Introduction: What is NLP?
What is NLP? Natural Language Processing, also known as / abbreviated as NLP, helps machines process and understand human language in a contextual manner so that they can pre-programmatically carry out repetitive tasks such as translation, summarization, classification… etc.
Human language is complex, contextual, ambiguous, disorganized, and diverse. There are thousands of languages in the world and have their own syntactical and semantic rules. To add further complexity they have their dialects and slang. The first step in helping machines to understand natural language is to convert language into data that machines can interpret and understand. This conversion stage is called pre-processing and is used to clean up the data.
Let us dive deep into what is data pre-processing.
When trying to understand any natural language, syntactical and semantic analysis is key to understanding the grammatical structure of the language and identifying how words relate to each other in a given context. Converting this text into data that machines can understand with contextual information is a very strategic and complex process.
There are several methods to clean data and make it more structured, organized, and tagged so machines can process this data. Some ways of doing this include:
Tokens are building blocks of NLP, Tokenization is a way of separating a piece of text into smaller units called tokens. Here, tokens can be either words, characters, or spaces. Hence, tokenization can be broadly classified into 3 types – word, character, and subword (n-gram characters) tokenization.
Identifying parts of speech, marking up words as nouns, verbs, adjectives, adverbs, pronouns, etc.
Stemming and lemmatization:
Stemming and Lemmatization is Text Normalization (or sometimes called Word Normalization) techniques in the field of Natural Language Processing that are used to prepare text, words, and documents for further processing. Stemming and Lemmatization have been studied, and algorithms have been developed in Computer Science since the 1960s.
Stop word removal:
Filtering out common words that add little or no unique information, words like this, that, the, and at.
Source: Youtube | NLP
Natural Language Processing Algorithms
The next step once your data is ready is building an NLP algorithm, and training it so it can interpret natural language and perform specific tasks that you need.
There are two main approaches/algorithms you can use to solve NLP problems:
A rule-based approach:
Rule-based systems rely on hand-crafted grammatical rules that need to be created by experts in linguistics. The rules-based systems are driven systems and follow a set pattern that has been identified for solving a particular problem.
Machine learning algorithms:
Machine learning models, on the other hand, are based on statistical methods and learn to perform tasks after being trained on specific data based on the required outcome. This is a common Machine learning method and used widely in the NLP field.
Some examples of natural language processing.
Natural Language Processing enables us to perform a diverse array of tasks, from translation to classification, and summarization of long pieces of content. Let us dive a little deeper into each category.
Text classification or text tagging is assigning categories to open-ended text based on context relevancy. This can help us in many ways:
The process of analyzing emotions within a text and classifying them into buckets like positive, negative, or neutral. We can run sentiment analysis on product reviews, social media posts, and customer feedback. running sentimental analysis can be very insightful for businesses to understand how customers are perceiving the brand, product, and service offering.
With the help of natural language processing, a sentiment classifier can understand the complexity of each opinion, comment, and automatically tag them into classified buckets that have been preset. While this data can be manually reviewed and classified, NLP and sentiment analysis gives the organization scale and speed which are key elements to any organizational success. This also gives the organization the power of real-time monitoring and helps it be pro-active than reactive.
Identifying the main themes, topics, or categories within a text and assigning predefined tags. familiarity with the data you are analyzing is key during this process. This will help you slot content in predefined buckets.
This classification task consists of identifying the purpose, goal, or intention behind a text. This helps organizations in identifying sales and potential language tweaks to respond to issues from your inbox.
Another example of NLP is text extraction, which helps with identifying specific pieces of data that may be present in the text. Two key areas of text extraction are:
This helps you identify key pieces within the text and highlights them for you to read with the keywords in mind.
Named Entity Recognition (NER):
This technique allows you to extract the names of people, companies, places, etc.
Machine translation is used to translate one language in text or speech to another language. There are a ton of good online translation services including Google. Custom models can be built using this method to improve the accuracy of the translation.
Topic modeling is similar to topic classification. This finds relevant topics in a text by grouping texts with similar words and expressions based on context. this is generally used in exploratory data.
Natural Language Generation (NLG)
Natural language generation, NLG for short, is used for analyzing unstructured data and using it as an input to automatically create content.
It can be used to generate automated answers, write emails, and long text!
Natural Language Processing Applications
Natural language processing allows businesses to make sense of all sorts of unstructured data ― like emails, social media posts, product reviews, online surveys, and customer support tickets ― and gain valuable insights to enhance their decision-making processes. Companies are also using NLP to automate routine tasks, reducing times and costs, and ultimately becoming more efficient.
Here are some examples of how businesses are putting NLP into practice:
Automatically Analyzing Customer Feedback
Analyzing customer feedback is essential to know what clients think about your product. However, this data may be difficult to process. NLP can help you leverage qualitative data from online surveys, product reviews, or social media posts, and get insights to improve your business.
Automating Tasks in Customer Support
Businesses are using NLP models to automate tedious and time-consuming tasks in areas like customer service. This results in more efficient processes, and agents with more time to focus on what matters most: delivering outstanding support experiences.
Customer service automation powered by NLP includes a series of processes, from routing tickets to the most appropriate agent, to using chatbots to solve frequent queries. Here are some examples:
Text classification models allow companies to tag incoming support tickets based on different criteria, like topic, sentiment, or language, and route tickets to the most suitable pool of agents. An e-commerce company, for example, might use a topic classifier to identify if a support ticket refers to a shipping problem, missing item, or return item, among other categories:
Customer support teams are increasingly using chatbots to handle routine queries. This reduces costs, enables support agents to focus on more fulfilling tasks that require more personalization, and cuts customer waiting times.
Top NLP Tools to Help You Get Started
Natural language processing is one of the most complex fields within artificial intelligence. But, trying your hand at NLP tasks like sentiment analysis or keyword extraction needn’t be so difficult. Many online NLP tools make language processing accessible to everyone, allowing you to analyze large volumes of data in a very simple and intuitive way.
SaaS platforms are great alternatives to open-source libraries, since they provide ready-to-use solutions that are often easy to use, and don’t require programming or machine learning knowledge. These tools are also great for anyone who doesn’t want to invest time coding, or in extra resources.
If you want to integrate tools with your existing tools, most of these tools offer NLP APIs in Python (requiring you to enter a few lines of code) and integrations with apps you use every day.
Also Read: Artificial intelligence in Journalism.
8 of the Best SaaS NLP Tools:
Google Cloud NLP
The NLP tool you choose will depend on which one you feel most comfortable using, and the tasks you want to carry out.
Natural language processing is one of the most promising fields within Artificial Intelligence, and it’s already present in many applications we use daily, from chatbots to search engines.