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What is Natural Language Processing (NLP)?

Natural Language Processing is what allows chatbots to analyse input and respond appropriately. In this article you'll get a general idea of what NLP is and how it works.

Natural Language Processing is the ability of a computer program to understand human language as it is spoken.

In other words, NLP is the mechanism that allows chatbots—like NativeChat—to analyse what users say, extract essential information and respond with appropriate answers.

For example, if the user says: "When does your shop open?", the chatbot should be able to match this question to an opening-hours conversation, and respond with: "The shop opens from 9 AM to 5 PM".

What You are Going to Learn

The purpose of this article is to give you a general idea of what NLP is and how it works.

The idea is that with a better understanding of the NLP you should be able to train your chatbots to be able to understand their users a lot better.

There are many ways of handling each stage of the NLP processing, however, we will not go into too many details, as the aim of this article is to focus on the bigger picture.

The High-Level Understanding

From a high-level view, Natural Language Processing works in two stages:

NLP Model Training

In this stage, we (as the people that train our chatbots) provide our chatbots with a list of expressions that we would like them to associate with various conversations.

NLP takes all the training expressions and creates a model, which can be used to understand what the users say.

Note, the model training needs to be done up-front before the chatbot starts interacting with the users.

Process User Input and Respond

Each time a user says something, NLP takes the user input and matches to the NLP Model, analyses the results and responds with an answer or follow-up question.

NLP Model Training

Before we let our chatbots interact with the users, we need to train the NLP Model. But what does that even mean?

This is like creating a mental image of all the conversations that a chatbot should be aware of, together with what kind of expressions should trigger each conversation.

It is important to understand that the NLP Model is specific to the scenarios that it is meant to deal with. So, a banking chatbot should be able to understand and respond to Bank related requests (like: "How can I change the limit on my credit card?"), while a shopping chatbot should be able to deal with conversation helping a happy shopper.

Example

For example, we could train our chatbot to deal with two conversations to allow the users to ask for the office Address or Phone number.

  Training Questions Response
 Address Q&A  What is your address?
 Where are you located?
 14 Oak Park Drive, Bedford, MA 01730
 Phone Q&A  What phone can I reach you on?
 What is your phone number?
 +1-781-280-4000


The chatbot NLP Model is formed in three steps:

  • Tokenization
  • Highlighting Frequent Tokens
  • Collision Detection

Tokenization

Tokenization is a process in which we take an expression and split it into individual words and punctuation marks, which are called tokens.

For example: "What is your address?", would be turned into five tokens. Like this:

Tokenization

Example

Similarly, all training questions should be turned into the following tokens:

Tokenization for all items

The purpose of the tokens is for NLP to compare the training tokens, with the user input when they interact with the chatbot.

However, before that happens, NLP needs to do some further processing to the training tokens, to identify the tokens that are more or less important when a match happens.

Highlighting Frequent Tokens

You can probably imagine that not all tokens are equally important.

NLP highlights more valuable tokens by identifying the ones that repeat within the same conversation. The idea is to identify the words/punctuation marks that are commonly used to trigger a specific conversation.

NLP goes through each conversation one by one, analyses the Conversation Tokens, and highlights the frequent ones. However, at this stage, it doesn't compare tokens belonging to other conversations.

Example

For example, if we look at the training set for the Address Q&A. You will notice that the (?) is used in both expressions, however, (your) and (you) shouldn't count as repeating tokens as they are not exactly the same (well... not unless you live in the North of England 🤔).

Highlight Address Q&A Conversation 

While, if we look at the training set for the Phone Q&A. You will notice that (what), (phone) and (?) are used between both of the expressions.

Highlight Phone Q&A Conversation 

Collision Detection

Just like the frequent tokens for each conversation are highlighted as more important, NLP also identifies those tokens that keep repeating across different conversations. This is called Collision Detection.

The idea is that if the token (what) is used in training for multiple conversations, then you can imagine that matching that token is not as helpful as matching a unique or a highlighted token. Therefore, we should treat it as less important.

To understand this process better, it is best to visualise each conversation tokens as they belong to their respective set of tokens. As a result, we can display the highlighted tokens (in green) as a single token in each set. Like this:

Tokens as a Set

As part of the Collision Detection step, NLP looks for duplicates between all conversations sets and marks them as collisions.

Example

Following our example, we can compare the tokens on the left with the tokens on the right, and find 5 collisions (what), (is), (your), (you) and (?) and mark them as collisions (in red). Like this:

Collision Detection

The NLP Model

Finally, we can represent the whole model in a Venn Diagram, where:

  • Unique (blue) and Highlighted (green) Tokens — sit in their respective sets
  • The Collision Tokens (red) — sit at the intersection of the sets

Collision Detection with Sets Intersection

Matching User Expressions

Once your chatbot has a trained NLP Model, it can attempt to communicate with users.

Each time the user says something, NLP handles the input in the following steps:

  • (1) find matching conversations
  • (2) score the best match
  • (3) respond to the user

Finding Matching Conversations

To find matching conversations NLP takes the expression said by the user and splits it into tokens. Then it compares the user tokens with those in the NLP Model, to find all matching tokens and the matching conversations.

As a result of which, NLP should be able to identify which conversation matched the most tokens and the quality of each token match.

Example #1 — Where is your office located?

Coming back to our example, when the user says: "Where is your office located?" NLP should be able to identify the following matches:

Address Q&A conversation — 5 matched tokens

  • 2 unique tokens
  • 3 penalised tokens

Phone Q&A conversation — 3 matched tokens

  • 3 penalised tokens

Based on these matches we can see that the Address Q&A conversation is the best matching conversation.

example: where is your office located

 

Example #2 - What are your phone number?

In another example, the user says (the grammar mistake is on purpose): "What are your phone number?"

NLP should be able to identify the following matches:

Address Q&A conversation — 4 matched tokens

  • 1 unique token
  • 3 penalised tokens

Phone Q&A conversation — 5 matched tokens

  • 2 unique tokens — with 1 highlighted token
  • 3 penalised tokens

As a result, the Phone Q&A conversation comes out as the favorable match.

example: what are your phone number

Scoring the Best Match

The next step is to give a confidence score to the best-matched conversations.

Without getting into the details of the confidence score calculation, NLP takes into consideration the number of:

  • Matched Tokens — the more the better
  • Highlighted Tokens — each increases the confidence score
  • Collision Tokens — each reduces the importance of the token match
  • Input Tokens — longer input requires more matched tokens

All the above details are provided to the NLP Calculator, which returns a confidence score from 0 to 100% (or 0 to 1), which is the confidence the NLP has that a given conversation is a good match.

confidence scoring

Responding to the User

Finally, if the confidence score is above 65%—this is considered a successful match—the chatbot will respond with the appropriate answer. While for any confidence score below that threshold—this is considered a low confidence match—the chatbot will respond with something like: "Sorry, I don't understand."

Example

Finally, the user expression "Where is your office located?" would result in a high confidence match to the Address Q&A conversation. As a result of which the chatbot would respond with: "14 Oak Park Drive, Bedford, MA 01730."

confidence based response

Final Word

From a high-level view, NLP is a fairly straightforward process—although the devil sits in the details—which we can use to our advantage when we need to train our own chatbots.

If you want to improve your chatbots understanding of the user input, then make sure to give it more helpful training data:

  • Help it understand which words should be considered more important, by repeating the important words
  • If reasonably possible, avoid expressions that have too many penalised words

But also, you should be able to understand when and why adding a new conversation to the training set could potentially make previously high confidence conversations start giving lower confidence scores.

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