Physics

Radiology

Relevance (law)

Document retrieval

Mathematics

Term (time)

Biology

Image (mathematics)

World Wide Web

Inverted index

Mathematical analysis

Image retrieval

Political science

Paleontology

Artificial intelligence

Weighting

Information retrieval

Index (typography)

Relevance feedback

Context (archaeology)

Computer science

Quantum mechanics

Search engine indexing

Scheme (mathematics)

Medicine

tf–idf

Bag-of-words document representations play a fundamental role in modern search engines, but their power is limited by the shallow frequency-based term weighting scheme. This paper proposes HDCT, a context-aware document term weighting framework for document indexing and retrieval. It first estimates the semantic importance of a term in the context of each passage. These fine-grained term weights are then aggregated into a document-level bag-of-words representation, which can be stored into a standard inverted index for efficient retrieval. This paper also proposes two approaches that enable training HDCT without relevance labels. Experiments show that an index using HDCT weights significantly improved the retrieval accuracy compared to typical term-frequency and state-of-the-art embedding-based indexes.

Context-Aware Document Term Weighting for Ad-Hoc Search