What does NLP stand for in the context of computer science?

Natural Language Processing

What is tokenization in NLP?

Tokenization is the process of breaking down text into smaller units called tokens, typically words or subwords.

What is Part-of-Speech (POS) tagging?

POS tagging is the process of assigning grammatical categories (e.g., noun, verb, adjective) to each word in a text.

What is Named Entity Recognition (NER)?

NER is the task of identifying and classifying named entities (e.g., person names, organizations, locations) in text.

What is Sentiment Analysis?

Sentiment Analysis is the process of determining the emotional tone or opinion expressed in a piece of text, typically classifying it as positive, negative, or neutral.

What is a Word Embedding?

A word embedding is a dense vector representation of a word that captures semantic meaning, allowing words with similar meanings to have similar vector representations.

What does LSTM stand for, and what is it used for in NLP?

LSTM stands for Long Short-Term Memory. It's a type of recurrent neural network (RNN) architecture used for processing and predicting time series data, particularly useful for tasks involving sequential data like text.

What is the Transformer architecture in NLP?

The Transformer is a neural network architecture that uses self-attention mechanisms to process sequential data. It has become the foundation for many state-of-the-art NLP models, including BERT and GPT.

What does TF-IDF stand for, and what is it used for?

TF-IDF stands for Term Frequency-Inverse Document Frequency. It's a numerical statistic used to reflect the importance of a word in a document within a collection or corpus, often used in information retrieval and text mining.

What is lemmatization in NLP?

Lemmatization is the process of reducing words to their base or dictionary form (lemma). For example, "running" and "ran" would both be lemmatized to "run".