DeepLearning - IQ - 6. Natural Language Processing (NLP):

What is tokenization in the context of NLP?

Answer: Tokenization is the process of breaking down a text into smaller units, known as tokens. Tokens can be words, subwords, or characters. Tokenization is a crucial step in NLP for various tasks, allowing the model to process and understand the structure of the text.

Describe the purpose of word embeddings.

Answer: Word embeddings are dense vector representations of words in a continuous vector space. They capture semantic relationships between words, enabling algorithms to understand the context and meaning of words in a more nuanced way compared to traditional one-hot encodings. Popular word embedding methods include Word2Vec and GloVe.

What are Word2Vec and GloVe?

Answer: Word2Vec and GloVe are popular word embedding techniques. Word2Vec uses neural networks to learn vector representations of words based on their context in a given corpus. GloVe (Global Vectors for Word Representation) is another method that constructs word vectors based on the global statistical information of word co-occurrence.

What is the transformer architecture, and how is it used in NLP?

Answer: The transformer architecture is a type of neural network architecture designed for sequence-to-sequence tasks. It relies on self-attention mechanisms to capture long-range dependencies efficiently. Transformers are widely used in NLP tasks, and models like BERT and GPT-3 are built upon the transformer architecture.

Describe the concept of attention in NLP.

Answer: Attention in NLP refers to the mechanism that allows models to selectively focus on different parts of the input sequence when making predictions. It assigns different weights to different positions in the input sequence, enabling the model to attend to the most relevant information.

What is BERT, and how does it improve NLP tasks?

Answer: BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained transformer-based model for natural language understanding. It is bidirectional, meaning it considers both left and right context in each word. BERT has achieved state-of-the-art performance on various NLP tasks by learning contextualized word representations.

What are common challenges in sentiment analysis?

Answer: Common challenges in sentiment analysis include dealing with sarcasm, handling negations, understanding context and tone, handling subjective expressions, and adapting to domain-specific nuances. Sentiment analysis models must be robust enough to handle the complexities of human language and various writing styles.

What is natural language processing (NLP)?

Answer: Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and human language. It involves the development of algorithms and models to enable computers to understand, interpret, and generate human language.

Explain the concept of a recurrent neural network for sequence modeling in NLP.

Answer: Recurrent Neural Networks (RNNs) are used for sequence modeling in NLP to process sequences of data, such as sentences or documents. RNNs maintain hidden states that capture information from previous elements in the sequence, allowing them to model dependencies and context in sequential data.

Explain the bag-of-words model in NLP.

Answer: The bag-of-words model represents a document as an unordered set of words, ignoring grammar and word order but keeping track of word frequency. It creates a "bag" of words, and each document is represented by a vector where each element corresponds to the frequency of a specific word.