NLP – Natural Language Processing

NLP – What Is It / What Does It Do? 

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on enabling computers to understand, process, and generate human language (natural language). NLP sits at the intersection of linguistics, computer science, and artificial intelligence, allowing for the analysis and understanding of written or spoken language.
The primary goal of NLP is to enable computers to understand natural languages used by humans and interact with them. This involves aspects such as the structural rules of language (syntax), meaning (semantics), and context (pragmatics).

The following are the key areas that support the main goal of NLP:

• Large-Scale Text and Data Analysis
• Sentiment Analysis
• SEO
• Automatic Translation
• Chatbots and Virtual Assistants
• Grammar and Spell Checking

NLP –  Python 

In Python, NLP refers to the artificial intelligence techniques used to enable computers to understand, analyze, and process human language. Python is one of the most popular tools for NLP tasks due to its powerful infrastructure.
NLP deals with the logic of understanding text and spoken language used by humans. For example, tasks like sentiment analysis (determining if a text is positive or negative), translation, text summarization, or text classification are the basic elements that make up NLP.

Some of the Python libraries that support these projects and will be discussed in this article are listed below:

• NLTK
• spaCy
• Hugging Face Transformers

NLP – NLTK

 NLTK (Natural Language Toolkit) is a popular and powerful Python library used for natural language processing. Released in 2001, it is widely used in academic research, language modeling, and related fields.

NLTK provides tools and functions for practical NLP applications, such as text mining, tokenization, and stemming. While NLP represents the theoretical side, NLTK can be seen as a software tool that brings that theory to life in practical applications. In short, NLP covers the theory, while NLTK applies it in practice.

NLTK is commonly used in:

• Text Preprocessing
• Syntactic Analysis
• Text Classification
• Sentiment Analysis
• Translation and Summarization
• Named Entity and Sentence Structure Analysis

Google Translate and the Role of NLP

NLTK provides analysis based on traditional NLP techniques such as tokenization, lemmatization, and POS tagging, which are also used in Google Translate. However, compared to Google Translate’s deep learning models, NLTK may be more limited as it focuses on classic language processing techniques. In translation systems, deep learning models are typically used to process the translation, as these systems update with new data via a feedback loop.

NLTK Code Example – Text Preprocessing + Tokenization + Google Translate Translation

import nltk

from nltk.tokenize import word_tokenize

from googletrans import Translator

# Download NLTK data sets

nltk.download('punkt')

# Sentence to translate

sentence = "Natural Language Processing is an interesting field of study."

# Tokenization with NLTK

tokens = word_tokenize(sentence)

print("Tokenized Sentence:", tokens)

# Translate using Google Translate

translator = Translator()

translated = translator.translate(sentence, src='en', dest='de')

# Print result

print("Translated Sentence (German):", translated.text)

OUTPUT:

Tokenized Sentence: [‘Natural’, ‘Language’, ‘Processing’, ‘is’, ‘an’, ‘interesting’, ‘field’, ‘of’, ‘study’, ‘.’]

Translated Sentence (German): Natural Language Processing ist ein interessantes Studienfeld.

NLP – spaCy

 spaCy is a Python library known for analyzing text quickly and efficiently in NLP. It performs tasks such as tokenization, POS tagging, lemmatization, named entity recognition (NER), and dependency parsing with better performance compared to other libraries.

spaCy is designed for high performance and scalability with large data sets, making it well-suited for production environments. It works seamlessly with deep learning models, allowing it to be used in a variety of fields such as chatbots, sentiment analysis, text mining, and information extraction.

What distinguishes it from NLTK is that spaCy is optimized for production environments and better understands texts through embedded word vectors. NLTK is more flexible and commonly used in academic research or learning contexts.

spaCy Code Example – Keyword Targeting and Named Entity Recognition

import spacy

from collections import Counter

from spacy.lang.en.stop_words import STOP_WORDS

# Load spaCy’s transformer-based model

nlp = spacy.load("en_core_web_trf")

# Example text

text = “””

Tesla, an American electric vehicle and clean energy company, has revolutionized the car industry. 

Founded by Elon Musk in 2003, the company designs electric vehicles, solar products, and energy storage solutions.

Tesla’s electric cars, such as the Model S and Model X, have set new standards in terms of innovation, performance, and environmental sustainability.

“””

# Process the text

doc = nlp(text)

# Remove stopwords and punctuation to identify key terms

words = [token.text.lower() for token in doc if token.is_alpha and token.text.lower() not in STOP_WORDS]

# Calculate frequency of keywords

keyword_freq = Counter(words)

# Get the top 5 most frequent keywords

top_keywords = keyword_freq.most_common(5)

# Extract Named Entity Recognition (NER) results

entities = [(ent.text, ent.label_) for ent in doc.ents]

# Print results

print("\n **Keyword Frequency (Auto-Identified):**")

for keyword, count in top_keywords:

    print(f"{keyword}: {count}")

print("\n **Named Entity Recognition (NER) Results:**")

for entity, label in entities:

    print(f"{entity} - {label}")

OUTPUT:

**Keyword Frequency (Auto-Identified):**

tesla: 2

electric: 2

model: 2

energy: 2

car: 2

 **Named Entity Recognition (NER) Results:**

Tesla – ORG

American – NORP

Elon Musk – PERSON

2003 – DATE

Model S – PRODUCT

Model X – PRODUCT

NLP – Hugging Face Transformers 

Hugging Face Transformers is based on the deep learning Transformer architecture and includes popular pre-trained models such as BERT, GPT, T5, RoBERTa, and DistilBERT. These models work seamlessly with PyTorch and TensorFlow.
This library is widely used both in academia and industry and offers ready-to-use functions via a pipeline, making it easier to work with powerful NLP models with just a few lines of code. Additionally, the Tokenizer component helps to process texts in a format understood by models, speeding up the workflow.

BERT Pre-Trained Model Example – Text Meaning Extraction

from transformers import BertTokenizer, BertForMaskedLM

import torch

# Load model and tokenizer

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

model = BertForMaskedLM.from_pretrained('bert-base-uncased')

# Masked sentence

text = "Hugging Face is a [MASK] platform."

# Tokenize the sentence

inputs = tokenizer(text, return_tensors="pt")

# Predict the masked word

with torch.no_grad():

    logits = model(**inputs).logits

# Resolve the masked word

mask_token_index = torch.where(inputs.input_ids == tokenizer.mask_token_id)[1]

predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)

predicted_token = tokenizer.decode(predicted_token_id)

print(f"Predicted masked word: {predicted_token}")

OUTPUT (Multiple possible results):

Predicted masked word: great

Predicted masked word: powerful

.

.

.

NLP and SEO

SEO and NLP are two fields based on content and language understanding. While SEO uses keywords to tell search engines what a piece of content is about, NLP helps make these contents more understandable.
Nowadays, search engines do not focus solely on keywords; they also evaluate the context and meaning relationships within texts.

Google’s BERT and RankBrain algorithms play a crucial role in this context. With these systems, Google tries to understand the user’s intent, rather than just analyzing individual words. It’s no longer enough to have specific keywords; the content must truly address the user’s query in a meaningful way.

Since 2018, Google has used the BERT algorithm to ensure content is written in natural language and answers user questions in detail. Behind the scenes, when a user submits a query, Google analyzes the context of the words in the search using this technique, essentially performing semantic search.
Google also utilizes this technique in Snippets by summarizing content in bullet points or short paragraphs and displaying it directly in the search results. SEO is no longer just about keyword games; content must add real value to users.