Efficient Algorithms for the Uniform Tokenization Problem

Tokenization (also known as scanning or lexing) is a computational task that has applications in the lexical analysis of programs during compilation and in data extraction and analysis for unstructured or semi-structured data (e.g., data represented using the JSON and CSV data formats). We propose two algorithms for the tokenization problem that have linear time complexity (in the length of the input text) without using large amounts of memory. We also show that an optimized version of one of these algorithms performs well compared to prior approaches on practical tokenization workloads.

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Publication Details

Journal
Proceedings of the ACM on Programming Languages
Published
2025-04-09
DOI
https://doi.org/10.1145/3720498
Citations
2
Primary Topic
Algorithms and Data Compression
Type
article
Field-Weighted Citation Impact
4.96
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article

Efficient Algorithms for the Uniform Tokenization Problem

Konstantinos Mamouras, Wu Angela Li
2 citations
Proceedings of the ACM on Programming Languages
Algorithms and Data Compression
4.96
article

Efficient Algorithms for the Uniform Tokenization Problem

Konstantinos Mamouras, Wu Angela Li
article en
2 citations

Abstract

Tokenization (also known as scanning or lexing) is a computational task that has applications in the lexical analysis of programs during compilation and in data extraction and analysis for unstructured or semi-structured data (e.g., data represented using the JSON and CSV data formats). We propose two algorithms for the tokenization problem that have linear time complexity (in the length of the input text) without using large amounts of memory. We also show that an optimized version of one of these algorithms performs well compared to prior approaches on practical tokenization workloads.

Proceedings of the ACM on Programming LanguagesVol. 9(OOPSLA1)
Rice University (US)
Openalex Percentile: Top 6%
Algorithms and Data Compression
4.96
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