# A multi-perspective combined recall and rank framework for Chinese procedure terminology normalization

arXiv: https://arxiv.org/abs/2101.09101 · HTML: https://mlsanity.com/paper/2101.09101/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [Enhanced LSTM for Natural Language Inference](https://mlsanity.com/paper/1609.06038/) (2016)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [End-to-End Neural Ad-hoc Ranking with Kernel Pooling](https://mlsanity.com/paper/1706.06613/) (2017)
- [Understanding the Behaviors of BERT in Ranking](https://mlsanity.com/paper/1904.07531/) (2019)
- [BERT-based Ranking for Biomedical Entity Normalization](https://mlsanity.com/paper/1908.03548/) (2019)
- [Simple and Effective Text Matching with Richer Alignment Features](https://mlsanity.com/paper/1908.00300/) (2019)
- [Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN](https://mlsanity.com/paper/1604.04378/) (2016)
- [Medical Entity Linking using Triplet Network](https://mlsanity.com/paper/2012.11164/) (2020)
- [Deep Neural Models for Medical Concept Normalization in User-Generated Texts](https://mlsanity.com/paper/1907.07972/) (2019)

## Abstract (arXiv)

Medical terminology normalization aims to map the clinical mention to terminologies come from a knowledge base, which plays an important role in analyzing Electronic Health Record(EHR) and many downstream tasks. In this paper, we focus on Chinese procedure terminology normalization. The expression of terminologies are various and one medical mention may be linked to multiple terminologies. Previous study explores some methods such as multi-class classification or learning to rank(LTR) to sort the terminologies by literature and semantic information. However, these information is inadequate to find the right terminologies, particularly in multi-implication cases. In this work, we propose a combined recall and rank framework to solve the above problems. This framework is composed of a multi-task candidate generator(MTCG), a keywords attentive ranker(KAR) and a fusion block(FB). MTCG is utilized to predict the mention implication number and recall candidates with semantic similarity. KAR is based on Bert with a keywords attentive mechanism which focuses on keywords such as procedure sites and procedure types. FB merges the similarity come from MTCG and KAR to sort the terminologies from different perspectives. Detailed experimental analysis shows our proposed framework has a remarkable improvement on both performance and efficiency.