On Short Textual Value Column Representation Using Symbol Level Language Models

Abstract

String-type database columns containing short textual values are crucial for storing and managing a wide range of information in various applications. For example, they store categories, labels, enumerations, code, and abbreviations. Here, we discuss a string column representation using symbol level language models that grasps the symbol level ``distribution'' of the column textual values. These language models are known for their good prediction quality, memory-footprint and runtime efficiency, while being theoretically justified. We focus on a column matching application, and provide empirical indication for their usefulness.

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Cite

Text

Begleiter and Roll. "On Short Textual Value Column Representation Using Symbol Level Language Models." NeurIPS 2024 Workshops: TRL, 2024.

Markdown

[Begleiter and Roll. "On Short Textual Value Column Representation Using Symbol Level Language Models." NeurIPS 2024 Workshops: TRL, 2024.](https://mlanthology.org/neuripsw/2024/begleiter2024neuripsw-short/)

BibTeX

@inproceedings{begleiter2024neuripsw-short,
  title     = {{On Short Textual Value Column Representation Using Symbol Level Language Models}},
  author    = {Begleiter, Ron and Roll, Nathan},
  booktitle = {NeurIPS 2024 Workshops: TRL},
  year      = {2024},
  url       = {https://mlanthology.org/neuripsw/2024/begleiter2024neuripsw-short/}
}