🧠 NLP Interview Questions with Answers (Part 1)
1️⃣ What is Natural Language Processing (NLP)?
👉 NLP is a branch of AI that enables computers to understand, process, analyze, and generate human language.
Applications:
🔹 Chatbots 🤖
🔹 Machine Translation 🌐
🔹 Sentiment Analysis 😊
🔹 Text Summarization 📝
🔹 Speech Recognition 🎙️
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2️⃣ What is Tokenization in NLP?
👉 Tokenization is the process of breaking text into smaller units called tokens, such as words, subwords, or sentences.
Example:
💡 Tokenization is usually one of the first steps in NLP processing.
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3️⃣ What is Stop Word Removal?
👉 Stop words are common words that may carry relatively little useful information for certain NLP tasks.
Examples:
Example:
💡 Stop-word removal is task-dependent and is not always appropriate, especially for modern language models.
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4️⃣ What is Stemming in NLP?
👉 Stemming reduces words to a simpler root-like form, usually by removing prefixes or suffixes.
Example:
💡 Stemming is fast, but the resulting root may not always be a valid dictionary word.
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5️⃣ What is Lemmatization in NLP?
👉 Lemmatization converts a word into its base or dictionary form using linguistic information.
Example:
📌 Stemming → Rule-based word reduction
📌 Lemmatization → Linguistically informed base form
💡 Lemmatization generally produces more meaningful results than stemming, but can require more processing.
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💬 Save this for your NLP interview preparation!
🔥 Next Part will cover 5 important NLP questions on Bag of Words, TF-IDF, N-grams, Word Embeddings & Sentiment Analysis.
#NLP #NaturalLanguageProcessing #AI #ArtificialIntelligence #MachineLearning #NLPInterview #AIInterview #DataScience #Python #InterviewQuestions
1️⃣ What is Natural Language Processing (NLP)?
👉 NLP is a branch of AI that enables computers to understand, process, analyze, and generate human language.
Applications:
🔹 Chatbots 🤖
🔹 Machine Translation 🌐
🔹 Sentiment Analysis 😊
🔹 Text Summarization 📝
🔹 Speech Recognition 🎙️
---
2️⃣ What is Tokenization in NLP?
👉 Tokenization is the process of breaking text into smaller units called tokens, such as words, subwords, or sentences.
Example:
text id="npl8x2"
"I love Machine Learning"
↓
["I", "love", "Machine", "Learning"]
💡 Tokenization is usually one of the first steps in NLP processing.
---
3️⃣ What is Stop Word Removal?
👉 Stop words are common words that may carry relatively little useful information for certain NLP tasks.
Examples:
the, is, a, an, and, of, in
Example:
"The cat is on the table"
↓
"cat table"
💡 Stop-word removal is task-dependent and is not always appropriate, especially for modern language models.
---
4️⃣ What is Stemming in NLP?
👉 Stemming reduces words to a simpler root-like form, usually by removing prefixes or suffixes.
Example:
playing
played
plays
↓
play
💡 Stemming is fast, but the resulting root may not always be a valid dictionary word.
---
5️⃣ What is Lemmatization in NLP?
👉 Lemmatization converts a word into its base or dictionary form using linguistic information.
Example:
running → run
better → good
studies → study
📌 Stemming → Rule-based word reduction
📌 Lemmatization → Linguistically informed base form
💡 Lemmatization generally produces more meaningful results than stemming, but can require more processing.
---
💬 Save this for your NLP interview preparation!
🔥 Next Part will cover 5 important NLP questions on Bag of Words, TF-IDF, N-grams, Word Embeddings & Sentiment Analysis.
#NLP #NaturalLanguageProcessing #AI #ArtificialIntelligence #MachineLearning #NLPInterview #AIInterview #DataScience #Python #InterviewQuestions