Bag of Words – short Q&A

Answer: Bag-of-words represents a document as an unordered collection of token counts, ignoring word order and syntax while focusing on how many times each vocabulary item appears.

Answer: A document-term matrix is a 2D matrix where rows correspond to documents, columns to vocabulary terms, and each cell stores a count or weight representing that term in that document.

Answer: Most documents contain only a small fraction of the full vocabulary, so the document-term matrix has many zeros, making it high-dimensional and sparse.

Answer: Unigrams, bigrams and trigrams are simply different choices of n-grams as the “terms” in the bag; each n-gram type expands the vocabulary and captures different levels of context.

Answer: BoW ignores word order, so sequences with different meanings but the same set of tokens, like “dog bites man” and “man bites dog”, receive identical or very similar representations.

Answer: Larger vocabularies increase dimensionality and sparsity, potentially improving expressiveness but also raising memory and computation costs and risk of overfitting on small datasets.

Answer: Removing very rare terms reduces dimensionality and noise, since extremely infrequent tokens provide limited generalizable signal but increase sparsity and model complexity.

Answer: BoW features are fed into classifiers like Naive Bayes, logistic regression or SVMs, which learn weights for each term to predict categories such as sentiment or topic labels.

Answer: Term frequency is the raw count or normalized frequency of a token in a document, often used as the base value in the document-term matrix before applying further weighting schemes.

Answer: Using binary indicators (present/absent) instead of counts can be helpful when multiple occurrences add little extra information or when we want to reduce sensitivity to document length.

Answer: Normalizing feature vectors (e.g. to unit length) reduces the bias toward longer documents that naturally have higher raw counts, making comparisons more fair across texts of different lengths.

Answer: As the number of features grows, the data becomes increasingly sparse in high-dimensional space, making it harder for models to generalize without large amounts of labeled training data.

Answer: Feature selection removes uninformative or redundant terms using measures like chi-square, information gain or mutual information, simplifying the model and often improving accuracy.

Answer: TF-IDF starts from BoW term frequencies and reweights them by inverse document frequency to downweight common words and highlight terms that are more discriminative across documents.

Answer: Terms not seen during training are typically mapped to an “unknown” bucket or ignored, meaning they contribute no feature signal to the model for new documents.

Answer: Combining BoW with features like POS tags, lexicon scores or character n-grams can enrich the representation and capture complementary information that pure token counts miss.

Answer: Linear models assign weights to each term feature; the decision is a weighted sum over the bag-of-words vector, making interpretation straightforward via per-term contributions.

Answer: On small or medium-sized datasets with simple tasks like topic or spam classification, BoW with linear models can be strong, cheap baselines that are easier to train and explain than deep models.

Answer: Popular Python libraries like scikit-learn, Gensim and spaCy provide utilities to build vocabulary, compute BoW vectors and integrate them into machine learning pipelines.

Answer: While BoW uses sparse counts, modern models learn dense contextual embeddings; however, BoW ideas about term frequency, sparsity and feature selection still inform how we preprocess and analyze text.