Research on Cross-media Science and Technology Information Data Retrieval

TL;DR

This paper discusses the development of a cross-media science and technology information retrieval system that leverages deep semantic features to improve upon traditional unimodal keyword matching methods, addressing the needs of researchers in the era of big data.

Contribution

It proposes a novel retrieval system based on deep semantic features tailored for cross-media science and technology data, overcoming limitations of traditional unimodal keyword matching.

Findings

Enhanced retrieval accuracy with deep semantic features

Supports multimodal data retrieval effectively

Addresses the limitations of traditional keyword-based systems

Abstract

Since the era of big data, the Internet has been flooded with all kinds of information. Browsing information through the Internet has become an integral part of people's daily life. Unlike the news data and social data in the Internet, the cross-media technology information data has different characteristics. This data has become an important basis for researchers and scholars to track the current hot spots and explore the future direction of technology development. As the volume of science and technology information data becomes richer, the traditional science and technology information retrieval system, which only supports unimodal data retrieval and uses outdated data keyword matching model, can no longer meet the daily retrieval needs of science and technology scholars. Therefore, in view of the above research background, it is of profound practical significance to study the cross-media science and technology information data retrieval system based on deep semantic features, which is in line with the development trend of domestic and international technologies.