Natural Language Processing Bots

Published in

Voice Tech Podcast

10 min readMar 25, 2020

While natural language processing isn’t a new science, the technology is rapidly advancing thanks to an increased interest in human-to-machine communications, plus an availability of big data, powerful computing and enhanced algorithms.

About

As a human, you may speak and write in English, Spanish or Chinese. But a computer’s native language known as machine code or machine language is mostly incomprehensible to most people. At your device’s lowest levels, communication occurs not with words but through millions of zeros and ones that produce logical actions. In this article will discuss NLP and how it works in the real world.

Introduction

In the year 1950, Alan Turing and English computer scientist published his renowned paper titled “Computing Machinery and Intelligence”. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. So the machine can interact with human and trick them into thinking the machine was living entity itself, then it was artificially intelligent.

This known as the Turing Test and passing it has been one of the most sought after goals in computer science. Passing the Turing Test would signal the birth of artificial intelligence.

The fundamental of the Turing test is communication. The computer has to be able to communicate with the human in their natural language. This is called natural language processing.

We are in the phase where we are still trying the achieve artificial intelligence, A natural language processing the way it interacts with a human has become a prevalent trend and now it has part of many of the products which we use in our day to day life. It will be as quite interesting how this will going to change the Tech World though it already starts changing. Let’s figure out what makes this so exciting.

NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.

What is Natural Language Processing

As services offering elasticity and scalability these days. Natural language processing (NLP) can play a role to analyze, understand and generate Automated Speech Response (ASR). The NLP makes interactions with computers, and the rational way two humans interact with each other so that there will be no special skills are required to interact with NLP enabled product.

Like as we mentioned about the interactions between two humans, how humans can interact with each other in their natural language. That language is their native or learned by them, which can understand by another person such as English, Spanish and Chinese all are the natural languages.

On the contrary, computers can only understand un-natural of formal languages, and we called them computer programming languages such as C, C++, and JAVA, etc.). These languages were made for expressing a set of detailed instructions for a digital computer.

As the computers in formal languages, they don’t understand the language we use to communicate with each other; this is where NLP comes into the picture. Using NLP framework computer can listen and understand the natural language being spoken or sent via any medium to it. Also, if its a question or any task needs to be done by it, the computer can perform accordingly.

Yes, it’s not like that we speak something and the computer understands that in the same way we do. There are quite a few complicated steps involved in this process. NLP is not new in the AI field, but it became much popular after advance automation skills such as ChatOps (with scripted chatbots) is involved in infrastructure automation.

How is natural language processing used today?

There are multiple tasks can be accomplished using NLP; it all depends on the requirement and makes it understandable it using NLP so that communication can be done. Few are the everyday use cases of NLP nowadays:

Spam Filtering: Spam can be not only annoying but also dangerous to consumers. So any of the email service providers must protect their inboxes from spam. Recently Google reveals that they are using NLP to determine the spam’s email for the user’s mailbox. These spam filers scan text from the mail body and try to understand the meaning of it to determine whether mail is spam or not.
Sentiment Analysis & Algorithmic Trading: With the advance of NLP and understanding capabilities, if you want to predict your stock value ups and downs, NLP helps in that by analyzing the market trends and the news, So that intelligent automated system can be built which will analyze all stock related data and determine whether to keep the stock buy more and sell.
Virtual Assistants: When you ask Siri or the Google Assistant to fetch weather information in your city or the match score, they use NLP to understand the meaning of what we are asking them, and generate natural language response or fetch the result from internet.
Summering Information: There’s tons of information is available on the internet about any of the subjects in the form of articles and blogs, NLP is being used to understand all the information about any subject analyze it, and then generate the summary about it to understand that subject quickest.

These are the few use cases where NLP is being used in the real world. If you look there is the pattern on all the use case of NLP, in most of the instances NLP is used to understand the natural language, it is also used to generate natural language in some cases. So these are the major components of NLP. Natural Language Understanding (NLU) and Natural Language Generation (NLG).

How does natural language processing work?

To understand it batter how exactly NLP works, we need to understand components of NLP, which is NLU and NLG, and both components are valuable when it comes to proper implementation of NLP.

Natural Language Understanding

The NLP is all about the understanding and providing the meaning of the natural language that is received by the computer.

The first step in the NLP is to take natural language and normalize it for further processing while processing it finds part of speech adjective, etc., and this is speech recognition. Once the text has been processed, NLU kicks in and the try to understand the meaning of it.

There are multiple models available for speech recognition such as Lexical Analysis, Syntactic Analysis, Semantic Analysis etc. These models are turning your speech into text by either making mathematical calculations or tagging to determine that what you said. Hidden Markov Models (HMMs) which based on Syntactic Analysis, is the most common which is being used in NLP.

The HMMs listen what you said then break that into small units, once it breaks complete recording into small units, it compares these units with the pre-recorded speech to understand the speech. The best match which it finds with the recordings, it will print that as text.

After processing it, the main challenge comes into understanding the actual meaning.

As we mentioned, the NLP framework uses different models to understand the meaning, still the process quite similar. The computer first normalizes the text and then do post-processing, in that it tries to understand the grammar like nouns, verbs and adj in the text. This part of NLP is called Part of Speech Tagging.

