The R2I\_LIS Team Proposes Majority Vote for VarDial's MRC Task
This article presents the model that generated the runs submitted by the R2I{\_}LIS team to the VarDial2019 evaluation campaign, more particularly, to the binary classification by dialect sub-task of the Moldavian vs. Romanian Cross-dialect Topic identification (MRC) task. The team proposed a majority vote-based model, between five supervised machine learning models, trained on forty manually-crafted features. One of the three submitted runs was ranked second at the binary classification sub-task, with a performance of 0.7963, in terms of macro-F1 measure. The other two runs were ranked third and fourth, respectively.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningBinary ClassificationClassificationGeneral ClassificationSimilar Papers 제목 키워드 기반
Findings of the VarDial Evaluation Campaign 2017
We present the results of the VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects, which we organized as part of the fourth edition of the VarDial workshop at EA…
Dependency ParsingDialect IdentificationLanguage IdentificationA Report on the VarDial Evaluation Campaign 2020
This paper presents the results of the VarDial Evaluation Campaign 2020 organized as part of the seventh workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located w…
Dialect IdentificationLanguage IdentificationSTEVENDU2018's system in VarDial 2018: Discriminating between Dutch and Flemish in Subtitles
This paper introduces the submitted system for team STEVENDU2018 during VarDial 2018 Discriminating between Dutch and Flemish in Subtitles(DFS). Post evaluation analyses are also presented, the results obtained indicate …
Language Identification and Morphosyntactic Tagging: The Second VarDial Evaluation Campaign
We present the results and the findings of the Second VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects. The campaign was organized as part of the fifth editio…
Dependency ParsingDialect IdentificationLanguage IdentificationDTeam @ VarDial 2019: Ensemble based on skip-gram and triplet loss neural networks for Moldavian vs. Romanian cross-dialect topic identification
This paper presents the solution proposed by DTeam in the VarDial 2019 Evaluation Campaign for the Moldavian vs. Romanian cross-topic identification task. The solution proposed is a Support Vector Machines (SVM) ensemble…
General ClassificationTriplet