What is natural language processing?

Natural Language Processing (NLP) is a branch of AI that enables computers to understand and interpret text and spoken words, similar to how humans do. In today’s digital landscape, organizations accumulate vast amounts of data from different sources, such as emails, text messages, social media posts, videos, and audio recordings. NLP allows organizations to process and make sense of this data automatically. With NLP, computers can analyze the intent and sentiment behind human communication. From customer service chatbots in retailing to interpreting and summarizing electronic health records in medicine, NLP plays an important role in enhancing user experiences and interactions across industries.

Text Preprocessing in NLP

Natural Language Processing (NLP) has seen tremendous growth and development, becoming an integral part of various applications, from chatbots to sentiment analysis. One of the foundational steps in NLP is text preprocessing, which involves cleaning and preparing raw text data for further analysis or model training. Proper text preprocessing can significantly impact the performance and accuracy of NLP models.

Working in natural language processing (NLP) typically involves using computational techniques to analyze and understand human language. This can include tasks such as language understanding, language generation, and language interaction. It includes steps such as Text Input and Data Collection, Text Preprocessing, Text Representation, Feature Extraction, Model Selection and Training, Model Deployment and Inference, Evaluation and Optimization, Iteration and Improvement .

Why Text Preprocessing is Important?

Raw text data is often noisy and unstructured, containing various inconsistencies such as typos, slang, abbreviations, and irrelevant information. Preprocessing helps in:

• Improving Data Quality: Removing noise and irrelevant information ensures that the data fed into the model is clean and consistent.
• Enhancing Model Performance: Well-preprocessed text can lead to better feature extraction, improving the performance of NLP models.
• Reducing Complexity: Simplifying the text data can reduce the computational complexity and make the models more efficient.

Sentence Detection (Sentence Boundary Detection)

Sentence Boundary Detection locates the start and end of sentences in a given text. You can divide a text into linguistically meaningful units to perform tasks such as part of speech tagging and entity extraction.

Tokenization

Tokenization refers to a process of segmenting input text into words, punctuation, etc. It allows you to identify the basic units in your text that are called tokens.

Stop Words Removal

Stop words are the most common words in a language. Examples of stop words are the, who, too, and is. We usually remove the stop words because they are not significant in many text mining tasks such as word frequency analysis. You can identify and remove stop words by using NLTK's list of stop words after tokenizing the text.

Stemming

Stemming refers to a text processing task that reduces words to their root. For example, the words "adventure", "adventurer", and "adventurous" share the root adventur.” Stemming allows us to reduce the complexity of the textual data so that we do not have to worry about the details of how each word was used.

Word Frequency

Word frequency is an analysis that gives you insights into word patterns, such as common words or unique words in the text.

Part-Of-Speech (POS) Tagging

Part of speech (POS) analyzes the grammatical role each word plays in a sentence. In other words, it determines to which category each word (Noun, Pronoun, Adjective, Verb, Adverb, Preposition, Conjunction, and interjection) belongs. POS tags are useful when you want to assign a syntactic category to each word of the text for future analysis.

Lemmatization

Lemmatization is the process of reducing inflected forms of a word while ensuring that the reduced form belongs to a language. This reduced form or root word is called a lemma. For example, “visits”, “visiting”, and “visited” are all forms of “visit” (lemma). Here, "visit" is the lemma. The inflection of a word also reduces numbers (car vs cars). Lemmatization is an important step because it helps you reduce the inflected forms of a word so that they can be analyzed in the text more efficiently.

Chunking

Unlike tokenization, which allows you to identify every single word and sentence, chunking allows you to identify phrases in a textual input. Chunking allows you to extract a word or group of words that work as a unit to perform a grammatical function. The following examples are all examples of phrases:

• “A diet"
• “A fruitarian diet"
• “A meaningful fruitarian diet”

Chinking

Chinking is often used together with chunking. While chunking is used to identify and find a pattern, chinking is used to exclude a pattern.

Named Entity Recognition (NER)

A named entity is an object’s assigned name, for example, a person’s name, a film, a book title, or a song’s name. NLTK can recognize these named entities in a document by asking the model for a prediction. Because the performance of the models depends on the examples they were trained on, NEF might not always work perfectly and you might need to adjust the tuning based on your case.

Text Cleaning

We'll convert the text to lowercase, remove punctuation, numbers, special characters, and HTML tags.

Handling Contractions

Expanding contractions in the text.

Handling Emojis and Emoticons

Converting emojis to their textual representation.

Spell Checking

Correcting spelling errors in the text.