Year: 2024
Type: conference-paper
Abstract: We introduce string2string, an open-source library that offers a comprehensive suite of efficient algorithms for a broad range of stringto-string problems.It includes traditional algorithmic solutions as well as recent advanced neural approaches to tackle various problems in string alignment, distance measurement, lexical and semantic search, and similarity analysis-along with several helpful visualization tools and metrics to facilitate the interpretation and analysis of these methods.Notable algorithms featured in the library include the Smith-Waterman algorithm for pairwise local alignment, the Hirschberg algorithm for global alignment, the Wagner-Fischer algorithm for edit distance, BARTScore and BERTScore for similarity analysis, the Knuth-Morris-Pratt algorithm for lexical search, and Faiss for semantic search.In addition, it wraps existing efficient and widely-used implementations of certain frameworks and metrics, such as sacre-BLEU and ROUGE.Overall, the library aims to provide extensive coverage and increased flexibility in comparison to existing libraries for strings.It can be used for many downstream applications, tasks, and problems in natural-language processing, bioinformatics, and computational social sciences.It is implemented in Python, easily installable via pip, and accessible through a simple API.Source code, documentation, and tutorials are all available on our GitHub page: https://github.com/ stanfordnlp/string2string.
Authors: Mirac Suzgun, Stuart Shieber, Dan Jurafsky
Institutions: Stanford University, Harvard University
Language: English
Subfield: Theoretical Computer Science
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4402667133