NLP Algorithms Improve Computer Accessibility
of the Human Language

NLP, or natural language processing, is a complicated kind of algorithm meant to improve communication between computers and people. NLP makes it possible for people to use technology in ways they were not previously able to, improving their quality of life. 

Some of the tools NLP algorithms are used for include language translation, chatbots, virtual assistants like Siri and Alexa, speech recognition, and more. 

Rossum doesn’t use NLP in our approach. Our platform is built on a kind of machine learning algorithm called Deep Learning. Deep Learning and Natural Language Processing are sometimes confused with each other, however, they function quite differently and are used to achieve different results. 

Deep learning is an algorithm that can be used to teach computers how to perform specific tasks like reading documents and capturing data. It is created with an artificial neural network that mimics a human brain. Deep Learning teaches itself how to function and separate data by using large datasets to train itself. 

Alternatively, NLP algorithms were created to make human language more accessible to computers. It is an intersection of artificial intelligence, linguistics, and computer science. 

NLP code can be written for different tasks and therefore make use of different techniques to complete certain tasks; however, some projects are closely related. 

For example, NLP algorithms for chatbots and NLP algorithms for sentiment analysis may use the same processes of tokenizing, normalizing, Named Entity Recognition, and parsing. That’s because sentiment analysis is an important feature of a next-generation chatbot. 

As NLP algorithms improve as they are used. Other uses of natural language processing include summarization of text, text classification, spellcheck, text prediction and autocomplete, and more.

What is Natural Language Processing?

Natural Language Processing is an important tool that has improved many areas of human life. Many NLP applications, like Siri and Alexa, are used as important accessibility aids thanks to the connectivity of their systems and easy communication. 

For example, someone in a low-mobility home can use Alexa devices to lock and unlock doors, turn off lights, control the temperature, and interact with security cameras. 

Another NLP example that is often used as an accessibility tool is speech recognition. Speech-to-text NLP applications allow people who have difficulty typing to use their voice. The NLP then converts human speech into textual data. 

NLP also plays a large role in language translation, which makes it possible for two people from different languages to communicate with each other. 

Many people interact with different NLP tools every day without even realizing it. The predictive text function in many smartphones is an NLP algorithm. Many of the biggest email providers in the world use natural language processing to filter emails and protect users against spam messages. 

Many people also use spell check every day, which is built into many word processing software like Microsoft Word and Google Docs. NLP can also be used at work in customer service, marketing, and other kinds of science.

What is a text analysis tool?

One of the most popular applications of natural language processing is as a text analysis tool. One of the most famous text analysis examples in NLP is IBM’s Watson. Of all the different applications of NLP algorithms, test analysis can be used in a variety of ways. 

For example, IBM’s Watson is a text analysis tool, but it is separated out into different modules as text analysis can be used for different things. The Natural Language Understanding module extracts concepts, Named Entities, keywords, and categories. It can also sort by sentiment into complicated emotions like confusion, anger, disappointment, and excitement.

When it comes to text analysis, machine learning is used to separate, sort, and derive meaning from textual data. NLP text analysis can be used to analyze entire documents, single sentences, or even parts of a sentence. 

There are many industries that use NLP text analysis, including healthcare, research, marketing, customer service, and more. Businesses seeking to use NLP text analysis can use internal data or external data to glean insights. 

Some examples of documents often used in text analysis include:

• Research papers
• Customer service conversations
• Email messaging
• Customer Relationship Management records
• Web Scraping Tools
• APIs for social media sources like Facebook, Twitter, and Instagram

What are the best text mining techniques?

Text mining is an automated process that uses natural language processing algorithms to find insights and meaning from unstructured data. 

By turning the data into information that the computer understands, text mining automates text classification by sorting them by sentiment, topic, and intent. The algorithm will then translate the data again to be understood by people. 

Here are a few basic text-mining techniques:

• Word frequency
  • Text mining applications search for word frequency to identify the most used terms or concepts in data. It can reveal common trends within the dataset.
• Collocation 
  • This technique identifies words that are often found near each other. Collocations are commonly bigrams or trigrams, which are phrases like “machine learning,” “social media,” or “out of business.” Collocation is used to identify collocations and view them as one word.
• Concordance
  • Concordance is a technique use to recognize context. The English language has several words that can be used in different contexts and change the meaning. Concordance can help understand the meaning of the word based on tis context.

