关键词: 语义融合, 信息检索, 稠密检索, 预训练模型, 文本检索

Abstract:

Information Retrieval （IR） is a process that organizes and processes information using specific techniques and methods to meet users’ information needs. In recent years， dense retrieval methods based on pre-trained models have achieved significant success. However， these methods only utilize vector representations of text and words to calculate the relevance between query and document， ignoring the semantic information at the phrase level. To address this issue， an IR method called MSIR （Multi-Scale Information Retrieval） was proposed. IR performance was enhanced by integrating semantic information of different granularities from the query and the document. First， semantic units of three different granularities — word， phrase， and text — were constructed in the query and the document. Then， the pre-trained model was used to encode these three semantic units separately to obtain their semantic representations. Finally， these semantic representations were used to calculate the relevance between the query and the document. Comparison experiments were conducted on three classic datasets of different sizes， including Corvid-19， TREC2019 and Robust04. Compared with ColBERT （ranking model based on Contextualized late interaction over BERT （Bidirectional Encoder Representation from Transformers））， MSIR shows an approximately 8% improvement in the P@10， P@20， NDCG@10 and NDCG@20 indicators on Robust04 dataset， as well as some improvements on Corvid-19 and TREC2019 datasets. Experimental results demonstrate that MSIR can effectively integrate multi-granularity semantic information， thereby enhancing retrieval accuracy.

Key words: semantic fusion, Information Retrieval (IR), dense retrieval, pre-trained model, text retrieval