NLP Basics Interview Questions & Answers

1. Tokenization in NLP:

Tokenization is the process of breaking down a sequence of text into smaller units called tokens. These tokens can be words, subwords, characters, or other meaningful units depending on the task and language. Tokenization is important because it is a fundamental step in most NLP pipelines, as it prepares the text data for further processing and analysis.

1.1. Challenges that can arise during tokenization include:

• Handling multi-word expressions, idioms, or compound words.
• Dealing with punctuation, special characters, and non-textual elements.
• Accounting for different writing systems and character encodings.
• Addressing ambiguities in word boundaries, especially in languages without explicit word delimiters.

2. Handling bias in NLP models:

Bias in NLP models can arise from various sources, such as biased training data, skewed representations, or algorithmic biases. Techniques to mitigate bias include:

• Debiasing word embeddings by projecting them onto a subspace orthogonal to the bias subspace.
• Data augmentation and reweighting to balance the training data distribution.
• Adversarial training, where a discriminator is trained to identify and remove biases from the model’s representations.
• Incorporating explicit bias constraints or regularization terms during model training.
• Evaluating models for bias using curated test sets and applying bias mitigation techniques as needed.

3. Evaluation metrics for NLP models:

Common evaluation metrics for NLP tasks include:

• Text classification: Accuracy, precision, recall, F1-score, ROC-AUC.
• Machine translation: BLEU score, METEOR, chrF, and human evaluation.
• Language modelling: Perplexity, cross-entropy loss.
• Text summarization: ROUGE scores (ROUGE-N, ROUGE-L), BERTScore.
• Named entity recognition: F1-score, precision, and recall for each entity type.
• The choice of metric depends on the specific task and the trade-offs between different aspects of performance (e.g., precision vs. recall).

4. Word embedding:

Word embedding are dense vector representations of words, where words with similar meanings or contexts are mapped to similar vectors in a high-dimensional space. Word embedding are learned from large text corpora using models like Word2Vec (Skip-gram and CBOW) and GloVe. These embedding capture semantic and syntactic relationships between words, enabling NLP models to reason about word similarities and analogies. Word embedding are widely used as input features for many NLP tasks.

5. Transfer learning in NLP:

Transfer learning involves taking a pre-trained model on a large corpus and fine-tuning it on a specific task or domain. This approach has been highly successful in NLP, as it allows models to leverage the knowledge learned from vast amounts of text data, reducing the need for task-specific labeled data. Popular transfer learning models include BERT, GPT, RoBERTa, and XLNet. Transfer learning is important for building effective NLP models, especially in low-resource scenarios or for tasks with limited labeled data.

6. Handling out-of-vocabulary (OOV) words:

OOV words are words that are not present in the model’s vocabulary or training data. Techniques to handle OOV words include:

Subword tokenization: Breaking words into subword units (e.g., character n-grams, byte-pair encoding) to represent OOV words.
Using a special “UNK” token to represent all OOV words.
Copying or copying-and-generating mechanisms for tasks like machine translation and text summarization.
Incorporating character-level or hybrid word-character models to better handle OOV words.

7. Choosing model architecture and size:

The choice of model architecture and size depends on various factors, including:

• The complexity and requirements of the NLP task.
• The amount of available training data and computational resources.
• Trade-offs between model capacity, training time, and inference time.
• Domain-specific considerations or constraints (e.g., real-time inference, memory footprint).
• Generally, larger models with more parameters tend to perform better on complex tasks with abundant data, while smaller models may be preferred for resource-constrained scenarios or simpler tasks.

8. Supervised, unsupervised, and semi-supervised learning in NLP:

• Supervised learning: Models are trained on labeled data (e.g., text classification, machine translation). Supervised learning is used when labeled data is available and the task is well-defined.
• Unsupervised learning: Models are trained on unlabeled data to discover patterns and structures (e.g., topic modeling, word embeddings). Unsupervised learning is used when labeled data is scarce or the goal is to uncover hidden representations or structures in the data.
• Semi-supervised learning: Models are trained on a combination of labeled and unlabeled data, leveraging the strengths of both approaches. This is useful when labeled data is limited but unlabeled data is abundant.

9. Text data preprocessing and cleaning:

Common text preprocessing and cleaning techniques include:

• Lowercasing or casing normalization.
• Removing punctuation, digits, or special characters.
• Tokenization and sentence segmentation.
• Stop word removal.
• Stemming or lemmatization.
• Handling contractions and abbreviations.
• Normalizing text representations (e.g., unicode normalization, byte-pair encoding).
• Handling HTML/XML tags, URLs, emoticons, or other non-textual elements.

10. NLP libraries and frameworks:

Popular libraries and frameworks for NLP tasks include:

• NLTK (Natural Language Toolkit): A Python library for various NLP tasks, including tokenization, stemming, tagging, parsing, and semantic reasoning.
• spaCy: A Python library for advanced NLP tasks like named entity recognition, part-of-speech tagging, and dependency parsing.
• Hugging Face Transformers: A Python library providing pre-trained models and tools for transformer-based NLP tasks like text classification, question answering, and language generation.
• TensorFlow Text and Keras Text: TensorFlow’s library for text processing and NLP tasks.
• PyTorch Text: PyTorch’s library for NLP tasks, including data utilities and model implementations.
• AllenNLP: An open-source NLP research library built on PyTorch.

11. Sequence-to-sequence models in NLP:

Sequence-to-sequence models are a class of neural network architectures designed to handle tasks where the input and output are sequences of varying lengths. These models use an encoder to process the input sequence and a decoder to generate the output sequence. Sequence-to-sequence models are widely used in tasks like machine translation, text summarization, dialogue systems, and image/video captioning. Popular architectures include recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformer-based models like the Transformer and BERT.

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