What is natural language processing (NLP)?
Natural language processing (NLP) refers to the branch of computer science and more specifically, the branch of artificial intelligence or AI concerned with giving computers the ability to understand text and spoken words in much the same way human beings can.
NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real-time. There’s a good chance you’ve interacted with NLP in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences.
NLP tasks :
Several NLP tasks break down human text and voice data in ways that help the computer make sense of what it’s ingesting. Some of these tasks include the following:
- Speech recognition
- Part of speech tagging
- Word sense disambiguation
- Named entity recognition
- Co-refrence recognition
- Sentiment analysis
- Natural language generation
NLP tools and approaches:
Python and the natural language toolkit (NLTK). The Python programing language provides a wide range of tools and libraries for attacking specific NLP tasks. Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs.
Statistical NLP, machine learning and, deep learning :
Statistical NLP, which combines computer algorithms with machine learning and deep learning models to automatically extract, classify, and label elements of text and voice data and then assign a statistical likelihood to each possible meaning of those elements. Today, deep learning models and learning techniques based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) enable NLP systems that ‘learn’ as they work and extract ever more accurate meaning from huge volumes of raw, unstructured, and unlabelled text and voice data sets.
NLP use cases:
Natural language processing is the driving force behind machine intelligence in many modern real-world applications. Here are a few examples:
- Spam detection
- Machine translation
- Virtual agents & chatbots
- Social media sentiments analysis
- Text summarization
5 steps in NLP:
- Lexical analysis
- Syntactic analysis
- Semantic analysis
- Discourse analysis
- Pragmatic analysis
Pros/ advantages of NLP :
- NLP system offers exact answers to the questions, no unnecessary or unwanted some information.
- The accuracy of the answer increases with the amount of relevant information provided in the questions.
- Structuring a high unstructured data source. v Users can ask questions about any subject and get a direct response in seconds.
- It is easy to implement.
- Using a program is less costly than hiring a person. A person can take two or three times longer than a machine to execute the tasks mentioned.
- NLP system provides answers to the questions in natural language.
- Allow you to perform more language-based data compares to a human being without fatigue and in an unbiased and consistent way.
- NLP process help computer communicate with a human in their language and scales other language-related tasks.
- It is a faster customer service response time.
Cons/ disadvantages of NLP :
- NLP system doesn’t have a user interface that lacks features that allow users to further interact with the system.
- If it is necessary to develop a model with a new one without using a pre-trained model, it can take a week to achieve a good performance depending the amount of data.
- The system is built for a single and specific task only, it is unable to adapt to new domains and problems because of limited functions.
- In complex query language, the system may not be able to provide the correct answer it a question that is poorly worded or ambiguous.
- It is not 100% reliable, it is never 100% dependable. There is the possibility of error in its prediction and results