A Brief Guide to Chatbot Architecture
A Brief Guide to Chatbot Architecture
New to chatbots? No problem! Check out this short introduction to chatbot architecture and learn about the three classification methods that bots rely on.
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Humans always seem to be fascinated with self-operating devices and these days, it is human-like and automated chatbots that are of interest. The combination of immediate response and constant connectivity makes them an enticing way to extend or replace the trend of web applications. But how do these automated programs work? Let’s take a look.
How Do Chatbots Actually Work?
Chatbots work by adopting three classification methods.
1. Pattern Matching
Bots use pattern matching to classify text and produce a suitable response for customers. A standard structure of these patterns is the artificial intelligence markup language (AIML).
Here's a simple pattern matching example:
The machine then gives an output:
Human: Do you know who Abraham Lincoln is?
Robot: Abraham Lincoln was the U.S. President during American Civil War.
The chatbot knows the answer only because the name is in the associated pattern. Similarly, chatbots respond to anything related to the associated patterns. But they can not go beyond the associated pattern. To take it to a more advanced level, algorithms can help.
For each kind of question, a unique pattern must be available in the database to provide a suitable response. The many possible combinations of patterns that can create a hierarchical structure. We use algorithms to reduce the classifiers and generate a more manageable structure. Computer scientists call it a reductionist approach — in order to provide a simplified solution, reduce the problem.
Multinomial Naive Bayes is the classic algorithm for text classification and natural language processing (NLP). For instance, let’s assume a set of sentences that belong to a particular class is provided. Each word in an input sentence is counted for its occurrence and its commonality, and each class is assigned a score. The highest-scored class is the most likely to be associated with the input sentence.
Example sample training set:
“How are you doing?”
Example sample input sentence classifications:
Input: “Hello, good morning”
Term: “Hello” (no matches)
Term: “good” (class: greeting)
Term: “morning” (class: greeting)
Classification: Greeting (score=2)
With the help of equations, word matches are found using some sample sentences for each class. The classification score identifies the class with the highest term matches — but it also has some limitations. The score signifies the sentence's most likely intent but does not guarantee that it is the perfect match. The highest score only provides the relativity base.
3. Artificial Neural Networks
Neural networks are a way of calculating the output from the input using weighted connections calculated from repeated iterations while training the data. Each step through the training data amends the weights, resulting in an accurate output.
As discussed earlier, each sentence is broken down into different words and each word is then used as input for the neural networks. The weighted connections are then calculated by different iterations through the training data thousands of times, each time improving the weights to make it more accurate. The trained data of the neural network is a comparable algorithm of code. When there is a comparably small sample, i.e. in which the training sentences have 200 different words and 20 classes, then that would be a matrix of 200×20. But this matrix size increases by n times gradually and can cause a huge number of errors. In this kind of situation, processing speed should be considerably high.
There are multiple variations of neural networks, algorithms, and code. Complexity may also increase in some of the variations. But the fundamental idea remains the same: the most important job is classification.
Lastly, like most applications, chatbots are connected to databases. Chatbot databases are used to feed the chatbot information needed to give a suitable response to the user. Data about user activities and whether your chatbot was able to match their questions is captured in the datastore. NLP translates human language into information with a combination of patterns and text that can be mapped in real-time to find applicable responses.
Published at DZone with permission of Mitul Makadia . See the original article here.
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