Download all versions of rush JAR files with all dependencies
rush from group edu.utah.bmi.nlp (version 1.3.1.5)
RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is
specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table
to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and
eliminates the effect of rule order on accuracy.
If you wish to cite RuSH in a publication, please use:
Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool
Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587.
The full text can be found at:
https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616
This version allows defining section scopes for sentence segmentation.
Artifact rush
Group edu.utah.bmi.nlp
Version 1.3.1.5
Last update 10. May 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
Group edu.utah.bmi.nlp
Version 1.3.1.5
Last update 10. May 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
rush from group edu.utah.bmi.nlp (version 1.3.1.2)
RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is
specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table
to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and
eliminates the effect of rule order on accuracy.
If you wish to cite RuSH in a publication, please use:
Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool
Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587.
The full text can be found at:
https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616
This version allows defining section scopes for sentence segmentation.
Artifact rush
Group edu.utah.bmi.nlp
Version 1.3.1.2
Last update 01. May 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
Group edu.utah.bmi.nlp
Version 1.3.1.2
Last update 01. May 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
rush from group edu.utah.bmi.nlp (version 1.3.1.1)
RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is
specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table
to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and
eliminates the effect of rule order on accuracy.
If you wish to cite RuSH in a publication, please use:
Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool
Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587.
The full text can be found at:
https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616
This version allows defining section scopes for sentence segmentation.
Artifact rush
Group edu.utah.bmi.nlp
Version 1.3.1.1
Last update 30. April 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
Group edu.utah.bmi.nlp
Version 1.3.1.1
Last update 30. April 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
rush from group edu.utah.bmi.nlp (version 1.3.1)
RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is
specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table
to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and
eliminates the effect of rule order on accuracy.
If you wish to cite RuSH in a publication, please use:
Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool
Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587.
The full text can be found at:
https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616
This version allows defining section scopes for sentence segmentation.
Artifact rush
Group edu.utah.bmi.nlp
Version 1.3.1
Last update 30. April 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
Group edu.utah.bmi.nlp
Version 1.3.1
Last update 30. April 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
rush from group edu.utah.bmi.nlp (version 1.3.0)
RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is
specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table
to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and
eliminates the effect of rule order on accuracy.
If you wish to cite RuSH in a publication, please use:
Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool
Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587.
The full text can be found at:
https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616
This version allows defining section scopes for sentence segmentation.
Artifact rush
Group edu.utah.bmi.nlp
Version 1.3.0
Last update 25. March 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
Group edu.utah.bmi.nlp
Version 1.3.0
Last update 25. March 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
rush from group edu.utah.bmi.nlp (version 1.1.1)
RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is
specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table
to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and
eliminates the effect of rule order on accuracy.
If you wish to cite RuSH in a publication, please use:
Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool
Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587.
The full text can be found at:
https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616
This version allows defining section scopes for sentence segmentation.
Artifact rush
Group edu.utah.bmi.nlp
Version 1.1.1
Last update 24. March 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
Group edu.utah.bmi.nlp
Version 1.1.1
Last update 24. March 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
rush from group edu.utah.bmi.nlp (version 3.0)
RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is
specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table
to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and
eliminates the effect of rule order on accuracy.
If you wish to cite RuSH in a publication, please use:
Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool
Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587.
The full text can be found at:
https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616
This version allows defining section scopes for sentence segmentation.
Artifact rush
Group edu.utah.bmi.nlp
Version 3.0
Last update 10. February 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
Group edu.utah.bmi.nlp
Version 3.0
Last update 10. February 2018
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
rush from group edu.utah.bmi.nlp (version 2.0)
RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is
specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table
to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and
eliminates the effect of rule order on accuracy.
If you wish to cite RuSH in a publication, please use:
Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool
Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587.
The full text can be found at:
https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616
This version allows defining section scopes for sentence segmentation.
Artifact rush
Group edu.utah.bmi.nlp
Version 2.0
Last update 13. December 2017
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
Group edu.utah.bmi.nlp
Version 2.0
Last update 13. December 2017
Tags: reliable using handle nested wish tool segmentation mowery impact scopes 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages this efficient knowledge symp harris john sentence solution version execution timestamp hashing designed order accurate easy full allows rule written clinical note proc https reduces extremely defining rush simultaneous f005 3363247 kristina jianlin 3363244 annu section publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
rush from group edu.utah.bmi.nlp (version 1.0)
RuSH is an efficient, reliable, and easy adaptable rule-based sentence segmentation solution. It is
specifically designed to handle the telegraphic written text in clinical note. It leverages a nested hash table
to execute simultaneous rule processing, which reduces the impact of the rule-base growth on execution time and
eliminates the effect of rule order on accuracy.
If you wish to cite RuSH in a publication, please use:
Jianlin Shi ; Danielle Mowery ; Kristina M. Doing-Harris ; John F. Hurdle.RuSH: a Rule-based Segmentation Tool
Using Hashing for Extremely Accurate Sentence Segmentation of Clinical Text. AMIA Annu Symp Proc. 2016: 1587.
The full text can be found at:
https://knowledge.amia.org/amia-63300-1.3360278/t005-1.3362920/f005-1.3362921/2495498-1.3363244/2495498-1.3363247?timeStamp=1479743941616
This version allows defining section scopes for sentence segmentation.
Artifact rush
Group edu.utah.bmi.nlp
Version 1.0
Last update 26. April 2017
Tags: reliable using handle nested wish tool segmentation mowery impact 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages efficient knowledge symp harris john sentence solution execution timestamp hashing designed accurate easy order full rule written clinical note proc https reduces extremely rush simultaneous f005 3363247 kristina jianlin 3363244 annu publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
Group edu.utah.bmi.nlp
Version 1.0
Last update 26. April 2017
Tags: reliable using handle nested wish tool segmentation mowery impact 3362920 63300 time please danielle 3362921 adaptable t005 processing hurdle 1479743941616 text execute hash 2016 table 1587 amia eliminates cite telegraphic doing 3360278 leverages efficient knowledge symp harris john sentence solution execution timestamp hashing designed accurate easy order full rule written clinical note proc https reduces extremely rush simultaneous f005 3363247 kristina jianlin 3363244 annu publication which effect base accuracy based 2495498 growth specifically found
Organization The Department of Biomedical Informatics, University of Utah
URL https://github.com/jianlins/RuSH
License The Apache Software License, Version 2
Dependencies amount 3
Dependencies nlp-core, fastner, junit-repeat-rule,
There are maybe transitive dependencies!
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