Please consider implementing SymSpell v6.7. #2
Description
Thank you very much for your fast SymSpell Python port.
Please consider implementing SymSpell v6.7. which improves word segmentation and fixes problems with upper case, ligatures, hyphenation, punctuation, and American English.
https://github.com/wolfgarbe/SymSpell#changes-in-v67
The few changes are all marked in the source code with comment //v6.7
wolfgarbe/SymSpell@c9e4fed#diff-6de4264fac929bae78bbdf35d398e130
Please use the updated version of the frequency dictionary
https://github.com/wolfgarbe/SymSpell/blob/master/SymSpell/frequency_dictionary_en_82_765.txt
Input
AbstractHowdoesauser’spriorexperiencewithdeeplearningimpactaccuracy?Wepresentaninitialstudybasedon31participantswithdifferentlevelsofexperience.Theirtaskistoperformhyperparameteroptimizationforagivendeeplearningarchitecture.There-sultsshowastrongpositivecorrelationbetweentheparticipant’sexperienceandthefinalperformance.Theyadditionallyindicatethatanexperiencedparticipantfindsbettersolu-tionsusingfewerresourcesonaverage.Thedatasuggestsfurthermorethatparticipantswithnopriorexperiencefollowrandomstrategiesintheirpursuitofoptimalhyperpa-rameters.Ourstudyinvestigatesthesubjectivehumanfactorincomparisonsofstateoftheartresultsandscientificreproducibilityindeeplearning.1IntroductionThepopularityofdeeplearninginvariousfieldssuchasimagerecognition[9,19],speech[11,30],bioinformatics[21,24],questionanswering[3]etc.stemsfromtheseeminglyfavorabletrade-offbetweentherecognitionaccuracyandtheiroptimizationburdenlecunetal20attributetheirsuccess
output SymSpell v6.5 WordSegmentation
Abs tract How does a user ’s prior experience with deep learning impact accuracy ?We present an initial study based on 31 participants with different levels of experience .T heir task is to perform hyper parameter optimization for a given deep learning architecture .T here -s ult s show a strong positive correlation between the participant ’s experience and the fin al performance .T hey additionally indicate that an experienced participant finds better sol u-t ions using fewer resources on average .T he data suggests furthermore that participants with no prior experience follow random strategies in their pursuit of optimal hyper pa-ra meters .Our study investigates the subjective human factor in comparisons of state of the art results and sci entific reproducibility in deep learning .1Intro duct ion T he popularity of deep learning in various fi eld s such as image recognition [9,19], speech [11,30], bio informatics [21,24], question answering [3] etc . stems from the seemingly fav or able trade - off between the recognition accuracy and their optimization burden l ecu net al 20 attribute their success
output SymSpell v6.7 WordSegmentation
Abstract How does a user’s prior experience with deep learning impact accuracy? We present an initial study based on 31 participants with different levels of experience. Their task is to perform hyper parameter optimization for a given deep learning architecture. The results show a strong positive correlation between the participant’s experience and the final performance. They additionally indicate that an experienced participant finds better solutions using fewer resources on average. The data suggests furthermore that participants with no prior experience follow random strategies in their pursuit of optimal hyper parameters. Our study investigates the subjective human factor in comparisons of state of the art results and scientific reproducibility in deep learning. 1 Introduction The popularity of deep learning in various fields such as image recognition [9,19], speech [11,30], bio informatics [21,24], question answering [3] etc. stems from the seemingly favorable trade off between the recognition accuracy and their optimization burden l ecu net al 20 attribute their success