Papers by Saurabh Sohoney
Pretraining and Finetuning Language Models on Geospatial Networks for Accurate Address Matching (2024.emnlp-industry)
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| Challenge: | Existing approaches to address matching and building authoritative address catalogues are limited in data quality and require labeling effort to develop accurate models. |
| Approach: | They propose to view addresses as an address graph and curate inputs by placing geospatially linked addresses in the same context. |
| Outcome: | The proposed framework improves address matching and fine-tuning language models. |
Learning Geolocations for Cold-Start and Hard-to-Resolve Addresses via Deep Metric Learning (2022.emnlp-industry)
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| Challenge: | Existing systems for learning geolocation fail to cater to a significant fraction of addresses which are new in the system and have inaccurate or missing building level information. |
| Approach: | They propose a framework to resolve addresses to a shallower granularity termed neighbourhood . they propose 'deep metric learning' model to encode geospatial semantics in address embeddings . |
| Outcome: | The proposed framework reduces delivery defects and delivery defects in India and the United Arab Emirates. |