An Overview of NLU and NLP in Data Analysis
Natural Language Understanding and Natural Language Processing are revolutionizing technologies for redesigning how businesses obtain information from unstructured data. NLU and NLP strengthen data analysts' ability to derive sentiments, context, and meaning from textual data. Its implication ranges across various domains:
Financial Analysis
Customer Insights
Healthcare
Human Resource
Cybersecurity
With the accelerating complexities of datasets, the relevance of data analysis services and techniques is increasing. Deep dive and read this blog to explore how NLP and NLU are changing the future with their analytics capabilities.
Natural Language Processing (NLP)
From a computer's viewpoint, every natural language is free-form text. Simply put, a specific set of keywords doesn't exist at specific positions while providing inputs. Along with its unstructured nature, there are numerous ways of leveraging inputs using natural language. Take the below illustrations into consideration-
What will be today’s weather?
Is it going to rain today?
Should I carry an umbrella with me?
The intent behind all these sentences is similar and focused on querying today’s weather. As humans, it is easy for us to identify the underlying questions behind them. But it is a difficult task for machines. The algorithm requires proper format and structure of input to deliver results. If we try to determine code rules for each combination of words in any natural language, though, the complications will not end. Here, the role of NLP begins.
Natural Language Processing, a machine learning approach, empowers computers to comprehend, interpret, and manipulate human language. Its core functions involve Analyzing large volumes of data sets, Generating Insights and Information, and more. Natural Language Processing services will be game-changing in the analytics landscape, especially in 2024. NLP’s role begins with pre-processing data.
A few examples of pre-processing steps are:
Lemmatization
stemming
Named entity Recognition
Spell Checking
Sentence Segmentation
Dependency Parsing
Chunking
Text Normalization
Encoding, etc.
Implementation of NLP in Data Analytics
Use cases of NLP are numerous and ready to excel efficiency of tasks. Below are some of the applications mentioned:
Processing of Text
Natural Language Processing (NLP) is utilized to preprocess data in a text format prior to analysis. It involves functions like tokenization, which means breaking sentences into phrases or words; lemmatization, which means forming groups of inflected forms of words; and stemming, which means deriving the root form of the words. Many more functions can be leveraged using NLP in data analysis.
Extraction of Information
NLP abstracts structured information out of unstructured data. Structured data stands as a strong pillar for data analysis.
Analysis of Sentiment
It is utilized to analyze opinions or sentiments articulated in text data. It allows organizations to derive meaning and sentiment from customer reviews and input towards their services, products, business practices, etc.
Topic Modeling
Non-negative Matrix Factorization (NMF) and Latest Dirichlet Allocation (LDA) are applied for topic modelling.
Text Summarization
Natural Language Processing algorithms allow the generation of precise summaries for lengthy texts. This is crucial when abstracting essential information from large text data sets, allowing decision-makers to concentrate on relevant insights.
Along with the above, there are many other primary areas of execution for NLP which can range beyond your imagination. Allowing NLP to play its magical role can help businesses enhance the effectiveness of numerous functions.
Natural Language Understanding
Natural Language Understanding (NLU) is a subset of NLP. Its core work areas include interpreting natural language, evaluating context, generating meaningful insights, and drawing meaning. NLU utilizes computer software to interpret sentiments articulated in the input- text or speech, which can be any human language.
NLU allows the computer to decode sentiments articulated in a natural language the user utilizes. Its scope is not only limited to understanding syntax; it also allows the computer to respond in the same way.
Parsing, a primary aspect of NLU, includes converting written text into an organized format that computers can understand. NLU also allows machines to communicate back to users in their own language.
The primary focus of NLU execution is to generate voice and chat-enabled bots that can engage with humans without monitoring. Many IT companies and Startups work with or have their own NLU projects.
WORKING OF NLU
Using algorithms, NLU examines data to understand its context and breaks down human speech or text into a structured ontology. Natural Language Understanding works on two essential concepts: (1) Intent Recognition and (2) Entity Recognition
- The intent Recognition process allows sentiment recognition of users through given input in the form of text or speech. It is the beginning step of NLU workings, as it derives the meaning of the input.
- Entity Recognition discerns the entity within a communication and then derives vital information about the correspondent. Entities are further divided into two: numeric and Named Entities. Dates, percentages, currencies, and quantities are recognized as Numeric Entities, while Business Names, people’s names, locations, etc. are categorized as Named Entities.
NLU breaks sentences into individual and specific words known as tokens. Then, these tokens go over the dictionary to recognize the word and part of speech of that particular input. Then, its grammatical structure is analyzed.
For example, an Island Camping trip on Victoria Island in August. 9- can be breakdown as below:
Reservation of camping lot (Intent)
Victoria Island (Location)
Tickets (Intent)
August. 9 (Date)
APPLICATIONS OF NATURAL LANGUAGE UNDERSTANDING (NLU)
Below are the applications of NLU in data analysis areas:
Customer support via Intelligent Personal Assistant—NLU is a technology deployed for Chatbots, a computer program for communicating with users using natural language. These chatbots can answer user queries 24/7, improving the frontend staff's functions.
Machine Translation- ML is a part of AI that allows machines to understand and modify their behavior based on trained data. These ML algorithms are also utilized to answer in natural language.
Data Capturing, a process of recording and gathering information about a person, event, or object, allows machines to ask customers to input about billing, shipping, etc., information, which acts as a foundation for entity recognition.
NLU can be implemented in Conversational Interfaces, Interactive Voice Response and Message Routing, User Sentiment and Intent, and many other areas.
Learn more about the tasks performed by NLP and NLU. In addition to the above-mentioned, NLU and NLP have many other areas of implication. Their role ranges across many activities.
DIFFERENCE BETWEEN NATURAL LANGUAGE PROCESSING AND NATURAL LANGUAGE UNDERSTANDING
Natural Language Processing | Natural Language Understanding |
NLP concentrates on computer-human interactions. It leverages text generation, understanding, sentiment analysis, machine translation, and more. | NLU, a subset of NLP, deals explicitly with understanding human language. It focuses on understanding the text's intent, involving context, nuances, and intent. |
It is a comprehensive concept. | It is a narrow concept. |
NLP abstracts meaningful insights from unstructured data like customer reviews, news articles, emails, and social media. | NLU abstracts a deeper context to text data, leading to improved decision-making. |
Broad areas need to be considered when making decisions. At there, NLP plays its role. | NLU is enough when there is only text to be understood. |
NLP involves part-of-speech tagging, named entity recognition, tokenization, topic modeling, and syntactic parsing. | NLU involves semantic understanding-focused techniques such as semantic role labeling, word embedding, and deep learning models like transformers. |
NLP is a parent set. | NLU is a subgroup of NLP. |
It is a computer reading language. | It is a computer-understanding language. |
NLP functions at multiple levels, such as semantic, pragmatic, and syntactic. It includes text processing to abstract information, generate responses, and perform sentiment analysis. | NLU includes semantic analysis to understand the underlying meaning and context of text. |
NLP applications involve virtual assistance, chatbots, text summarization, machine translation, and sentiment analysis. | NLU includes voice assistance, content understanding, recommendation systems, and customer service automation. |
Wrapping Up
NLP and NLU, in data analysis, play the role of game changers. They have modified the landscape of data analysis. Empower your organization with natural language processing services and excel in the effectiveness of your functions. NLP and NLU are transformative factors for every business. They can boost the power of your organization’s engine. When NLP and NLU are new currencies, let them transform your business with their magic stick!