Natural Language Processing (NLP) is defined as the process by which computers understand and process natural human language, like speech and text. NLP is the sub-branch of data science that generates insights from text. Knowledge of NLP is essential for data analysts and data scientists. A data analyst processes and performs statistical analysis of existing datasets. They assess large data sets to identify trends, develop charts, and create visual presentations to help businesses make more strategic decisions. Data analysis is a complex field; enrolling in an online data analytics course or a data science programs will help you gain the knowledge and skills you need to succeed in this field.
Every two years, data gets doubled, and this time frame is getting shorter. What is more important to note is that most of this data is unstructured. All the Facebook posts, tweets, blogs, and articles are in textual form. Only about 20% of this data is structured. Natural Language Processing systems can process and analyze this massive amount of text-based data in an unbiased and consistent manner. They can understand complex concepts, decode ambiguities of language to draw key facts, or provide summaries. Sentiment analysis, speech recognition, and automated summary can be achieved using NLP. Products like Siri and Alexa employ NLP to understand and respond to user requests.
Become an NLP Expert:
There are a thousand courses available online to learn Natural Processing Language. While these tutorials are important and very helpful, the problem arises when you are a beginner, when you don’t know what it is that you want or should you start from.
Following are the steps you need to learn Natural Language Processing:
Programming is an essential skill when it comes to any technical field. Some of the most popular programming languages you must learn before entering this field are:
Python is the most commonly used language because of its versatility. It is also beginner-friendly and has easy and consistent syntax. It also comes with lots of packages that make code reusability possible. It is an excellent choice for Natural Language Processing because of its transparent semantics and syntax.
This language allows you to figure out how to organize text utilizing full-text search, information extraction, clustering, and tagging. It is a platform-independent language, making the processing of information easy. It comes with LingPipe, OpenNLP, and Stanford CoreNLP, making it a commonly used language in this field.
2. Mathematics, Statistics, and Probability:
Like in data science, mathematics, statistics, and probability play an essential role in Natural Language Processing. Mathematics is a very vast field, and to understand NLP algorithms, you need to have a deep understanding of the following four aspects:
- Linear Algebra
- Basics of Statistics
- Probability Theory
3. Machine Learning Basics:
Before you dig deeper into Natural Language Processing, make sure you have a strong understanding of machine learning basics. Many NLP algorithms depend on different machine learning concepts.
Machine learning for NLP involves a set of techniques for identifying parts of speech and sentiments of text. Therefore, you need to be familiar with different types of ML algorithms like Supervised ML and Unsupervised ML.
Supervised ML techniques can be exhibited as a model that is then applied to other texts. Unsupervised ML is a set of algorithms that work across vast amounts of data to generate meaning.
4. Text Preprocessing:
Text preprocessing is the process of cleaning and preparing text by performing a set of procedures on them. It covers concepts like lexicons, lemmatization, stemming, stopword removal, and tokenization.
Stemming is used for dealing with standardizing vocabulary and sparsity issues. Lemmatization is very similar to stemming. The only difference is that instead of just chopping things off, it transforms words into actual roots.
5. NLP Core Techniques:
The text that was cleaned and prepared by text preprocessing is further analyzed using natural language processing techniques. Techniques like applying language models, parsing text to extract representation, executing machine learning translations, and building sequence models aim to perform specific tasks and extract information from text.
FAQs on NLP:
Q1: What is NLP used for?
Ans: Natural Language Processing allows computers to read text, hear speech, interpret it, measure sentiment, and determine which part is important. It makes it possible for computers to communicate with humans.
Q2: What are some examples of natural language processing?
Ans: Some examples of NLP are Search Results, Predictive Text, Language Translation, Data Analysis, Digital Phone Calls, and Text Analytics.
Q3: What are natural processing techniques?
Ans: 5 common natural processing techniques used for extracting information from the text are- Named Entity Recognition, Text Summarization, Sentiment Analysis, Topic Modeling, and Aspect Mining.
Q4: Is NLP a part of deep learning?
Ans: Deep learning and NLP are a part of a larger field of study, Artificial Intelligence. NLP allows machines to understand human language, and deep learning enriches the applications of NLP.