How does a chatbot actually work?
Have a brief Guide to Chatbot Architecture, How these Chatbots work? How it processes Human Language? How does the Chatbot learn after it’s live?
An AI Chatbot Work By Utilizing Three Classification Methods:
- Pattern Matches
- Algorithm Based
- Artificial Intelligence Based (Artificial Neural Networks)
- Natural Language Understanding (NLU)
- Natural Language Processing (NLP)
1. Pattern Matches:
Bots utilize pattern matches to group the text and it produces an appropriate response from the clients. “Artificial Intelligence Markup Language (AIML), is a standard structured model of these Patterns.
A simple example of Pattern matching :
In this kind of method bots always react to some related pattern which is already been there in AIML.
But it can’t go past the related pattern. For every sort of question, a striking pattern must be accessible in the database to give a thoughtful response.
With a number of pattern combinations, it makes a hierarchical structure. We utilize algorithms to lessen the classifiers and produce a more logical structure.
Chatbot knows the answer only because his or her name is in the associated pattern. Similarly, chatbots respond to anything relating it to the associated patterns.
But it can not go beyond the associated pattern. To take it to an advanced level algorithm method can help, which we will discuss later.
2. Algorithm For A Chatbot
For each kind of question, a unique pattern must be available in the database to provide a suitable response. With lots of combinations of patterns, it creates a tree structure.
We use algorithms to reduce the classifiers and generate a more manageable structure. Computer scientists call it a “Reductionist” approach- in order to give a simplified solution, it reduces the problem.
With proper steps, we can provide bots a more proper understanding of human interaction.
Multinomial Naive Bayes is the algorithm for text classification and NLP. Let’s have a little glance on how the algorithm works:
Let each row of our term-document training matrix be the feature count vector for training case i.
tf_train[i] # feature count vector for training case i y_train[i] # label for training case i
The count vectors are defined as:
p = sum of all feature count vectors with label 1
p = tf_train[y_train==1].sum(0) + 1
q = sum of all feature count vectors with label 0
q = tf_train[y_train==0].sum(0) + 1
Notice that we add 1 to both count vectors to ensure that every token appear at least one time in each class.
The log-count ratio r is:
r = np.log((p/p.sum()) / (q/q.sum()))
b = np.log(len(p) / len(q))
Just the ratio of number of positive and negative training cases.
For instance, let’s assume a set of sentences are given which are belonging to a particular class. With the new input sentence, each word is counted for its occurrence and is accounted for its commonality and each class is assigned a score.
The highest scored class is the most likely to be associated with the input sentence.
lets take a sample training set
lets take a Input Sentence classification:
Classification score identifies the class with the highest term matches but it also has some limitations. This score identifies the most likely one but does not guarantee the perfect match.
This method relies on relativity.
3. Artificial Intelligence Based (Artificial Neural Networks) For Chatbot:
Neural Networks are a way of calculating the output from the input using weighted connections which are calculated from repeated iterations while training the data.
The weights, as well as the functions that compute the activation, can be modified by a process called learning which is governed by a learning rule.
Each step through the training data amends the weights resulting in the output with accuracy.
Each sentence is broken down into different words and each word then is used as input for the neural networks. The weighted connections are then calculated by different iterations, and each iteration goes through the training data thousands of times.
Every time improving the weights to making it accurate. The trained data of the neural network is a comparable algorithm more and less code. The trained data is used to compare and check relativity.
When there is a comparably small sample, where the training sentences have m different words and n classes, then that would be a matrix of m×n. But this matrix size increases more gradually and can cause a huge number of errors.
In this kind of situation, processing speed should be considerably high as there is a huge amount of data.
There are multiple variations in neural networks, algorithms as well as patterns matching code. Complexity may also increase in some of the variations. But the fundamental remains the same, and the important work is that of classification.
Natural Language Understanding (NLU) For Chatbot
Natural Language Understanding is a collection of APIs that offer text analysis through natural language processing. This set of APIs can analyze text to help you understand its concepts, entities, keywords, sentiment, and more.
Additionally, you can create a custom model for some APIs to get specific results that are tailored to your domain. This system is for demonstration purposes only and is not intended to process Personal Data.
