Top NLP Interview Questions And Answers

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NLP is a subfield of artificial intelligence that focuses on the interaction between computers and humans using natural language. It involves processing, understanding, and generating human language in a way computers can comprehend and respond to.

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Some common challenges in NLP include:

• Ambiguity: Words and phrases can have multiple meanings.
• Named Entity Recognition (NER): Identifying entities like names of people, places, or organizations.
• Sentiment Analysis: Understanding the sentiment expressed in a text (positive, negative, neutral).
• Language Syntax and Semantics: Capturing the intricate structure and meaning of sentences.

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Tokenization is the process of breaking down a text into individual units, typically words or subwords. These units, called tokens, are the building blocks for various NLP tasks like language modeling, text classification, and translation.

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Stop words are common words (e.g., “a,” “an,” “the,” “is”) that are often filtered out during NLP preprocessing. These words are usually removed to reduce noise and focus on the more meaningful words in the text, which can lead to improved efficiency and accuracy in NLP tasks.

Both stemming and lemmatization are techniques used for text normalization:

• Stemming: It involves removing suffixes from words to obtain the root form, known as the stem. The resulting stems may not be actual words.
• Lemmatization: It aims to convert words to their base or dictionary form, called the lemma. Lemmatization results in actual words and often requires a dictionary or lexicon to perform the conversion.

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TF-IDF is a numerical representation used to measure the importance of a term in a document relative to a collection of documents. It considers both the frequency of the term in the document (TF) and the inverse document frequency (IDF) to give higher weights to rare terms that are more informative.

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Word embedding is a technique used to represent words as dense vectors in a continuous vector space. Each word is mapped to a fixed-length vector, and similar words are placed closer to each other in the vector space. Word embeddings are crucial in NLP tasks as they capture semantic relationships and contextual information.

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A language model is a probabilistic model that predicts the likelihood of a sequence of words in a language. It assigns probabilities to sentences, making it useful for tasks like machine translation, speech recognition, and generating human-like text.

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• Supervised learning: It involves training a model on labeled data, where the input and the corresponding output are known. The model learns to make predictions based on the input-output pairs it has seen during training.
• Unsupervised learning: In contrast, unsupervised learning involves training on unlabeled data. The model finds patterns and structures in the data without explicit output labels.

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Attention mechanisms allow models to focus on relevant parts of the input while performing a task. In NLP, the attention mechanism assigns different weights to different words in the input sequence, indicating their importance for the task at hand. This has significantly improved the performance of tasks like machine translation and text summarization.

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Rule-based approaches rely on hand-crafted linguistic rules to process and understand language. In contrast, machine learning-based approaches learn patterns and relationships from data, enabling more adaptive and data-driven NLP models.

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Named Entity Recognition is an NLP task that aims to identify and classify named entities (such as names of people, organizations, locations) in text.

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The bag-of-words model represents text as a collection of individual words, ignoring grammar and word order. It creates a sparse vector representing the frequency of each word in the document.

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Sequence-to-sequence models are used for tasks like machine translation, summarization, and chatbot generation. They consist of two recurrent neural networks – an encoder to process input sequences and a decoder to generate output sequences.

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Word2Vec is a model that learns word embeddings by predicting context words given a target word. GloVe, on the other hand, is a model that learns word embeddings by leveraging the global statistics of the corpus to encode word relationships.

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BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the quality of machine translations by comparing them to one or more human reference translations. It measures the similarity between the generated translation and the references.

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Transfer learning involves training a model on a large dataset and then fine-tuning it on a smaller, task-specific dataset. In NLP, this is commonly done using pre-trained language models like BERT or GPT, which are then fine-tuned for specific tasks like sentiment analysis or question answering.

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RNNs are a class of neural networks designed to process sequential data. They have connections that form loops, allowing them to maintain a hidden state that captures information from previous time steps. They are used in tasks like language modeling, speech recognition, and sentiment analysis.

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GPT (Generative Pre-trained Transformer) is a large-scale language model based on the Transformer architecture. It is trained in an unsupervised manner on a vast corpus of text to predict the next word in a sequence. This pre-training enables it to perform well on a wide range of downstream NLP tasks with fine-tuning.

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BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that uses bidirectional context to generate word embeddings. It revolutionized NLP by achieving state-of-the-art results on various tasks and introducing the concept of transfer learning to NLP.