termbased

Termbased is an approach in information retrieval and text processing that relies on textual terms as the primary features for representing documents and queries. In a termbased framework, documents are often converted into a bag-of-words or n-gram representation, where each unique term is a dimension and its value reflects frequency or presence. This focus on discrete terms forms the basis for many traditional retrieval and classification tasks.

Common techniques in termbased systems include indexing by terms, term frequency (TF), inverse document frequency (IDF), and TF-IDF weighting, as well as vector space models and boolean retrieval. Lemmatization or stemming and stop-word removal are often employed to normalize terms. Termbased methods can also use term co-occurrence, phrase extraction, and simple keyword matching to measure relevance.

Applications of termbased methods include classic search engines, document retrieval, spam filtering, topic classification, and text clustering. They provide fast, scalable performance and interpretability, especially for large collections. They also serve as a baseline for evaluating more complex models that incorporate semantics or context.

Limitations of termbased approaches include limited handling of synonymy, homographs, and polysemy, as well as vocabulary mismatch between queries and documents. They may overlook semantic relationships and contextual meaning. Hybrid approaches combine termbased signals with semantic indexing, language models, or knowledge graphs to address these gaps. Evaluation typically relies on precision, recall, and F1 on labeled corpora.