Natural language Understanding(NLU) is a branch of natural language processing (NLP), which helps computers understand and interpret human language by breaking down the elemental pieces of speech.
While speech recognition captures spoken language in real-time, transcribes it, and returns text, NLU goes beyond attention to determine a user’s intent.
Speech recognition is powered by statistical machine learning methods that add numeric structure to large datasets.
In NLU, machine learning models improve over time as they learn to recognize syntax, context, language patterns, unique definitions, sentiment, and intent.
The business application relies on NLU, which helps chatbots to understand human behavior and route them to the right task.
Twilio Autopilot, the first fully programmable conversational application platform, includes a machine learning-powered NLU engine. Autopilot enables developers to build dynamic conversational flows.
It can be easily trained to understand the meaning of incoming communication in real-time and then trigger the appropriate actions or replies, connecting the dots between conversational input and specific tasks.
The Impact Of NLU In Empowering Digital Market Growth
API such as Twilio Autopilot, NLU is widely used for customer communication. NLU provides customers to navigate menus and collect information which is easier and faster and creates a better experience for the customer resulting in better market growth.
Businesses use Autopilot to build conversational applications such as messaging bots and voice assistants.
Developers only need to design, train, and build a natural language application once to have it work with all existing (and future)channels such as voice, SMS, chat, Messenger, Twitter, WeChat, and Slack.
Areas where NLU is being used in applications that interact with human language:
Turn nested phone trees into a simple “what can I help you with” voice prompts. Analyze answers to “What can I help you with?” and determine the best way to route the call.
Automate data capture to improve lead qualification, support escalations, and find new business opportunities. For example, ask customers questions and capture their answers using Access Service Requests (ASRs) to fill out forms and qualify leads.
Build fully-integrated bots, trained within the context of your business, with the intelligence to understand human language and help customers without human oversight. For example, allow customers to dial into a knowledge base and get the answers they need.
Natural Language Processing (NLP) For Chatbot:
Natural Language Processing, or NLP for short, is broadly defined as the automatic guidance of natural language, like speech and text, by software.
The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers.
Natural language processing systems take strings of words (sentences) as their input and produce structured representations capturing the meaning of those strings as their output. The nature of this output depends heavily on the task at hand.
A natural language understanding system serving as an interface to a database might accept questions in English which relate to the kind of data held by the database. In this case, the meaning of the input (the output of the system) might be expressed in terms of structured SQL queries which can be directly submitted to the database.
History of NLP
The first use of computers to manipulate natural languages was in the 1950s with attempts to automate translation between Russian and English [Locke & Booth].
These systems were spectacularly unsuccessful requiring human Russian-English translators to pre-edit the Russian and post-edit the English.
Based on World War II code-breaking techniques, they took individual words in isolation and checked their definition in a dictionary. They were of little practical use.
Popular tales about these systems cite many miss-translations including the phrase “hydraulic ram” translated as “water goat“.
In the 1960s natural language processing systems started to examine sentence structure but often in an ad hoc manner. These systems were based on pattern matching and a few derived representations of meaning.
The most well known of these is Eliza [Weisenbaum] though this system was not the most impressive in terms of its ability to extract meaning from language.
Serious developments in natural language processing took place in the early & mid-1970s as systems started to use more general approaches and attempt to formally describe the rules of the language they worked with.
LUNAR [Woods 1973] provided an English interface to a database holding details of moon rock samples.
SHRDLU [Winograd] interfaced with a virtual robot in a world of blocks, accepting English commands to move the blocks around and answer questions about the state of the world.
Since that time there has been a parallel development of ideas and technologies that provide the basis for modern natural language processing systems.
Research in computer linguistics has provided greater knowledge of grammar construction [Gazdar] and Artificial Intelligence researchers have produced more effective mechanisms for parsing natural languages and for representing meanings [Allen].
Natural language processing systems now build on a solid base of linguistic study and use highly developed semantic representations.
Recently (during the 1990s) natural language systems have either focused on specific, limited domains with some success or attempted to provide general purpose language understanding ability with less success.
