Top NLP Interview Questions (2024)
What is NLP?
What are the main components of NLP?
Explain the term "Bag of Words" in NLP?
What is TF-IDF in NLP?
What are recurrent neural networks (RNNs) and why are they useful in NLP?
What is the difference between stemming and lemmatization in NLP?
What is sequence-to-sequence modeling in NLP?
How does attention improve machine translation in NLP?
What is the difference between supervised and unsupervised learning in NLP?
What is the difference between precision and recall in NLP evaluation?
What are some challenges in NLP?
What is attention mechanism in NLP?
Explain the concept of named entity recognition (NER) in NLP?
What is the purpose of word embeddings?
What are some recent advancements in NLP?
Q: What is NLP?
Ans:
Natural Language Processing is referred to as NLP. It is an area of artificial intelligence that focuses on the use of natural language in communication between machines and people. The goal of NLP is to make it possible for computers to meaningfully read, interpret, and produce human language.
Q: What are the main components of NLP?
Ans:
NLP's primary components include:
- Tokenization is the process of cutting up text into smaller pieces, like words or phrases.
- Understanding word creation and structure using morphology analysis.
- POS tagging: Labelling words with part-of-speech designations.
- Identification and classification of named entities in text is known as named entity recognition (NER).
- Analyzing the grammatical structure of sentences is known as parsing.
- Identifying the sentiment or emotion expressed in a text using sentiment analysis.
- Machine translation: Text that has been translated automatically from one language into another.
- Answering questions: Providing responses to queries based on a context.
Q: Explain the term "Bag of Words" in NLP?
Ans:
A fundamental representation method used in NLP is the Bag of Words (BoW) model. It ignores the arrangement and structure of the words in a document and simply considers how frequently and how frequently certain terms occur. By building a vocabulary from the entire corpus and representing each document as a vector with each dimension corresponding to a word in the vocabulary, it generates a "bag" of words.
Q: What is TF-IDF in NLP?
Ans:
A numerical statistic called TF-IDF (Term Frequency-Inverse Document Frequency) measures the significance of a term in a document within a collection or corpus. It is derived by multiplying the inverse document frequency (IDF), which assesses the rarity of the word throughout the entire corpus, by the term frequency (TF), which gauges the frequency of a word in a document.
Q: What are recurrent neural networks (RNNs) and why are they useful in NLP?
Ans:
A type of neural network design known as recurrent neural networks (RNNs) can process sequential data by maintaining internal memory. Because they can manage variable-length input sequences and identify connections between words in a sentence, they are very helpful in NLP. In applications like language modelling, text generation, and machine translation, RNNs, particularly variations like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), have been successful.
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Q: What is the difference between stemming and lemmatization in NLP?
Ans:
Techniques for text normalisation include stemming and lemmatization. Stemming eliminates grammar and context and reduces words to their stem or root form. Whereas, lemmatization takes into account morphological analysis and the context to reduce words to their simplest or dictionary form. Although lemmatization can be slower than stemming, it typically yields superior results.
Q: What is sequence-to-sequence modeling in NLP?
Ans:
A type of NLP architecture known as sequence-to-sequence (Seq2Seq) modelling maps an input sequence to an output sequence. It is frequently used in activities like dialogue generation, text summarization, and machine translation. An encoder processes the input sequence in Seq2Seq models, while a decoder creates the output sequence based on the encoded representation.
Q: How does attention improve machine translation in NLP?
Ans:
By allowing the model to concentrate on essential parts of the source sentence when creating each word of the target phrase, attention enhances machine translation. Attention enables the model to adaptively attend to various parts of the source phrase at each decoding step rather than relying exclusively on a fixed-length encoded representation of the source sentence. This makes it possible for the model to successfully handle lengthy phrases and identify dependencies, leading to improved translation quality.
Q: What is the difference between supervised and unsupervised learning in NLP?
Ans:
In supervised learning, input data and associated output labels are both provided, and the NLP model gets educated using labelled data. From this labelled data, the model learns how to generalize and make predictions about new, unseen data. Unsupervised learning, in comparison, lacks labelled data. Without explicit instruction, the model discovers patterns and structures on its own from the input data. NLP uses unsupervised learning methods such as word embeddings, topic modelling, and clustering.
Q: What is the difference between precision and recall in NLP evaluation?
Ans:
Metrics like precision and recall are used to gauge how well NLP models perform, especially when it comes to tasks like text categorization, named entity recognition, and information retrieval. Recall is the proportion of correctly identified cases among all of the actual positive occurrences, while precision measures the proportion of correctly identified instances among the instances anticipated to be positive. While recall highlights the coverage or thoroughness of the predictions, precision highlights the accuracy of the predictions.
Q: What are some challenges in NLP?
Ans:
Among the challenges associated with NLP are:
- Ambiguity: Natural language has many words and sentences with numerous meanings, which can make it quite ambiguous.
- Words outside of vocabulary: NLP models frequently struggle with words outside of their training vocabulary.
- Understanding context: Accurately capturing the context and meaning of language is a difficult activity.
- Insufficient training data: Acquiring large labelled datasets for NLP applications can be expensive as well as time-consuming.
- NLP applications involve ethical issues related to privacy, bias, and fairness.
Q: What is attention mechanism in NLP?
Ans:
NLP models use the attention mechanism technique to concentrate on particular input sequence segments when producing an output. Depending on their importance for the current step, it enables the model to give various portions of the input sequence varying weights or priority. Sequence-to-sequence models now perform substantially better in tasks like question answering and machine translation.
Q: Explain the concept of named entity recognition (NER) in NLP?
Ans:
The process of locating and classifying named entities (such as people, groups, places, dates, etc.) in text is known as named entity recognition (NER). To recognise and describe these entities, NER systems often use machine learning techniques like sequence labelling or named entity chunking. Applications like information extraction, chatbots, and question-and-answer systems all depend on NER.
Q: What is the purpose of word embeddings?
Ans:
Dense vector representations of words called word embeddings capture semantic relationships and meanings. In a continuous vector space, related words are placed closer together, and they are used to represent words in that space. Word embeddings give machine learning models the ability to understand the relationships that exist between words in context, which makes them valuable for a variety of NLP applications like sentiment analysis, named entity recognition, and machine translation.
Q: What are some recent advancements in NLP?
Ans:
The latest advances in NLP include:
- Transformer models: State-of-the-art performance in a variety of NLP tasks has been obtained using Transformer architectures, such as the Transformer and its extensions (e.g., BERT, GPT).
- Pretrained language models: Large-scale pretrained language models have been built, enabling for transfer learning and task-specific fine-tuning.
- Multilingual NLP: Attention has been drawn to NLP models that can handle many languages, enabling cross-lingual applications.
- Few-shot and zero-shot learning: Methods have been developed for performing tasks in languages with no labelled data or for training NLP models with a little amount of labelled data.
- Ethics: Addressing ethical issues, such as bias detection and mitigation, fairness, and interpretability in NLP models, is becoming more and more important.