Language-based AI helps you improve the user experience through features like Autocomplete, Spellcheck, and Autocorrect.

The field of language-based AI — also referred to as natural language processing, or NLP— has undergone breathtaking, unprecedented advances over the past few years. Two related technology breakthroughs have driven this remarkable recent progress: self-supervised learning and a powerful new deep learning architecture known as the transformer. Natural Language Processing (NLP) is a form of Artificial Intelligence (AI) that deals with the processing and manipulation of language. Among many features of Artificial Intelligence Services and Solutions, it is a technology that facilitates the interaction between humans and computers, hence making it easier for them to communicate with one another.

For every startup, seeing fruitful growth and making their customers happy are crucial goals. Language-based AI helps you improve the user experience through features like Autocomplete, Spellcheck, and Autocorrect to help your customers find the appropriate information, which in turn enriches the user experience. It also adds to their staying time on your website, hence proving beneficial to you.

This is why top startups opt for NLP features to be inculcated in their website as these are an important part and can lead to high conversion rates.

Let us see some widely used business applications of Language-based AI by startups and giant firms:

1. Chatbots: They are the most ubiquitous use case of NLP as they are better at handling customer support requests and inquiries. They serve as the first line of support, sorting, and routing requests to the appropriate teams or departments. Also, chatbots provide virtual assistance for simple customer problems and offload low-priority, high-turnover tasks that do not require any skill. E.g., Zomato chatbox.

2. Email Filters: This is another widely used application of NLP. By analyzing the text in the emails that flow through the servers, email providers stop spam-based email content from entering their mailboxes. Plus, it adds a layer of Cybersecurity and also saves time, e.g., Unroll.me

3. Hiring: NLP helps to hire managers to select and shortlist better candidates by filtering resumes. Automated candidate sourcing tools can scan the CVs of applicants to extract the required information and pinpoint the candidates who are the right fit for the job. This will save much time and give a more efficient solution. E.g., Oracle Taleo

4. Neural machine translation: It is one of the oldest applications of NLP. In this, machine translation uses a neural network to translate low-impact content like emails, regulatory texts, and so on and speed up communication with partners as well as other business interactions. The neural machine translation tool uses a bidirectional recurrent neural network, also called an encoder, to process a source sentence into vectors for a second recurrent neural network, called the decoder, to predict words in the target language. E.g., Google Translate.

5. Sentiment analysis: Also known as opinion mining, NLP helps in identifying the attitude, emotional state, judgment, or intent of the customer. This is done by either assigning polarity to the text (positive, neutral, or negative) or, in turn, making efforts to recognize the underlying mood of the context (happy, sad, calm, angry). This allows businesses to gain a broad public opinion on the organization and its services. It also helps in drawing competitive comparisons and making important adjustments in business strategies, whenever necessary. E.g., Repustate

6. Targeted Advertising: Businesses always emphasize reaching the maximum audience to increase the chances of lead generation. So, NLP can be an excellent source for intelligent targeting and placement of advertisements in the right place at the right time and with the right audience. This is done through analysis of search keywords, browsing behavior, emails, and social media platforms to find potential customers online. Targeted advertising works mainly on Keyword Matching. Mostly for this, text analytics, and text mining tools are leveraged. E.g., Apache OpenNLP

7. Copywriting: NLP can grow businesses by improving their content marketing strategy. It can write marketing content that better aligns with your brand voice and can provide insights on which messages are most appealing to your target audience— e.g., Alibaba's AI copywriter.

8. Insider Threat Detection: NLP-based insider threat applications can help determine if there is any illegal or nefarious intent within communications and detect threat patterns for rapid risk mitigation. This is vital since data breaches can cost huge losses to both companies and customers. E.g., Splunk