A major goal in contemporary language processing research is to produce systems that work with complete threads of discourse (with human-like abilities) rather than only with isolated sentences [Russell & Norvig(a)]. Successes in this area are currently limited.
Impact of NLP on Bigdata
Interest in natural language processing (NLP) began in earnest in 1950 when Alan Turing published his paper entitled “Computing Machinery and Intelligence,” from which the so-called Turing Test emerged. Turing basically asserted that a computer could be considered intelligent if it could carry on a conversation with a human being without the human realizing they were talking to a machine.
The goal of natural language processing is to allow that kind of interaction so that non-programmers can obtain useful information from computing systems. This kind of interaction was popularized in the 1968 movie “2001: A Space Odyssey” and in the Star Trek television series.
Natural language processing also includes the ability to draw insights from data contained in emails, videos, and other unstructured material.
“In the future,” writes Marc Maxson, “the most useful data will be the kind that was is too unstructured to be used in the past.” [“The future of big data is quasi-unstructured,” Chewy Chunks, 23 March 2013]
Maxson believes, “The future of Big Data is neither structured nor unstructured. Big Data will be structured by intuitive methods (i.e., ‘genetic algorithms’), or using inherent patterns that emerge from the data itself and not from rules imposed on data sets by humans.”
Alissa Lorentz agrees with Maxson that the amazing conglomeration of data now being collected is mostly of the unstructured variety. “The expanding smorgasbord of data collection points are turning increasingly portable and personal, including mobile phones and wearable sensors,” she writes, “resulting in a data mining gold rush that will soon have companies and organizations accruing Yottabytes (10^24) of data.”
Maurizio Lenzerini agrees with Lorentz. Even if the data is structured, he notes, integrating and relating that data can be an IT nightmare.
As he puts it, “The problem is even more severe if one considers that information systems in the real world use different (often many) heterogeneous data sources, both internal and external to the organization.
” He adds, “If we add to the picture the (inevitable) need of dealing with big data, and consider in particular the two v’s of ‘volume’ and “velocity,” we can easily understand why effectively accessing, integrating and managing data in complex organizations is still one of the main issues faced by IT industry nowadays.”
Although he didn’t specifically mention the third “V” — variety — that was what he had in mind when he was discussing heterogeneous data sources. “When talking about data variety,” writes Ling Zhang, “most often people talk about multiple or diverse data sources, variant data types, structures, and formats say, structured, semi or non-structured data like text, images, and videos.” She goes on to explain that variety involves even more complex because you have to consider subjectivity.
It should be clear by now that natural language processing involves a lot more than a computer recognizing a list of words. As Mark Kumar asserts, “The issue of data variety remains … difficult to solve programmatically. … As a result, many big data initiatives remain constrained by the skills of the people available to work on them.
And this challenge is keeping the industry from realizing the full potential of big data in diverse fields.”Kumar agrees with Lorentz that, “when it comes to data variety, a large part of the challenge lies in putting the data into the right context.”
We believe that only a system that can sense, think, learn, and act is going to be up to the challenge of performing natural language processing.
Our Cognitive Reasoning Platform uses a combination of artificial intelligence and the world’s largest common sense ontology to help identify relationships and put unstructured data in the proper context.
The reason that a learning system is necessary is that the veracity of data is not always what one would desire.
Most analysts appear to agree that the next big thing in IT is going to involve a semantic search. It’s going to be a big thing because it will allow non-subject matter experts to obtain answers to their questions using only natural language to pose their queries. The magic will be contained in the analysis that goes into the search that leads to answers that are both relevant and insightful.
This blog basically focuses on how chatbots work by using different methods and how these methods have a great influence on market growth and also we will be understanding the important facts about these methods. This blog also covers how these methods have changed the total perception of business in the way to be more productive.
The Revolution of chatbot started on the scene in 2011 as business intelligence, artificial intelligence and messaging platforms combined into new forms of responsive technology. New ways were needed to support companies interacting with buyers and provide customer support that aligned and could evolve with changing communication habits. All thanks to our chatbot friends. 🙂
Hope we’re not just the biological boot loader for digital superintelligence. Unfortunately, that is increasingly probable— Elon Musk (@elonmusk) August 3, 2014
Part III is coming next week.