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This content will become publicly available on December 15, 2025

Title: Fast Sentence Classification using Word Co-occurrence Graphs
We consider a supervised classification problem of categorizing e-commerce products based on just the words in the title. If done in real-time, the categorization can greatly benefit sellers by enabling them to offer immediate feedback. We present a deterministic algorithm by constructing weighted word co-occurrence graphs from the listing/item titles. We empirically evaluate this algorithm on two publicly available product listing datasets, Etsy and Amazon. Our method’s accuracy is comparable to that of a supervised classifier constructed using the fastText library. The inference time of our model is up to 2.9× faster than the fastText classifier and has small training times. The training and inference of our model scales well for big datasets performing large-scale classification on millions of listings. We perform a detailed analysis and provide insights into our method and the product categorization task.  more » « less
Award ID(s):
1955971
PAR ID:
10593050
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3503-6248-0
Page Range / eLocation ID:
620 to 629
Format(s):
Medium: X
Location:
Washington, DC, USA
Sponsoring Org:
National Science Foundation
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