arXiv:2002.12540
UKARA 1.0 Challenge Track 1: Automatic Short-Answer Scoring in Bahasa Indonesia
원문 초록
We describe our third-place solution to the UKARA 1.0 challenge on automated essay scoring. The task consists of a binary classification problem on two datasets | answers from two different questions. We ended up using two different models for the two datasets. For task A, we applied a random forest algorithm on features extracted using unigram with latent semantic analysis (LSA). On the other hand, for task B, we only used logistic regression on TF-IDF features. Our model results in F1 score of 0.812.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Leveraging Cascaded Binary Classification and Multimodal Fusion for Dementia Detection through Spontaneous Speech
Multitask LSTM for Arboviral Outbreak Prediction Using Public Health Data
Data Selection for ERMs
Active Learning for Manifold Gaussian Process Regression
Inverse Scene Text Removal
Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision
토론
이 논문과 연결된 포럼 글이 아직 없습니다.