Text Representation and Classification
Text representation and classification cover how documents are turned into features and how those representations support organizing collections by category, similarity, and latent topics.
Definition
Text representation and classification is the body of methods for converting documents into feature representations and for assigning, grouping, or projecting those representations, including supervised categorization into known classes, unsupervised clustering, and latent topic or semantic modeling, in service of retrieval and collection organization.
Scope
This area covers the representation of text for retrieval and the unsupervised and supervised organization of document collections: document representation and term weighting, automatic text classification into predefined categories, text clustering into discovered groups, and latent-semantic and topic models that uncover hidden structure. It treats representation and organization as they support information retrieval, drawing on machine learning while focusing on the retrieval-oriented use of these methods rather than general-purpose machine learning theory.
Sub-topics
Core questions
- How are documents converted into features, and how are terms weighted?
- How can documents be automatically sorted into predefined categories?
- How can a collection be grouped into clusters without predefined labels?
- How do latent topic and semantic models reveal hidden structure in text?
- How do these representations improve retrieval, browsing, and filtering?
Key concepts
- document representation
- term weighting (tf-idf)
- text classification / categorization
- text clustering
- latent semantic analysis
- topic models
- feature selection
- vocabulary mismatch
Key theories
- Vector representation and term weighting
- Representing documents as weighted feature vectors, typically over terms with tf-idf-style weights, provides the common substrate on which classification, clustering, and similarity computation all operate.
- Supervised text categorization
- Given labeled examples, machine-learning classifiers can assign documents to predefined categories, with the choice of features and learner determining accuracy, as systematized in the text-categorization literature.
- Latent semantic and topic structure
- Methods such as latent semantic analysis and latent Dirichlet allocation project documents into lower-dimensional spaces or topic distributions, capturing semantic relationships and mitigating vocabulary mismatch.
Clinical relevance
These methods power spam filtering, topic-based routing and filtering, faceted browsing, deduplication, and search-results organization, and topic and semantic models support exploratory search and recommendation. Document representation also underlies the move from sparse term vectors to dense learned embeddings in modern retrieval.
History
Text categorization grew from rule-based systems in the 1980s into a machine-learning discipline through the 1990s, consolidated in Sebastiani's 2002 survey. Latent semantic analysis (1990) introduced dimensionality reduction for retrieval, and latent Dirichlet allocation (2003) established probabilistic topic modeling, both shaping how semantic structure in text is represented.
Key figures
- Fabrizio Sebastiani
- Susan Dumais
- David Blei
- Christopher Manning
Related topics
Seminal works
- manning2008
- sebastiani2002
- deerwester1990
- blei2003
Frequently asked questions
- What is the difference between text classification and text clustering?
- Classification is supervised: it assigns documents to predefined categories using labeled training examples. Clustering is unsupervised: it groups documents by similarity without predefined categories, discovering structure rather than fitting it to known labels.
- Why are latent topic models useful for retrieval?
- Topic and latent-semantic models represent documents by underlying themes rather than exact words, which helps match queries and documents that use different vocabulary for the same concept and supports browsing a collection by topic.