Machine Learning for Chatbots
Machine Learning for Chatbots: Definition, Processes, and Use Contexts
Machine learning technology is increasingly being used for chatbots.
To understand how machine learning is used in the development of chatbots, it is essential to understand the different chatbots and how machine learning technology can make chatbot communication more intelligent and customer-centric.
Chatbots are interactive automation programs or conversational tools for automating user communication. Apart from this core capability, chatbots can be of different types with various features and capabilities. Chatbot programs allow users to interact with a website, mobile app, or computer program through a chat interface. We can use chatbots for functional benefits, promotions, guiding customers, solving customer problems, handling queries, and entertaining users with interactive conversations.
The rule-based chatbots used in e-commerce stores mainly serve the purpose of providing customer service. Naturally, they are easier for the developers, involve less technical complexities, and focus on completing elementary tasks. But since Artificial Intelligence (AI) and its offset technology, Machine Learning have made significant progress in recent times opening new vistas and avenues of machine-led conversation to address customer situations and contexts more accurately, even for the mobile app development services company, these rule-based chatbots are increasingly getting obsolete and outdated.
Machine Learning now refers to a set of technologies, including some established and acclaimed technologies and a few promising new technologies still in the making. For example, Natural Language Processing (NLP) is now seen as a constituent machine learning technology for chatbots that helps to understand patterns and data-driven insights from large volumes of data. The rest of the post will explain how machine learning and NLP can be incorporated into building chatbots.
Clean data the chatbots can easily understand is crucial for high-performance and intelligent chatbots. They now use several data processing methods for effective data processing to make Chatbots work smoothly. For preprocessing the data for the chatbot, removing stop words, capital letters, and labels are essential.
NLP technology also uses the lexical feature to help to focus on the words instead of sentence structures or grammar. Some preprocessing methods that this linguistic approach uses include word-level n-grams, stemming, and lemmatization.
The syntactic approach to processing data uses tagging and chunking the part of speech (POS). According to this approach, POS tagging is acceptable for each word to determine the part of speech for each word, including nouns, pronouns, verbs, etc. We use the following method for partitioning sentences into various segments that are non-overlapping in nature.
The semantic approach helps you keep your attention fixed on sentence structure while the meaning of words is considered separately.
Vector Representation is another central approach using vector maps to show high-dimensional and low-dimensional words. Every word spoken in the communication comes subjected to specific rules and relationships to represent the same with vector coordinates. By this approach, closely related phrases are placed nearby or together. For vector representation, standard techniques used by the chatbots include word2vec, doc2vec, and Global Vectors.
The retrieval technique is widely popular now for understanding short and tiny texts in chatbot conversations. Some machine learning experts and researchers devised this solution to tackle the problem of short-length chatbot conversations. As per this approach, short and often single-word conversations are collected from different social chats and discussions and are used to train various models.
The generation-Based approach also effectively feeds the chatbot models using an encoder-decoder framework. This approach is very similar to the increasingly popular sequence-to-sequence (seq2seq) model for predicting the following sentence in a conversation.
Long Short-Term Memory (LSTM) network is another central approach that focuses on the probability of creating a response based on previous conversations. This is much similar to the encoder-decoder method we just explained.
For the details, please read the original article Machine Learning for Chatbots: Definition, Processes, and Use Contexts at CMARIX.