Go Beyond Basic Chatbot: Explore Advanced NLP Techniques

In our day-to-day lives, most of us have come across using a chatbot, maybe without even knowing it. Advanced NLP Techniques help these chatbots understand human queries to provide a solution. But have you ever wondered what a Chatbot is? Or how it works and what its functions are? Let’s find out

What a Chatbot is.

A chatbot is a computer program that operates through the cloud(at the backend), it assumes and processes Human queries and conversations, allowing us to interact with digital apparatuses as if interacting with a real person.

How does the interaction flow of Chatbot work?

When you frame a message or speak to a chatbot through a user interface, the message flows from the chatbot to its cloud at the backend. Which then comprehends the intended message using NLP techniques or AI and searches its database of pre-programmed responses for a relevant answer. The chatbot then provides the answer to you through the user interface. A user can choose to end the conversation right there or if they continue further then this process repeats.

Natural Language Processing (NLP)

Natural Language Processing is a field of computer science that falls under Artificial intelligence. NLP helps computers understand human language by translating unstructured human texts into structured ones for the computer to understand. The process of translating unstructured texts into structured ones is known as Natural Language Understanding (NLU) and vice versa as Natural Language Generation (NLG). Therefore, NLP uses computational Linguistics, machine learning, and statistical models to make computers understand the intent and meaning behind the language.

Why should we use NLP? What Benefits can we get from Advanced NLP techniques?

Employing NLP can be very beneficial to analyzing unstructured data and transforming it into a structured one. Following are a few benefits of NLP

Advanced NLP Techniques

NLP is an intricate field that involves techniques to analyze text and comprehend human language. It uses various tools to complete the process of structuring the data. The process ranges from simple text digestion to advanced machine-learning algorithms. Advanced NLP techniques that are widely used for natural language understanding are given below.

1. Tokenization

Tokenization is a basic NLP technique that breaks down a text string into small parts. These small parts are called tokens. These tokens take the form of symbols, numbers, etc. Tokens make developing an NLP model easier for data scientists by understanding the concept.

Here are some of the tokenization techniques that are used in NLP:

• Rule-based Tokenization
• Subword Tokenization
• Penn Tree Tokenization
• White Space Tokenization
• Spacy Tokenization
• Dictionary Based Tokenization

2. Stemming and Lemmatization

The next important technique is Stemming and lemmatization. Stemming aims to eliminate the suffix or prefix to get down to the word’s root. For example,  if the word is ‘better’, stemming will bring it down to ‘bet’.

However, compared to stemming, lemmatization is a more advanced technique of NLP. It focuses on vocabulary and morphological analysis to return the lexical form of the word. This form is known as the lemma.

3. Stop Words Removal

There are words in the unstructured data that make it unnecessarily long. Words like ‘and,’ ‘or,” but,’ etc are stop words. After Stemming and Lemmatization this step removes all the stop words that create noise in the data. This helps in making the data concise. However, stop word removal in every NLP model is not mandatory. There are various libraries like Gensim or SpaCy for word removal.

4. Speech Recognization

The process of converting speech into text is known as Automated Speech Recognition (ASR) or Speech Recognition. For example, if you present an audio recording, ASR will analyze it and transcribe it into written text. ASR is divided into two models, that is:

• Acoustic Model: For phonetic representation of sound signals
• Language Model: For the knowledge of the structure of the language like words, grammar, etc.

5. Text Summarization

When we write a long text, it makes it difficult for the computer to understand it. Here’s where Text summarization comes in, it serves the purpose of cutting short the long text. To get a concise and summarized version with the main information present in it. It is a very efficient and useful NLP technique. There are two text summarization tools:

• Summarization based on Extraction
• Summarization based on Abstraction

6. Named Entity Recognition (NER)

NER is one of the most interesting NLP techniques. Named Entity Recognition, much like its name, serves the purpose of identifying named entities within the text. Entities such as persons, corporations, sites, etc. Businesses can benefit from this type of NLP technique as it makes it easier to extract structured data that can be useful for data mining. Here are a few NER systems:

• Dictionary-based
• Rule-based
• Machine Learning based

Here is an example of NER, suppose the text is, ‘Narendra Modi visited ISRO.’

Now, NER will identify Narendra Modi as a person and ISRO as an organization.

Conclusion

In the enormous world of NLP, we can find many models, techniques, and evolvement. We now know a few basics of NLP and how a chatbot works. Digging deeper we will find the potential that NLP holds in the future of AI. As the use of AI is increasing, NLP techniques are also developing. These techniques are extremely useful for organizations, businesses, and competitive fields to gather information from unstructured unending data on the internet.

References

• 8 Advanced NLP Techniques with Case Studies
• Top 14 NLP Techniques for Data Scientists in 2024