# Named Entity Normalization Model Using Edge Weight Updating Neural Network: Assimilation Between Knowledge-Driven Graph and Data-Driven Graph

arXiv: https://arxiv.org/abs/2106.07549 · HTML: https://mlsanity.com/paper/2106.07549/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 52 references that are in the map.
- First posted on arXiv 14 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Edge-labeling Graph Neural Network for Few-shot Learning](https://mlsanity.com/paper/1905.01436/) (2019)
- [Biomedical Entity Representations with Synonym Marginalization](https://mlsanity.com/paper/2005.00239/) (2020)
- [Enhancing Clinical Concept Extraction with Contextual Embeddings](https://mlsanity.com/paper/1902.08691/) (2019)
- [BERT-based Ranking for Biomedical Entity Normalization](https://mlsanity.com/paper/1908.03548/) (2019)
- [Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models](https://mlsanity.com/paper/2008.05190/) (2020)
- [Medical Entity Linking using Triplet Network](https://mlsanity.com/paper/2012.11164/) (2020)
- [Siamese Graph Neural Networks for Data Integration](https://mlsanity.com/paper/2001.06543/) (2020)
- [Matching Article Pairs with Graphical Decomposition and Convolutions](https://mlsanity.com/paper/1802.07459/) (2018)

## Abstract (arXiv)

Discriminating the matched named entity pairs or identifying the entities' canonical forms are critical in text mining tasks. More precise named entity normalization in text mining will benefit other subsequent text analytic applications. We built the named entity normalization model with a novel Edge Weight Updating Neural Network. Our proposed model when tested on four different datasets achieved state-of-the-art results. We, next, verify our model's performance on NCBI Disease, BC5CDR Disease, and BC5CDR Chemical databases, which are widely used named entity normalization datasets in the bioinformatics field. We also tested our model with our own financial named entity normalization dataset to validate the efficacy for more general applications. Using the constructed dataset, we differentiate named entity pairs. Our model achieved the highest named entity normalization performances in terms of various evaluation metrics.