We introduce a new relevance scoring technique that enhances existing relevance scoring schemes with term position information. This technique uses chronological term rank (CTR) which captures the positions of terms as they occur in the sequence of words in a document. CTR is both conceptually and computationally simple when compared to other approaches that use document structure information, such as term proximity, term order and document features. CTR works well when paired with Okapi BM25. We evaluate the performance of various combinations of CTR with Okapi BM25 in order to identify the most effective formula. We then compare the performance of the selected approach against the performance of existing methods such as Okapi BM25, pivoted length normalization and language models. Significant improvements are seen consistently across a variety of TREC data and topic sets, measured by the major retrieval performance metrics. This seems to be the first use of this statistic for relevance scoring. There is likely to be greater retrieval improvements possible using chronological term rank enhanced methods in future work.
['Adam D. Troy', 'Guo-Qiang Zhang']
Enhancing relevance scoring with chronological term rank