# Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model

arXiv: https://arxiv.org/abs/2203.15595 · HTML: https://mlsanity.com/paper/2203.15595/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [S4L: Self-Supervised Semi-Supervised Learning](https://mlsanity.com/paper/1905.03670/) (2019)
- [Leveraging Graph to Improve Abstractive Multi-Document Summarization](https://mlsanity.com/paper/2005.10043/) (2020)
- [Deep Cross-Modal Hashing](https://mlsanity.com/paper/1602.02255/) (2016)
- [Beyond One-hot Encoding: lower dimensional target embedding](https://mlsanity.com/paper/1806.10805/) (2018)
- [Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition](https://mlsanity.com/paper/2103.16019/) (2021)
- [BERT-DST: Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1907.03040/) (2019)
- [Scientific and Technological Information Oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval](https://mlsanity.com/paper/2203.08615/) (2022)
- [Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks](https://mlsanity.com/paper/1909.06627/) (2019)
- [Word Mover's Embedding: From Word2Vec to Document Embedding](https://mlsanity.com/paper/1811.01713/) (2018)
- [An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges](https://mlsanity.com/paper/1704.02223/) (2017)
- [Word2Vec applied to Recommendation: Hyperparameters Matter](https://mlsanity.com/paper/1804.04212/) (2018)
- [Comprehensive Graph-conditional Similarity Preserving Network for Unsupervised Cross-modal Hashing](https://mlsanity.com/paper/2012.13538/) (2020)
- [Deep Tensor CCA for Multi-view Learning](https://mlsanity.com/paper/2005.11914/) (2020)

## Abstract (arXiv)

Science and technology big data contain a lot of cross-media information.There are images and texts in the scientific paper.The s ingle modal search method cannot well meet the needs of scientific researchers.This paper proposes a cross-media scientific research achievements retrieval method based on deep language model (CARDL).It achieves a unified cross-media semantic representation by learning the semantic association between different modal data, and is applied to the generation of text semantic vector of scientific research achievements, and then cross-media retrieval is realized through semantic similarity matching between different modal data.Experimental results show that the proposed CARDL method achieves better cross-modal retrieval performance than existing methods. Key words science and technology big data ; cross-media retrieval; cross-media semantic association learning; deep language model; semantic similarity