Download all versions of rush JAR files with all dependencies
rush from group edu.utah.bmi.nlp (version 2.0.1.0-jdk11)
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 2.0.1.0-jdk11
Last update 06. August 2024
Tags: 3363244 accurate danielle 63300 hashing accuracy section nested 3363247 wish reliable extremely solution publication annu specifically written text https order knowledge sentence which f005 based using hurdle impact this handle please easy execute version tool clinical eliminates adaptable 1587 effect processing john hash symp full note doing simultaneous rule 3360278 found segmentation reduces 2495498 1479743941616 designed table timestamp allows execution kristina proc mowery efficient harris amia 3362921 defining rush jianlin 3362920 2016 t005 cite growth telegraphic time scopes leverages base
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 2.0.1.0-jdk11
Last update 06. August 2024
Tags: 3363244 accurate danielle 63300 hashing accuracy section nested 3363247 wish reliable extremely solution publication annu specifically written text https order knowledge sentence which f005 based using hurdle impact this handle please easy execute version tool clinical eliminates adaptable 1587 effect processing john hash symp full note doing simultaneous rule 3360278 found segmentation reduces 2495498 1479743941616 designed table timestamp allows execution kristina proc mowery efficient harris amia 3362921 defining rush jianlin 3362920 2016 t005 cite growth telegraphic time scopes leverages base
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 2.0.1.0-jdk8)
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 2.0.1.0-jdk8
Last update 06. August 2024
Tags: 3363244 accurate danielle 63300 hashing accuracy section nested 3363247 wish reliable extremely solution publication annu specifically written text https order knowledge sentence which f005 based using hurdle impact this handle please easy execute version tool clinical eliminates adaptable 1587 effect processing john hash symp full note doing simultaneous rule 3360278 found segmentation reduces 2495498 1479743941616 designed table timestamp allows execution kristina proc mowery efficient harris amia 3362921 defining rush jianlin 3362920 2016 t005 cite growth telegraphic time scopes leverages base
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 2.0.1.0-jdk8
Last update 06. August 2024
Tags: 3363244 accurate danielle 63300 hashing accuracy section nested 3363247 wish reliable extremely solution publication annu specifically written text https order knowledge sentence which f005 based using hurdle impact this handle please easy execute version tool clinical eliminates adaptable 1587 effect processing john hash symp full note doing simultaneous rule 3360278 found segmentation reduces 2495498 1479743941616 designed table timestamp allows execution kristina proc mowery efficient harris amia 3362921 defining rush jianlin 3362920 2016 t005 cite growth telegraphic time scopes leverages base
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 2.0.0.0-jdk11)
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 2.0.0.0-jdk11
Last update 05. February 2024
Tags: 3363244 accurate danielle 63300 hashing accuracy section nested 3363247 wish reliable extremely solution publication annu specifically written text https order knowledge sentence which f005 based using hurdle impact this handle please easy execute version tool clinical eliminates adaptable 1587 effect processing john hash symp full note doing simultaneous rule 3360278 found segmentation reduces 2495498 1479743941616 designed table timestamp allows execution kristina proc mowery efficient harris amia 3362921 defining rush jianlin 3362920 2016 t005 cite growth telegraphic time scopes leverages base
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 2.0.0.0-jdk11
Last update 05. February 2024
Tags: 3363244 accurate danielle 63300 hashing accuracy section nested 3363247 wish reliable extremely solution publication annu specifically written text https order knowledge sentence which f005 based using hurdle impact this handle please easy execute version tool clinical eliminates adaptable 1587 effect processing john hash symp full note doing simultaneous rule 3360278 found segmentation reduces 2495498 1479743941616 designed table timestamp allows execution kristina proc mowery efficient harris amia 3362921 defining rush jianlin 3362920 2016 t005 cite growth telegraphic time scopes leverages base
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 2.0.0.0-jdk1.8)
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 2.0.0.0-jdk1.8
Last update 04. February 2024
Tags: 3363244 accurate danielle 63300 hashing accuracy section nested 3363247 wish reliable extremely solution publication annu specifically written text https order knowledge sentence which f005 based using hurdle impact this handle please easy execute version tool clinical eliminates adaptable 1587 effect processing john hash symp full note doing simultaneous rule 3360278 found segmentation reduces 2495498 1479743941616 designed table timestamp allows execution kristina proc mowery efficient harris amia 3362921 defining rush jianlin 3362920 2016 t005 cite growth telegraphic time scopes leverages base
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 2.0.0.0-jdk1.8
Last update 04. February 2024
Tags: 3363244 accurate danielle 63300 hashing accuracy section nested 3363247 wish reliable extremely solution publication annu specifically written text https order knowledge sentence which f005 based using hurdle impact this handle please easy execute version tool clinical eliminates adaptable 1587 effect processing john hash symp full note doing simultaneous rule 3360278 found segmentation reduces 2495498 1479743941616 designed table timestamp allows execution kristina proc mowery efficient harris amia 3362921 defining rush jianlin 3362920 2016 t005 cite growth telegraphic time scopes leverages base
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 1.4.1.5-jdk1.8)
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 1.4.1.5-jdk1.8
Last update 14. March 2022
Tags: 3363244 accurate danielle 63300 hashing accuracy section nested 3363247 wish reliable extremely solution publication annu specifically written text https order knowledge sentence which f005 based using hurdle impact this handle please easy execute version tool clinical eliminates adaptable 1587 effect processing john hash symp full note doing simultaneous rule 3360278 found segmentation reduces 2495498 1479743941616 designed table timestamp allows execution kristina proc mowery efficient harris amia 3362921 defining rush jianlin 3362920 2016 t005 cite growth telegraphic time scopes leverages base
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 1.4.1.5-jdk1.8
Last update 14. March 2022
Tags: 3363244 accurate danielle 63300 hashing accuracy section nested 3363247 wish reliable extremely solution publication annu specifically written text https order knowledge sentence which f005 based using hurdle impact this handle please easy execute version tool clinical eliminates adaptable 1587 effect processing john hash symp full note doing simultaneous rule 3360278 found segmentation reduces 2495498 1479743941616 designed table timestamp allows execution kristina proc mowery efficient harris amia 3362921 defining rush jianlin 3362920 2016 t005 cite growth telegraphic time scopes leverages base
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 1.3.2.0)
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 1.3.2.0
Last update 16. February 2020
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 1.3.2.0
Last update 16. February 2020
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 1.3.1.9)
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 1.3.1.9
Last update 17. April 2019
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 1.3.1.9
Last update 17. April 2019
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 1.3.1.8)
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 1.3.1.8
Last update 07. August 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 1.3.1.8
Last update 07. August 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 1.3.1.7)
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 1.3.1.7
Last update 11. 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 1.3.1.7
Last update 11. 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 1.3.1.6)
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 1.3.1.6
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 1.3.1.6
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!
Page 1 from 2 (items total 19)
© 2015 - 2025 Weber Informatics LLC | Privacy Policy