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NLP framework also has a lexicon of a language means the collection of words and phrases in a speech. Most of NLP framework us Syntactic Analysis (Parsing), to apply these words on the text to parse and determine the probable meaning of the text.

At the end of processing, the computer should have the probable meaning of the text sent by you. Still, there are multiple challenges in the NLP when it comes to real-world implementation. Such as the words are having lots of meaning of the words are having the same meanings, though that can be handled using the hardcoded rule in the framework.

Natural Language Generation

NLG is not that difficult as NLU. NLG simply translate processed text back to the natural language, and then it can be sent back to the user as it is of via voice which is text-to-speech.

Once the NLP framework determined the information which is translated to text. If you have asked a question about the match score, most probably it will go online and fetch the latest result for you then. Process it and send back to you via text or voice.

It organizes the text structure according to the text given as input and uses lexicon vocabulary to complete a meaningful sentence for the answer.

Finally, the text is ready for the user, which will be delivered via text or the voice. For TTS (text-to-speech) engine uses a prosody model, which will modify text for an audible format like, the pause in audio, audio speed, duration. Then it delivers that recording to the user as complete voice speech.

Some NLP/NLU frameworks that will make your bot language-intelligent

Over the past year’s remarkable progress has been noticed in NLP/NLU technologies. Primarily the term ChatOps which become automation trend in the market where scripted bots are doing tasks automation. Here NLP kicks in to move all the automation on based on natural language. Resulting several platforms is providing NLP/NLU services.

IBM’s Watson Conversation Service
Microsoft LUIS
Google Natural Language API
Wit.ai
Api.ai (Dialogue Flow)
Alexa Skills Kit
Recast.AI
Pat

IBM’s Watson Conversation Service

The Watson AI platform provides multiple services, including language processing. IBM’s Watson Conversation Service (WCS) provides AI based NLP/NLU engine. Using that anyone can quickly build and deploy chatbots and virtual agents across a variety of channels, including mobile devices, messaging platforms, and even robots.

Microsoft’s Language Understanding Intelligence Service

Microsoft’s Language Understanding Intelligence Service (LUIS) is a part of Microsoft Cognitive Services (MCS). Designed to identify valuable information in conversations, LUIS interprets user goals (intents) and distils relevant information from sentences (entities), for a high quality, nuanced language model. LUIS integrates seamlessly with the Azure Bot Service, making it easy to create a sophisticated bot.

Google Natural Language API

Google Cloud Natural Language reveals the structure and meaning of the text by offering powerful machine learning models in an easy to use REST API. You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call centre or a messaging app. You can analyze text uploaded in your request or integrate with your document storage on Google Cloud Storage.

Wit.ai

Wit.ai makes it easy for developers to build applications and devices that you can talk or text to. Our vision is to empower developers with an open and extensible natural language platform. Wit.ai learns human language from every interaction and leverages the community: what’s learned is shared across developers.

Api.ai (Dialogue Flow)

Dialogue gives users new ways to interact with your product by building an engaging voice and text-based conversational interfaces powered by AI. Connect with users on the Google Assistant, Amazon Alexa, Facebook Messenger, and other popular platforms and devices.

Alexa Skills Kit

Amazon Alexa can be considered one of the most straightforward language processing technologies when compared with the other platforms listed in this article. However, the volume of users leveraging Alexa Services on a daily basis also makes it one of the most popular NLP engines in the market. Functionally, the Alexa Skills Kit enables the definition of intents and entities relevant in conversational interactions. One of the most significant advantages of Alexa is its integration with other Amazon Web Services offerings like AWS Lambda.

Recast.AI

Recast.AI is a platform for implementing bot solutions with sophisticated NLP/NLU capabilities. The platform provides developer-friendly interfaces to determine intent and entities in natural language sentences. Additionally, Recast.AI includes a robust toolkit for training and improving NLP models based on user interactions.

Pat

Pat is a newcomer to the NLP/NLU platform market focused on humanizing human-machine interactions. Functionally, Pat deviates from traditional statistical NLP models and focuses on leveraging neural network algorithms to correctly assign meaning to words in a sentence. As a result, the Pat platform can accurately analyze extremely complex natural language interactions

Some NLP bot use cases for everyone!

Recruitment:- Using NLP bot we can have initial evaluation also, the round of interview of candidates, also it will help in fresher’s interview drive
Support:- It can help in support tasks such as engaging support engineer, raising a ticket with helpdesk and getting status of tickets
Marketing:- It can help company official website visitors to answer them their FAQs
Information Sharing:- It can integrate with the portal to fetch users/team details and the contact numbers.
IT Automation:- One of the good examples of IT automation is to have Print Bot which will print the files uploaded by the user
HR Process:- It can integrate with PeopleSoft for timesheet update approvals, fetching Holiday list and Polices

Conclusion

We’re already seeing new ways and developing even better systems. Companies like Google are experimenting with Deep Neural Networks (DNNs) to push the limits of NLP and make it possible for humans-to-machine interactions to feel just like human-to-human interactions.

While we’re still a ways away from DNN-based text-to-speech engines from hitting the market, the potential for this technology is exciting! And of course, organizations are always looking for ways to improve the bottom line by Introducing new technologies. We can use the above-mentioned NLP stacks to make NLP bots.