Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that facilitates the machines to read, comprehend and interpret the underlying meaning from human languages with its main lying in the interaction between Data Science and human language. NLP uses Machine Learning and Deep Learning techniques to extract meaning from human language.
How NLP Works?
The type of application being used determines the first step in NLP. For instance, the development of voice-based systems involves translating words into text using Hidden Markov Models (HMM) as the first step. HMM leverages math models for understanding human language and translating the same into text which is further processed by the NLP system.
Interpreting the context and understanding the language by dividing each part of the sentence into its part of speech forms the next step. This step involves the usage of algorithms that are trained on grammar rules. These algorithms help the NLP system understand the word context by leveraging statistical ML.
In certain scenarios that involve speech-to-text, the first step is avoided and the NLP system starts interpreting words in the first step itself using algorithms and grammar rules.
To understand the human language, NLP uses two main methods; Syntax and Semantic analysis.
The syntax is a process of arranging words as per grammar rules. This process enables NLP to extract meaning from a language as per grammar rules.
Syntax techniques used for NLP are as follows;
- Parsing comprises of analyzing sentences for grammar
- Sentence breaking places boundaries for large texts
- Word Segmentation divides a large piece of text into smaller fragments
- Morphological Segmentation involves grouping of words
- Stemming divides words to its root forms using inflection
The semantic analysis helps to extract meaning from the text. It enables the NLP system to comprehend the meaning and analyze the structure of sentences for logical interpretation of human language.
Following are the semantic techniques used by NLP system to understand sentences;
- Sense Disambiguation helps to derive the word meaning using its context
- Named Entity Recognition identifies the words that can be divided into groups based on category
- Natural Language Generation leverages a database to extract semantics hidden within words
Technical Approaches for Developing NLP Systems
Two main technical approaches are mainly used to develop an NLP system; Rules-based and machine learning-based methods.
Rules-based involves applying linguistic rules to text. Each rule comprises of an antecedent and a prediction. For instance, when performing sentiment analysis on product reviews, one lists out the positive and negative words. Each review will be analyzed for the number of negative and positive words that help to determine the sentiment of the overall text.
Machine Learning-based method involves the use of algorithms that can learn to understand human language based on previous experience. This method leverages text annotation services to train the ML algorithms to associate a particular input with its corresponding output. When we take the previous example of sentiment analysis, an algorithm is built to classify reviews as positive, neutral, or negative automatically. For accomplishing this task, the algorithms are trained using manually labeled text to enable them to make the predictions on their own for unseen data.
Use Cases of NLP
Email Assistant
We are using NLP every day in the form of auto-correct, auto-complete, grammar, and spell-check. Email filters that keep the spam emails away from your inbox also leverage NLP technology.
Sentiment Analysis
A common application of NLP, sentiment analysis helps to determine the negative or positive polarity of a text. Sentiment analysis enables businesses to gain customer views on products or services. It can be used to categorize company or product reviews and poll customers’ opinions from their comments and social media posts.
NLP alone cannot handle this task and requires ML/DL algorithms to perform data analytics and back-end computation to understand large volumes of data.
Chatbots
NLP is used to train chatbots for specific behavior and enhance their performance before deployment. Chatbots leverage NLP algorithms for answering queries raised by customers. The NLP algorithms enable the chatbot to understand the meaning behind a customer query and answer to it in real-time without human intervention.
About RightClick.AI
RightClick.AI specializes in providing world-class data labeling services and is one of the top data annotation companies in India. Looking to extract insights from text/voice or want a customized chatbot for your business, RightClick.AI offers a range of NLP services to support a wide range of businesses. Reach out to us at info@rightclick.com for more info.