Chatbots, voice-activated assistants and other AI-powered devices have been in use for over a decade. Over this time, human-to-machine interactions improved due to sophisticated data science and machine learning techniques. AI-powered tools and natural language processing (NLP) are rapidly evolving and gaining greater efficiency even as their cost goes down.
Statista predicted in 2019 that the NLP market is anticipated to grow to 43.9 billion dollars by 2025. As a result, many organizations are investing in NLP technologies.
This article provides a robust discussion of NLP use cases in a variety of business tasks.
NLP technology can improve the user experience while increasing security through the use of NLP-powered question generation. Software developers deploy algorithms that compose unique security questions that only specific users can answer.
The algorithm specifies personal data for a user, for example, a favorite football team, then explores data sources related to the topic, and extract information such as a coach’s name, year of foundation, etc, using a named entity recognition model. The entities are then used as possible answers to the questions the system will generate.
Whether it’s a marketing campaign or improved delivery of services, organizations rely on customer feedback to develop revenue-generating business strategies. Unfortunately, surveys and customer feedback can provide inconsistent results and may even require extra effort on the part of customer-facing employees. NLP and machine-learning techniques assist businesses and reduce workload through the use of automated sentiment analysis.
Sentiment analysis is an AI-powered and NLP-implemented tool that provides viable market research by analyzing customer reviews, comments, social media mentions and other digital footprints. As the tool crawls across the web, NLP is deployed using a sentiment analysis model that converts and classifies words and phrases into viable marketing insights.
The algorithm identifies frequently used phrases and classifies them as positive, neutral or negative. This information is packaged into meaningful market data that is used to inform marketing campaigns or other activities.
Customer service automation is becoming more sophisticated as technological improvements become more advanced.
Natural language models are being applied to a variety of NLP tasks such as text generation, classification, and summarization. Each of these tasks is advanced in their own right, but when combined they can be used to create sophisticated question-answering systems (QnA) that operate like automated chat assistants, or chatbots.
Chatbots depend on AI to start customer interactions and are capable of using NLP to field basic use scenarios. Should a request or scenario extend beyond its capabilities, NLP-based bots can bring in a human operator. NPL-bots’ ability for personalized conversation is a significant advantage for operations that depend on human employees for a range of complex tasks. Simple requests are handled by bots. Human workers are free for other tasks.
Recent innovations in language modeling like BERT and XLNet should relieve any organization's fears that chatbots are incapable of handling queries.
BERT is pre-trained on a 3.3 billion word corpus. Its 93.2% F1 score surpasses a human-level score of 91.2% that promises the bots will only get smarter in the future. XLNet is outperforming BERT on numerous NLP tasks.
Before starting a business or launching a new product, founders conduct analysis and research in order to size-up their competitors, evaluate the market, identify leads and target demographics and other details that will propel them to the peak of the industry.
Founders have many tools available for complex market analysis and research. Research tasks are simplified when NLP-powered tools automatically build an overview of a new company’s unique, competitive landscape.
The algorithm scans the Internet for words and data related to a predetermined list of companies and inputs this information into an NLP module. The module builds semantic relationships between the listed companies. Companies are ranked from zero to one using a multimodal semantic field that shows how closely companies relate to one another, creating a detailed competitive landscape.
Manual report writing is time-consuming. Automated NLP techniques free up time by converting unstructured text information into readable text using speech-to-text dictation and formulated data entry. NLP tools facilitate an automated report-writing process by identifying and locating missing data.
Machine learning consultants can design deep learning models that target relevant information within unstructured text, combining that information into specific, readable reports.
As seen in the examples above, natural language processing has a multitude of uses across diverse industries and provides a competitive advantage to businesses on the edge of expansion. With the growing amount of text data created daily, NLP will only become more and more important to extract valuable insights of the data and apply them in business applications.