A more advanced technique of text mining is text classification. Text classification works by assigning tags to unstructured text data. This makes it easy for the algorithm to organize and structure the data, creating meaningful insights.

• Text classification has several use cases, including:
  • Topic analysis
  • Sentiment Analysis
  • Language Detection
  • Intent Detection

How can a NLP algorithms list help you?

NLP algorithms are designed based on the type of problem it is meant to solve. Therefore, the top NLP algorithms will likely be further segmented into the type of task it has been trained to do. However, algorithms may comprise many of the same techniques or methods, regardless of their specific task. 

For example, nearly every NLP algorithm is tasked with deriving meaning from textual data. In order to do that, it has to understand the data. In order to understand the data, the machine has to break it down into its simplest form or into a format it can understand. 

Here is an NLP algorithms list that nearly every NLP tool utilizes:

• Stemming and lemmatization
  • This is when algorithms take words with roots like “writer,” “writing,” and “written” down to the base word of “write.” 
  • Stemming is a rules-based approach that does not know or learn the context of the word when chopping. It is faster than lemmatization but less accurate. 
  • Lemmatization is a dictionary-based approach that learns the context of the word before it chops. It is slower but more accurate.
• Latent Dirichlet Allocation
  • This method is used for Topic Modeling. This NLP is used when trying to discover themes or common topics within a dataset. 
  • Latent Dirichlet Allocation requires a list of subjects to which the documents in the dataset can be applied. After the algorithm has run several times, the topics and themes can be reassessed for increased accuracy.

What are NLP chatbots?

NLP chatbots are used by companies for several purposes. Many companies use them as customer service assistants who can route customers and take their initial comments before passing them off to a human customer service agent. 

There are plenty of NLP chatbot examples that people interact with every day: 

• Replika, an AI friend

• BlenderBot by Facebook

• Meena by Google
• Tidio Support Bot

An NLP chatbot is made of three indelible parts:

• A Dialogue System
  • Dialogue systems can vary, but in order for it to be a true dialogue system, it must be able to accept input and produce output. Dialogue systems can be differentiated based on modality, device, style, or initiative. 

• Natural Language Understanding (NLU)
  • This is the part of NLP that processes and understands human input. It’s vital that the NLU aspect of the chatbot is as strong as possible to ensure that the person conversing with the chatbot will have a successful solution. 
  • If the chatbot doesn’t understand the input and routes it to the most appropriate channel, the person interacting with it will be unsatisfied. 

• Natural Language Generation (NLG)
  • NLG is another aspect that makes up NLP as a whole. If the NLU of the chatbot is successful and understands the user’s input, the algorithm will determine an appropriate response and translate it into the user’s language. 
  • The NLG of a chatbot is determined through conversation design that is outlined through workflows, templates, or intent-driven models.

Chatbots without NLP are simple rules-based bots that can spit out predetermined responses based on the input. While they’re good for simples issues, they aren’t capable of handling more complex problems.

What are advanced NLP algorithms?

Some of the techniques we’ve discussed in this article are advanced NLP algorithms. They are emerging and complicated methods that require a lot of development to work properly. A list of these new NLP algorithms include:

• Named Entity Recognition
  • Named Entity Recognition, or NER, is capable of taking unstructured data and sifting through it for named entities of people, places, monetary values, brands, medicines, plants, animals, locations, and the like. Anything that has a name. 
  • A NER algorithm should be able to recognize that Rowan McDonald is a person and separate from the brand McDonald’s. 

• Semantic Search
  • This is an algorithm that understands the intent behind a search and finds data that matches that intent. Intent is determined by a user’s search history, past purchases, past online behavior, location, and other details such as cookies. 

• Sentiment analysis
  • Sentiment analysis, sometimes called “emotion mining” is used by businesses for consumer and employee insights. This NLP algorithm analyzes data to associate sentiment with entities, topics, or aspects. 
  • Most sentiment analysis algorithms assign the topics an aggregate positive, neutral, or negative score, but some of the more advanced sentiment analysis algorithms can determine more complicated emotions. 

These NLP algorithms are capable of complicated replication in a way that was not accessible before. Businesses and consumers alike can use NLP to increase their quality of life through tools like speech recognition, text summarization, and more. 

Currently, most of the more complicated NLP algorithms, like sentiment analysis, are only available to businesses.