Introduction to Natural Language Processing (NLP)

Natural Language Processing (NLP) enables computers to understand and interpret human language. While computers excel at processing structured data, such as spreadsheets or databases, natural language in its unstructured form (text, speech, etc.) presents a unique challenge. NLP bridges this gap by allowing machines to process and understand human languages, making it an essential tool in modern AI systems.

Let’s learn more about Natural Language Processing in this article.

Need for Natural Language Processing

The growing amount of unstructured natural language data in the world makes it increasingly important for machines to comprehend and analyze it effectively. By training NLP models, we can equip computers to process language data in a variety of forms, from written text to voice input. With centuries of human-written literature and massive data available, it’s vital to teach computers to interpret that wealth of information. However, this task comes with significant challenges, such as resolving ambiguities in meaning, Named-Entity Recognition (NER), and coreference resolution.

While NLP systems are improving, they still face difficulties in understanding the exact meaning of sentences.

For example, consider the phrase: “The boy radiated fire-like vibes.” Does it refer to a motivating personality or imply something literal? Such ambiguities make text analysis complex for computers.

To solve these challenges, NLP breaks down language understanding into smaller, manageable components. This approach, known as the NLP pipeline, involves several stages that collectively enable machines to interpret human language effectivel

Key Steps in Natural Language Processing (NLP) Pipeline

1. Sentence Segmentation

Sentence segmentation is the first step in NLP, which involves breaking text into individual sentences. This helps the computer understand the structure of the text.

Example Input: San Pedro is a town on the southern part of Ambergris Caye in Belize. According to estimates, it has a population of 16,444.

Output:

• Sentence 1: San Pedro is a town on the southern part of Ambergris Caye in Belize.
• Sentence 2: According to estimates, it has a population of 16,444.

2. Word Tokenization

Word tokenization divides sentences into smaller components called tokens (words or punctuation). These tokens are crucial for understanding how a sentence is structured.

Example Input: San Pedro is a town in Belize.

Output: Tokens: [‘San’, ‘Pedro’, ‘is’, ‘a’, ‘town’, ‘in’, ‘Belize’]

3. Predicting Parts of Speech (POS)

POS tagging involves identifying the function of each word in a sentence, such as whether it’s a noun, verb, or adjective. This helps determine the role each word plays in the context of the sentence.

Example Input: San Pedro is a town.

Output: ‘San Pedro’ – Noun, ‘is’ – Verb, ‘a’ – Article, ‘town’ – Noun

4. Lemmatization

Lemmatization converts words to their root forms. For example, “Buffalo” and “Buffaloes” are both lemmatized to “Buffalo,” ensuring that variations of the same word are treated identically.

Example Input: There are Buffaloes grazing in the field.

Output: Buffalo (root word)

5. Stop Word Removal

Stop words (e.g., “a,” “the,” “and”) are common words that provide minimal meaning. Removing them helps reduce noise and improve the efficiency of NLP models.

6. Dependency Parsing

Dependency parsing identifies relationships between words, creating a syntactic tree. This helps understand the grammatical structure of a sentence and the roles of each word.

Example Input: San Pedro is an island in Belize.

Output: Parse Tree: ‘San Pedro’ (subject) → ‘is’ (verb) → ‘island’ (object)

Noun phrases group related words to represent a specific concept. In the sentence “The second-largest town in the Belize District,” we can extract the noun phrase “second-largest town.”

7. Named Entity Recognition (NER)

NER identifies and categorizes entities such as people, places, or dates in text.

Example Input: San Pedro is a town on the southern part of the island of Ambergris Caye in the Belize District of the nation of Belize, in Central America.

Output:

• San Pedro: Geographic Entity
• Ambergris Caye: Geographic Entity
• Belize: Geographic Entity
• Central America: Geographic Entity

8. Coreference Resolution

Coreference resolution identifies when two or more expressions in a text refer to the same entity. For example, the word “it” might refer to a specific person or thing earlier in the sentence.

Example Input: San Pedro is a town on the southern part of the island of Ambergris Caye. According to 2015 mid-year estimates, the town has a population of about 16,444. It is the second-largest town in the Belize District.

Output: “It” refers to “San Pedro.”

Techniques Used in NLP

NLP techniques can be broadly categorized into two approaches:

1. Rule-based Methods: These involve manually created rules and heuristics to process language data. For example, defining patterns in language to extract meaning.
2. Machine Learning (ML) and Deep Learning (DL): These involve using algorithms to automatically learn from data and improve over time. ML models such as decision trees, support vector machines, and deep learning models like recurrent neural networks (RNNs) and transformers are commonly used in modern NLP.

One of the most prominent breakthroughs in NLP in recent years has been the use of transformers, a type of deep learning architecture that powers models like BERT and GPT. These models excel at tasks like language understanding and generation, enabling applications like chatbots and automated content creation.

Applications of NLP

1. Speech Recognition: Converting spoken language into text, which powers voice assistants like Siri and Alexa.
2. Language Translation: Automatically translating text from one language to another (e.g., Google Translate).
3. Chatbots and Virtual Assistants: Understanding and responding to user queries in natural language (e.g., customer support).
4. Text Summarization: Condensing long documents into shorter summaries without losing important information.
5. Sentiment Analysis: Understanding opinions expressed in text, which is useful in social media monitoring, product reviews, and customer feedback analysis.
6. Information Retrieval: Enhancing search engines by interpreting and matching search queries with relevant documents.