تقرير
MICO: Selective Search with Mutual Information Co-training
| العنوان: | MICO: Selective Search with Mutual Information Co-training |
|---|---|
| المؤلفون: | Wang, Zhanyu, Zhang, Xiao, Yun, Hyokun, Teo, Choon Hui, Chilimbi, Trishul |
| المصدر: | Proceedings of the 29th International Conference on Computational Linguistics (COLING). 2022 |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Information Retrieval, Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups. Selective search is designed to reduce the latency and computation in modern large-scale search systems. In this study, we propose MICO, a Mutual Information CO-training framework for selective search with minimal supervision using the search logs. After training, MICO does not only cluster the documents, but also routes unseen queries to the relevant clusters for efficient retrieval. In our empirical experiments, MICO significantly improves the performance on multiple metrics of selective search and outperforms a number of existing competitive baselines. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2209.04378 |
| رقم الأكسشن: | edsarx.2209.04378 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |