comparable independent models that learn meaning in isolation.
Proceedings ArticleDOI
Tuning Multilingual Transformers for Language-Specific Named Entity Recognition
TL;DR: This paper addresses the problem of multilingual named entity recognition on the material of 4 languages: Russian, Bulgarian, Czech and Polish using the BERT model and uses a hundred languages multilingual model as base for transfer to the mentioned Slavic languages.
Journal ArticleDOI
Twitter and Research: A Systematic Literature Review Through Text Mining
TL;DR: This study systematically mines a large number of Twitter-based studies to characterize the relevant literature by an efficient and effective approach and finds that while 23.7% of topics did not show a significant trend, it is found that these hot and cold topics represent three categories: application, methodology, and technology.
Posted Content
Modelling‚ Visualising and Summarising Documents with a Single Convolutional Neural Network
TL;DR: A model is introduced that is able to represent the meaning of documents by embedding them in a low dimensional vector space, while preserving distinctions of word and sentence order crucial for capturing nuanced semantics.
Journal ArticleDOI
Adversarial learning for distant supervised relation extraction
TL;DR: A two layers fully-connected neural network is used as the generator and the Piecewise Convolutional Neural Networks (PCNNs) as the discriminator and experiment results show that the proposed GAN-based method is effective and performs better than state-of-the-art methods.
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.