In the field of natural language processing, deep learning will give the greatest help. The deep learning method mainly relies on these five key advantages. Reading this article will further understand the important deep learning methods and applications of natural language processing.
In the field of natural language processing, the promise of deep learning is to bring better performance to new models. These new models may require more data, but no longer require so much linguistic expertise.
There are a lot of hype and big talk about deep learning methods, but in addition to hype, deep learning methods are getting the most advanced results for challenging problems, especially in the field of natural language processing.
In this article, you'll see the specific prospects of deep learning methods for natural language processing problems. After reading this article, you will know:
1. The promise of natural language processing for deep learning.
2. What do deep learning practitioners and research scientists have to say about the promise of natural language processing for deep learning?
3. Important deep learning methods and applications for natural language processing.
Commitment to deep learningThe method of deep learning is very popular, mainly because they honor the original promise.
This is not to say that there is no technical hype, but rather that this kind of hype is based on very real results. These results are being confirmed by a series of challenging artificial intelligence issues in computer vision and natural language processing.
The first large-scale presentation of deep learning power is in the field of natural language processing, especially in speech recognition. Recent developments have been in machine translation.
In this article, we will see five specific commitments to the deep learning approach in the field of natural language processing. These commitments have been recently emphasized by researchers and practitioners in this field, and these people's attitudes toward these commitments are much more restrained than the average news report.
All in all, these commitments are:Deep learning inserts replace existing models. The deep learning method can be inserted into an existing natural language system, and the resulting new model can achieve equal or better performance.
New NLP model. Deep learning methods provide new modeling methods to challenge natural language problems (such as sequence-sequence prediction).
Feature learning. The deep learning method can learn features from the natural language required by the model without the need for experts to specify and extract features. ,
keep improve. The performance of deep learning in natural language processing is based on real-world results, and the improvements that are brought about are ongoing and may accelerate.
End-to-end model. Large end-to-end deep learning models can accommodate natural language problems and provide a more general and better approach.
We will now take a closer look at each of these commitments. In fact, there are some other commitments in natural language processing deep learning; these are just the five most prominent choices I have chosen.
Deep learning insert replaces existing modelThe first promise of deep learning in natural language processing is the ability to replace existing linear models with models with better performance, and to learn and utilize nonlinear relationships.
Yoav Goldberg emphasized in his "Introduction to NLP Researchers' Neural Networks" that the deep learning approach has yielded impressive results, he said in this article: "Recently, neural network models have also begun to apply to text natural language signals, And again brought very promising results."
He also continues to emphasize that these methods are easy to use and can sometimes be used to replace existing linear methods in bulk. He said: "Recently, the field has achieved some success in switching from a linear model of sparse input to a nonlinear neural network model of dense data. Most neural network techniques are easy to apply and sometimes can replace old linear classifications. However, in many cases there are still obstacles to using neural networks."
New NLP modelAnother promise is that deep learning methods help to develop new models.
A good example is the use of a circular neural network that can learn and judge the output of very long sequences. This approach is completely different from the previous ones because they allow NLP practitioners to get rid of traditional modeling assumptions and achieve state-of-the-art results.
Yoav Goldberg's NLP Deep Learning Monograph "Nerve Language Approach to Natural Language Processing" on page xvii states that complex neural network models like recurrent neural networks can bring new opportunities for NLP modeling. He said, "In 2014, the field has begun to see some success in the transition from a linear model of sparse input to a dense neural network model of dense input.... Other changes are more advanced. Researchers need to change their thinking and bring new modeling opportunities. In particular, a series of methods based on cyclic neural networks (RNNs) alleviate the dependence on the ubiquitous Markov assumptions in the sequence model, allowing arbitrary Long sequences are conditional and produce efficient feature extractors. These advances have led to breakthroughs in language modeling, automated machine translation and other applications."
Feature learningThe deep learning method has the ability to learn feature representations without requiring the expert to manually specify and extract features from natural language.
NLP researcher Chris Manning highlighted this point in the first lecture of the Natural Language Processing Deep Learning course.
He describes the limitations of manually defining input features: In this way, in previous applications, machine learning only proved human-defined features in statistical NLP, and the computer had little to learn.
Chris believes that the promise of deep learning methods is automatic feature learning. He emphasized that feature learning is automatic, not artificial; it is easy to adapt, not fragile, and can be continuously and automatically improved.
Chris Mining said in the first lecture of the 2017 Natural Language Processing and Deep Learning Lecture, “In general, our artificially designed features are often over-specified, they are incomplete and take a long time to design and Verification will allow you to achieve a limited level of performance after a busy day. Deep-learned features are easy to adapt and can be quickly trained, and they can continue to learn in order to achieve a better level of performance that was previously unachievable.
keep improveAnother commitment of NLP's deep learning is continuous improvement on challenging issues.
In the first lecture of "Natural Language Processing and Deep Learning", Chris Manning said that the method of deep learning is very popular because they are very useful. “The real reason why deep learning is so exciting for most people is that it really works,†he said.
He emphasized that the preliminary results of in-depth learning are impressive. Deep learning is better in the voice field than any other method in the past 30 years.
Chris mentioned that deep learning brings not only the most advanced results, but also the speed of improvement that is constantly improving. He said, "... In the past six or seven years, it was amazing that the method of deep learning has been constantly improving and getting better at an alarming rate. I actually want to say that this is unprecedented, I Seeing this field progressing rapidly, a better approach is introduced every month."
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