  Term frequency normalization is a serious issue since lengths of documents
are various. Generally, documents become long due to two different reasons -
verbosity and multi-topicality. First, verbosity means that the same topic is
repeatedly mentioned by terms related to the topic, so that term frequency is
more increased than the well-summarized one. Second, multi-topicality indicates
that a document has a broad discussion of multi-topics, rather than single
topic. Although these document characteristics should be differently handled,
all previous methods of term frequency normalization have ignored these
differences and have used a simplified length-driven approach which decreases
the term frequency by only the length of a document, causing an unreasonable
penalization. To attack this problem, we propose a novel TF normalization
method which is a type of partially-axiomatic approach. We first formulate two
formal constraints that the retrieval model should satisfy for documents having
verbose and multi-topicality characteristic, respectively. Then, we modify
language modeling approaches to better satisfy these two constraints, and
derive novel smoothing methods. Experimental results show that the proposed
method increases significantly the precision for keyword queries, and
substantially improves MAP (Mean Average Precision) for verbose queries.
