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Interpreting TF-IDF term weights as making relevance decisions

2008; Association for Computing Machinery; Volume: 26; Issue: 3 Linguagem: Inglês

10.1145/1361684.1361686

1558-1152

Ho Chung Wu, Robert W. P. Luk, Kam‐Fai Wong, K. L. Kwok,

Topic Modeling

A novel probabilistic retrieval model is presented. It forms a basis to interpret the TF-IDF term weights as making relevance decisions. It simulates the local relevance decision-making for every location of a document, and combines all of these “local” relevance decisions as the “document-wide” relevance decision for the document. The significance of interpreting TF-IDF in this way is the potential to: (1) establish a unifying perspective about information retrieval as relevance decision-making; and (2) develop advanced TF-IDF-related term weights for future elaborate retrieval models. Our novel retrieval model is simplified to a basic ranking formula that directly corresponds to the TF-IDF term weights. In general, we show that the term-frequency factor of the ranking formula can be rendered into different term-frequency factors of existing retrieval systems. In the basic ranking formula, the remaining quantity - log p (r¯| t ∈ d ) is interpreted as the probability of randomly picking a nonrelevant usage (denoted by r¯) of term t . Mathematically, we show that this quantity can be approximated by the inverse document-frequency (IDF). Empirically, we show that this quantity is related to IDF, using four reference TREC ad hoc retrieval data collections.