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April 1, 20260 citationsOpen Access

OUHCIR at the NTCIR-16 Data Search-2 Task

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SGSatanu GhoshJLJiqun Liu

Key Points

  • The aim is to enhance dataset search methodologies and evaluate ranking effectiveness in information retrieval.
  • Participated in the NTCIR-16 Data Search-2 task focusing on English datasets.
  • Utilized traditional BM25 and modern language models for dataset ranking.
  • Explored the influence of metadata features on ranking performance.
  • Achieved scores of 0.153, 0.161, and 0.174 for nDCG@10, nERR@10, and Q-measure, respectively.
  • Ranked 13th out of 25 submissions across all evaluated metrics.

Abstract

In this paper, we report our work and discuss the results for NTCIR- 16 DataSearch-2 IR subtask. NTCIR-16 Data Search-2 was organized to improve the present knowledge and promote the concepts of dataset search among IR researchers. In this particular subtask, we tried to perform ad-hoc retrieval for datasets based on given queries. While this task was available in English and Japanese, we decided to only compete for the English subtask. We sought to perform the ranking of datasets by using traditional BM25-based ranking functions and recent language models. During the evaluation experiments, we also explored the impact of metadata features on the performance of dataset ranking. Our best performing submission achieved a score of 0.153, 0.161 and 0.174 in nDCG@10, nERR@10, and Q-measure, respectively. In all the metrics, this run was ranked 13th among 25 submitted runs.

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Cite This Study

Ghosh et al. (2022) studied this question.

synapsesocial.com/papers/69cd7b345652765b073a8fd8https://doi.org/10.20736/0002002257
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