# Address Matching Based On Hierarchical Information

arXiv: https://arxiv.org/abs/2305.05874 · HTML: https://mlsanity.com/paper/2305.05874/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 10 May 2023.

## Builds on (most cited first, 5 of 5)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [FLAT: Chinese NER Using Flat-Lattice Transformer](https://mlsanity.com/paper/2004.11795/) (2020)
- [Text Matching as Image Recognition](https://mlsanity.com/paper/1602.06359/) (2016)
- [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://mlsanity.com/paper/1909.00204/) (2019)
- [Semantic Modelling with Long-Short-Term Memory for Information Retrieval](https://mlsanity.com/paper/1412.6629/) (2014)

## Abstract (arXiv)

There is evidence that address matching plays a crucial role in many areas such as express delivery, online shopping and so on. Address has a hierarchical structure, in contrast to unstructured texts, which can contribute valuable information for address matching. Based on this idea, this paper proposes a novel method to leverage the hierarchical information in deep learning method that not only improves the ability of existing methods to handle irregular address, but also can pay closer attention to the special part of address. Experimental findings demonstrate that the proposed method improves the current approach by 3.2% points.