NAACL 2025findings0 citations
TeCoFeS: Text Column Featurization using Semantic Analysis
Ananya Singha, Mukul Singh, Ashish Tiwari, Sumit Gulwani, Vu Le, Chris Parnin
Abstract
Extracting insights from text columns can bechallenging and time-intensive. Existing methods for topic modeling and feature extractionare based on syntactic features and often overlook the semantics. We introduce the semantictext column featurization problem, and presenta scalable approach for automatically solvingit. We extract a small sample smartly, use alarge language model (LLM) to label only thesample, and then lift the labeling to the wholecolumn using text embeddings. We evaluateour approach by turning existing text classification benchmarks into semantic categorization benchmarks. Our approach performs better than baselines and naive use of LLMs.
BibTeX
@inproceedings{singha-etal-2025-tecofes,
title = "{T}e{C}o{F}e{S}: Text Column Featurization using Semantic Analysis",
author = "Singha, Ananya and
Singh, Mukul and
Tiwari, Ashish and
Gulwani, Sumit and
Le, Vu and
Parnin, Chris",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.392/",
pages = "7055--7061",
ISBN = "979-8-89176-195-7"
}