Here's a concise yet comprehensive explanation of Natural Language Processing (NLP), including key terminologies, preprocessing steps, and dependencies.
Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that enables machines to understand, interpret, and generate human language. It bridges the gap between human communication (natural language) and machine understanding.
| Term | Description |
|---|---|
| Token | Smallest unit of text (word, subword, or character). |
| Corpus | A large collection of text data used for training or analysis. |
| Stop Words | Common words (like a, the, in) often removed as they add little meaning. |
| Stemming | Reducing words to their root form (e.g., running → run). |
| Lemmatization | More accurate root word extraction using vocabulary and morphology (e.g., better → good). |
| Bag of Words (BoW) | Represents text by word frequency, ignoring grammar and order. |
| TF-IDF | Stands for Term Frequency - Inverse Document Frequency; measures word importance. |
| POS Tagging | Assigning grammatical labels to words (e.g., noun, verb). |
| NER (Named Entity Recognition) | Identifies proper nouns like Person, Organization, Location. |
| Embedding | Vector representation of words (e.g., Word2Vec, GloVe, BERT). |
Text Cleaning
Lowercasing
Removing punctuation, numbers, and special characters
Tokenization
Splitting text into individual words or subwords
Stop Words Removal
Filtering out frequently used but uninformative words
Stemming/Lemmatization
Converting words to their root/base form
Vectorization
Converting text to numerical format using BoW, TF-IDF, or embeddings
POS Tagging / Chunking / NER
Further linguistic parsing for understanding context
| Library | Use Case |
|---|---|
| NLTK | Classical NLP toolkit; tokenization, stemming, POS tagging, etc. |
| spaCy | Fast NLP pipeline with efficient tokenization, NER, POS, and lemmatization |
| TextBlob | Simple API for basic NLP tasks |
| Gensim | Topic modeling and document similarity (e.g., LDA) |
| Scikit-learn | ML models and feature extraction (e.g., TF-IDF vectorizer) |
| transformers (HuggingFace) | Pretrained language models like BERT, GPT, etc. |
| TfidfVectorizer / CountVectorizer | Feature extraction tools from sklearn.feature_extraction.text |
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AI: Artificial intelligence
