How to Get Started in NLP (Natural Language Processing)

Today we hear the name ChatGPT from most of the people around us. It has become a personal assistant to many. You must have wondered how chatbots like ChatGPT can correctly understand and respond to human language.

The answer is Natural Language Processing (NLP), which is all about teaching machines to understand human language. Just like we communicate with each other, NLP allows computers to communicate with humans using natural language. For example, a virtual assistant like Siri or Alexa tries to understand what humans say and respond appropriately.

NLP is used in a variety of applications, such as sentiment analysis, language translation, text classification, and more.

Table of Contents

1. Introduction to NLP
2. Applications of NLP
3. Natural Language Processing Pipeline
4. Advantages and Disadvantages
5. Future scope and Projects in NLP

Introduction to NLP

NLP or Natural Language Processing, is a technology that helps computers understand, interpret, and generate human language. It allows computers to communicate with humans using natural language, just like we do with each other. The goal of NLP is to make it easier for people to communicate with computers and for computers to understand and process human language.

The field of NLP can be categorized into three main parts:

Speech Recognition: The conversion of spoken language into written text.

Natural Language Understanding (NLU): The capacity of computers to comprehend human language.

Natural Language Generation (NLG): The production of human-like language by a computer.

Applications of NLP

Some of the applications of NLP are as follows:

• Speech recognition and transcription: Natural language processing (NLP) techniques are widely used to convert speech to text, which is important for dictation and voice-controlled assistants.
• NLP techniques are also used to translate text from one language to another, which is useful for jobs like global communication and e-commerce.
• Text summarization: NLP approaches are used to shorten large text documents, which is useful for tasks like news summarization and document indexing.
• Sentiment analysis: Natural language processing (NLP) techniques are used to assess the sentiment or emotion expressed in text, which is important for activities like customer feedback analysis and social media monitoring.
• Question answering: NLP approaches are used to answer natural language inquiries, which is important for activities like chatbots and virtual assistants.

NLP is a fast-expanding discipline used in different industries, including healthcare, education, e-commerce, and customer service. With advances in NLP, computers can now interpret and process human languages in ways that can be utilized for a variety of applications, such as speech recognition, language translation, question answering, and more.

Natural Language Processing Pipeline

The Natural Language Processing (NLP) pipeline includes the mentioned components:

• Raw Documents: The first step in NLP is to obtain the raw text data in the form of documents, such as tweets, articles, reviews.
• Pre-processing Pipeline: In this step, the raw text is pre-processed by performing various operations such as tokenization, stop-word removal, stemming, and lemmatization. This step is important as it helps to clean and normalize the data, making it easier to work.
• Feature Transformations: The pre-processed text is then transformed into features that can be used for machine learning models. This step involves operations such as vectorization, feature engineering, and normalization. This step aims to convert the text data into numerical features that can then be used as input to machine learning algorithms.
• Feature Extractor: After the pre-processing and feature transformation steps, the data is passed through a feature extractor, which selects and extracts the most relevant features from the data. This step is critical as it helps reduce the data’s dimensionality.
• ML Models for Classification: Once the extracted features are used to train Machine Learning models for classification. These models can be based on various algorithms, such as decision trees, random forests, neural networks, and support vector machines. This step aims to use the trained models to predict the class labels of new, unseen data.

Each step is important and requires careful consideration to ensure that the final model is accurate and effective.

Let us go through the Feature Transformations, which are a part of pre-processing techniques with the help of python example codes using the NLP libraries in Python.

Word tokenization

Word tokenization involves breaking down a sentence into individual words, also known as tokens. This is typically performed by splitting the sentence into spaces, although other methods can also be used. Punctuation marks are also considered tokens, as they have a distinct meaning and provide important information for NLP tasks.

For example, given the input sentence: “I love visiting the park.”

The word tokenization step would result in the following list of tokens:

Output:

1. “I”
2. “love”
3. “visiting”
4. “the”
5. “park”

Here is an example code

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By tokenizing the words, it becomes possible to perform subsequent NLP tasks, such as part-of-speech tagging, on individual words. Word tokenization is an important step in NLP that enables the computer to process text data and enable a deeper understanding of the language.

Stemming

Stemming is a process in Natural Language Processing (NLP) that involves reducing words to their root form. The goal of stemming is to reduce words to a common form so that they can be analyzed and compared in a more meaningful way.

For example, the words “running,” “runner,” and “ran” are all related to the concept of “run.” Stemming can reduce these words to their root form, “run,” making it easier to perform tasks such as text classification and information retrieval.

Stemming algorithms work by removing suffixes from words, such as -ing, -ed, -es. This results in a stemmed word that is often not an actual word but a base form that can be used to represent the word in various NLP tasks.

The below python example code will help better.

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There are several different stemming algorithms, including the Porter Stemmer, Snowball Stemmer, and Lancaster Stemmer. The choice of stemmer will depend on the specific NLP task being performed.

Remove Stop words

It involves identifying and removing common words such as “the”, “a”, “an”, “and”, “of”, that are unlikely to carry significant meaning in the text. These words are commonly referred to as “stop words” because they can be “stopped” or removed without affecting the overall meaning of the text.

For example, given the sentence: “I am going to the store to buy groceries.”

The stop words identification step would result in the following list of tokens and lemmas after removing stop words:

• “going” (go)
• “store” (store)
• “buy” (buy)
• “groceries” (groceries)

The following code will help understand better.

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By removing stop words, it becomes possible to reduce the size of the text data and focus on the most meaningful words in the text. Stop word identification is widely used in NLP models such as text classification, information retrieval, and text analysis. It helps to improve the efficiency of NLP models and to enable more effective analysis of the text data.

There are many more feature transformation steps Named Entity Recognition (NER), Predicting the Parts of Speech for each token, and Finding noun phrases that can be performed to improve the training process of the data.

Advantages and Disadvantages of NLP:

Future Scope

Bots: Chatbots help clients get to the point fast by answering questions and referring them to relevant resources and goods at all hours of the day and night. Chatbots must be fast, smart, and simple to use in order to be effective. Chatbots use NLP to interpret language through text or voice recognition interactions.

Supporting Invisible UI: Human communication, both verbal and written, is involved in almost every connection we have with technology. Amazon’s Echo is just one example of how humans will increasingly interact with technology in the future. An invisible or zero user interface will rely on direct communication between the user and the computer, whether by speech, text, or combination. NLP assists in making this concept a reality.

Smarter Search: One aspect of NLP’s future that we have been considering at Expert System for a long time is improved search. Instead of focusing on keywords or themes, smarter search allows a chatbot to understand a customer’s request and enable “search like we talk” capabilities (much like we could question Siri). Google recently announced the addition of natural language processing (NLP) capabilities to Google Drive, allowing users to search for documents and information using natural language.

Projects in NLP

To get started with NLP using Python, here is a list of a few projects that can be built to enhance our knowledge and gain more expertise in the field of NLP.

• Resume Screening with Python
• Keyboard Autocorrection Model
• Fake News Detection Model
• NLP for Whatsapp Chats
• Twitter Sentiment Analysis
• SMS Spam Detection Model
• Movie Reviews Sentiment analysis
• Amazon Product Reviews Sentiment Analysis

Conclusion

NLP methods are utilized in a vast range of applications like search engines, sentiment analysis, text summarization, question answering, machine translation, and more. NLP is a dynamic field with continuous advancements, particularly in deep learning, which has significantly improved NLP performance. Despite this progress, NLP remains a challenging field that requires a strong understanding of both computational and linguistic principles.

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