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Deep Learning in NLP
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Recurrent Neural Networks (RNNs)
A class of neural networks for processing sequential data. Common use cases include language modeling and text generation.
Long Short-Term Memory (LSTM)
An advanced RNN that can learn long-term dependencies. It's widely used in machine translation and speech recognition.
Gated Recurrent Units (GRUs)
Simplified version of LSTM with fewer parameters. Commonly used for sequence modeling like LSTMs.
Convolutional Neural Networks (CNNs) for NLP
Traditionally used for image processing, CNNs are used for NLP tasks such as sentence classification and feature extraction.
Word Embeddings
A representation of text where words with similar meaning have a similar representation. Used in almost all NLP tasks.
Transformer Model
A neural model that relies on self-attention mechanisms. It has led to breakthroughs in translation, summarization, and question-answering.
Self-Attention Mechanism
Part of the Transformer model, it allows the model to weigh the importance of different parts of the input data.
Bidirectional Encoder Representations from Transformers (BERT)
A Transformer-based model designed to understand the context of words in search queries and other text. Commonly used for NLP tasks like entity recognition.
Generative Pretrained Transformer (GPT)
An autoregressive language model that uses transformer networks. It's used for text generation, translation, and summarization.
Sequence-to-Sequence (Seq2Seq) Models
Models that transform a given sequence of elements in one domain to another sequence. Widely used in machine translation.
Attention Mechanisms
Components that allow models to focus on specific parts of the input sequence, important for tasks such as translation and text summarization.
Neural Machine Translation (NMT)
A type of machine translation that relies on neural networks, particularly Seq2Seq models, to translate text.
Named Entity Recognition (NER)
A task of identifying and classifying key elements from text into predefined categories. Often uses LSTM-CRF models.
Conditional Random Fields (CRF)
A statistical modeling method used in NLP for structured prediction. In deep learning, it's combined with LSTMs for tasks like NER.
Question Answering (QA)
A complex NLP task where systems automatically answer questions posed in natural language, often requiring deep learning-based models.
Text Classification
The task of assigning labels to text documents. Frequent uses of CNNs and RNNs are seen alongside algorithms like fastText.
Sentiment Analysis
A method used to determine the sentiment expressed in text, ranging from RNNs to Transformer models for better context understanding.
Language Modeling
The task of predicting the next word or character in a sequence using statistical or neural methods, with GPT being a notable example.
Transfer Learning in NLP
A technique where a model pre-trained on a large dataset is fine-tuned for a specific NLP task, improving performance significantly.
Text Summarization
Creating a short, accurate, and fluent summary of a long text. Sequence-to-sequence models with attention are typically used.
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