Bag-of-words model
A bag-of-words model represents text using counts, presence indicators, or weights for its words or tokens while disregarding their order and syntactic relationships.
Natural Language Processing in Action, Second Edition
Bag-of-words is a text representation that records word occurrences while disregarding the order and syntactic relationships of the words.
- How it is used
- Represents text numerically for document retrieval and NLP tasks such as search, spam filtering, and sentiment analysis.
- How deep it goes
- Works through tokenization, binary vectors, counts, and normalized frequencies, while deferring richer weighting and word relationships to other models.
- Examples
- Counts tokens in a sentence about algorithmic bias and contrasts greeting permutations with an order-sensitive search query.
- What you can do after
- Build it from scratch with token counts, inspect frequencies, and recognize when discarded word order makes the representation unsuitable.
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Deep Learning with Python, Third Edition
A text model that ignores token order and represents a document by which words occur in it, often with per-word weights.
- How it is used
- Supports lightweight text classification by converting documents into unordered vocabulary features.
- How deep it goes
- Defines token sets, multi-hot vectors, learned weights, and a linear classifier, but stops short of modeling word order.
- Examples
- Uses a short movie sentence and IMDb sentiment reviews, including a 20,000-term vector and an approximately 88% test accuracy.
- What you can do after
- Build it from scratch with TextVectorization and a linear classifier for lightweight sentiment prediction.
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Data Without Labels
Bag-of-words is a text representation that uses word occurrences or frequencies as features while disregarding word order and context.
- How it is used
- Represents text quickly as frequency vectors for document classification and other text-analysis tasks.
- How deep it goes
- Explains tokenization, vocabulary creation, frequency vectors, and thresholds, but stops short of implementation code and contextual modeling.
- Examples
- Uses two short sentences to demonstrate token frequencies, vocabulary-sized vectors, and zero values for absent words.
- What you can do after
- Apply the model to convert sentences or documents into frequency vectors for fast document classification.
Machine Learning System Design
A bag-of-words representation describes text using counts or indicators of its words while disregarding word order.
- How it is used
- Transforms sequential data such as text into features that machine-learning algorithms can process.
- How deep it goes
- Defines the representation and its word-order limitation, then points to longer N-grams and alternatives rather than implementation details.
- Examples
- Uses text as the input sequence and contrasts single-word unigrams with longer N-grams.
- What you can do after
- Apply it to transform text into machine-learning features while recognizing the loss of word-order information.
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Deep Learning with R, Third Edition
A bag-of-words model represents text as an unordered collection of tokens and assigns learned weights to token presence indicators.
- How it is used
- Deploys unordered token indicators as a simple baseline for movie-review sentiment classification.
- How deep it goes
- Explains tokenization, set conversion, multi-hot encoding, and learned word weights, but stops short of detailed model architecture.
- Examples
- Uses a toy review about crying, vocabulary IDs, an unordered token set, and a six-position multi-hot vector.
- What you can do after
- Apply it via tokenization and multi-hot encoding to create fixed-size inputs for a text-classification model.
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Related concepts
- sequence modelcontrasts with · A sequence model processes ordered data while accounting for dependencies among its positions to produce predictions or representations.
- n-gram modeladdresses · An n-gram model is a probabilistic language model that estimates sequence probabilities using contiguous sequences of a fixed number of tokens, typically to predict a token from preceding context.
- latent Dirichlet allocationused by · Latent Dirichlet allocation is a generative probabilistic topic model representing documents as mixtures of latent topics and topics as distributions over words.
- parameteruses · A model parameter is a numerical value, such as a weight or bias, that is learned or set to determine how a model computes outputs.
- term frequency-inverse document frequencycontrasts with · Term frequency-inverse document frequency is a text-weighting scheme that combines a term’s frequency in a document with its inverse frequency across a document collection.
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