What is Natural Language Processing? Natural Language Processing or NLP is the sub-field of Artificial Intelligence that deals with the study of natural language. In layman terms, it is the process of finding insights from unstructured textual data by applying the necessary technical and business skills pertaining to the problem statement. From understanding sentiments to topic identification in textual data, the various Natural Language Processing Techniques are state-of-the-art technology in the modern world.
Python is the ideal language for any NLP task. In this natural language processing tutorial, you would come across models that could help you in your next project. The pre-trained models possess the transfer learning capabilities which has made natural language processing in ai faster and cheaper. The NLP model could be pre-trained and then fine-tuned to be used in various applications. NLP learns both lower and higher-level language features because of this which improves the performance of the model.
Below are the six pre-trained Natural Language Processing models –
To build NLP models, ELMo or Embedding from Language Models are very useful. It is a state-of-the-art model that is used mostly in modeling syntax, semantics, or any other complex word characteristics. The model is trained on a huge text corpus and learned from biLM or deep bidirectional language models. A few of the problems across the Natural Language Processing applications such as sentiment analysis, question-answering could be solved using the pre-trained ELMo model.
The contextual representation of each word provided by ELMo is a result of its deeply trained neural network which uses morphological analysis for unseen vocabulary in the training data. ELMo could be an ideal alternative to GloVe or other word vectors.
Designed by Jeremy Howard and Sebastian Ruder of fast.ai and DeepMind respectively, ULMFiT or Universal Language Model Fine-Tuning initiated the start of transfer learning altogether. Transfer Learning allows reusing the model already built for a task. In NLP or computer vision problems where time and recourse is an issue, the idea of transfer learning could be used to improve the model as well as speed up the training process.
ULMFiT is one such deep learning in natural language processing pre-trained model that promotes transfer learning and could also reduce the error on any dataset by almost 25 percent. Some of the Natural Language Processing applications where it could be used are – finding relevant documents, spam identification, text classification, and so on.
All the recent developments in Natural Language Processing has its core in the Transformer architecture which was introduced by Google in 2017. Now, the attention mechanism is a process of connecting an encoder and a decoder. In layman terms, the important parts of an input sequence are decided by the attention-mechanism at each step. A transformer is one such pre-trained model that relies on the attention mechanism architecture.
This image is the architecture of a Transformer. In time-series forecasting, Transformer could be used which outperforms the RNN and CNN. The Transformer-XL is an updated version of Transform.
The Bert or Bidirectional Encoder Representations framework was published and open-sourced by Google which generalizes to a variety of NLP tasks. It works on a pre-training contextual representation and is the first deeply, bidirectional, unsupervised language.
BERT allows training a question-answering system in 30 minutes. It could also be used to extract entities from text, sentiment analysis, etc. The bidirectional characteristics of BERT allow to language model which works much better and has a deeper sense of context. In just a few years, the NLP model could be trained using BERT.
For long term Natural Language Processing tasks, Google’s Transformer-XL could play a very important role. The Transformer model faces challenges while implementing the long-range dependencies in sequential data. It is unable to model the dependencies beyond the fixed length and also fails in respecting the sentence boundaries in shorter sentences.
This issue is resolved by the Transformer-XL model as it allows relative positional encoding scheme and segment level recurrence mechanism. Dependencies that are 450 percent longer than Transformers could be learned by the Transformer-XL model and is also 1800 times faster than the vanilla Transformer model. The SOTA bpc is also improved by this model as a result of its long-term dependency.
The GPT-2 model of OpenAI which raised some eyebrows because of its unprecedented potential reaches state-of-the-art in 2018. In forty gigabytes of text, GPT-2 could predict the next word. The initial light-version was released for research purpose due to the chance of using it for malicious activities.
It is an unsupervised learning model that doesn’t require any fine-tuning, unlike ELMo. Using the conditional sample generation command, a text could be generated and you could use GPT-2 to build a simple model. The original GPT-2 model has 1.5 billion parameters while the sample model has 117 million.
The pre-trained model is one of the exciting things surrounding Natural Language Processing examples and several papers have been published regarding that. Performing analysis on a huge corpus could be a daunting task but the pre-trained models could be used as a base only to fine-tune it for different tasks. The model performance could be improved using a few of these pre-trained models.