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(2024-03-20,
KyleGorman
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---+ Baum-Welch expectation maximization library --- %ICON{led-red}% *Version 0.3.8 is now available for [[BaumWelchDownload][download]].* %ICON{new}% Baum-Welch is now available on [[https://conda-forge.org/][conda-forge]]. Linux (x86) and Mac OS X users who already have [[https://docs.conda.io/en/latest/][conda]] can install the library and all dependencies using the following command: =conda install -c conda-forge baumwelch=. --- _OpenGrm_ _Baum-Welch_ is a C++ library (including associated binaries) which allows the user to estimate the parameters of a discrete hidden Markov model (HMM) using the Baum-Welch algorithm (a special case of the expectation maximization meta-algorithm). It uses [[http://www.openfst.org][OpenFst library]] finite-state transducers (FSTs) and FST archives (FARs) as inputs and outputs. * [[BaumWelchDownload][Download]] * [[BaumWelchDocs][Documentation]] * [[http://opengrm.org/doxygen/baumwelch/html/][Documented source code]] If you use this toolkit in your research, we would appreciate it if you cited at least one of: K. Gorman and C. Allauzen. 2024. [[https://aclanthology.org/2024.eacl-long.43/][A* shortest string decoding for non-idempotent semirings]]. In _Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics_, pages 732-739. (Describes the decoding algorithm.) K. Gorman, C. Kirov, B. Roark, and R. Sproat. 2021. [[https://aclanthology.org/2021.findings-emnlp.85/][Structured abbreviation expansion in context]]. In _Findings of the Association for Computational Linguistics: EMNLP 2021_, pages !995-1005. (Describes the pair n-gram formulation.)
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Topic revision: r18 - 2024-03-20
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KyleGorman
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