Natural Language Processing enabled bots are trained with comprehensive data, which helps to generate a precise solution for the customer’s queries.
With the arrival of Chatbots, the process of solving customer’s query has changed. It has become a highly valued entity in the industry. Every sector in the market is trying to enhance operations and reduce the dependency of a human through Chatbots. The global chatbot market size is predicted to grow from US$2.6 billion in 2019 to US$ 9.4 billion by 2024.
Chatbots are trained using ML models and pre-processed with static information to ease out customer experience. A small drawback of machine-learned chatbots is the inability to understand the context of user queries, which ensures their failure to answer certain questions. To concede this challenge, organizations are opting for the NLP enabled chatbots. These chatbots overcome the pre-existing challenge of language variations, thus understanding the context of the problem and giving a customized solution to the customer’s inquiry.
Based on the deep learning algorithms, NLP understands the meaning of the text and provides answers through conceptual analysis, like human beings. The chatbots are trained over multiple and varied interactions with conversations that they can confront while dealing with customers. The data from surveys, complaints, and emails are processed to NLP-enabled chatbots so that they can be trained. Moreover, these chatbots break down the question of the user into individual entities and analyze the syntax of the language.
Faster Bot Training using Natural Language Processing
An ML bot requires a bigger time, data and practice to get trained for generating accurate answers. This makes the training so time-consuming and labor-intensive.
NLP-enabled bots, on the other hand, are trained with comprehensive data so that the users are given a precise solution. The NLP-enabled bots are specially trained in an intelligible and complete understanding of verb tenses through conjugation, singular, and plural proper nouns, adjectives, adverbs for the precise response which can be generated for the queries of the customer. Moreover, with the help of basic meaning installed in these chatbots, the synonyms stored assists in identifying the missing element of the user query.
Also with this, NLP enabled bots to understand the language semantics and phrases, analyze, and make sense of the unstructured data generated through customer interaction. It also follows and interprets slangs and abbreviations, thus contributing to sentiment analysis.
