arXiv:2202.03119
Moving Other Way: Exploring Word Mover Distance Extensions
원문 초록
The word mover's distance (WMD) is a popular semantic similarity metric for two texts. This position paper studies several possible extensions of WMD. We experiment with the frequency of words in the corpus as a weighting factor and the geometry of the word vector space. We validate possible extensions of WMD on six document classification datasets. Some proposed extensions show better results in terms of the k-nearest neighbor classification error than WMD.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Position: Mechanistic Interpretability Should Prioritize Feature Consistency in SAEs
FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
ImpliRet: Benchmarking the Implicit Fact Retrieval Challenge
Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models
Trend-Aware Fashion Recommendation with Visual Segmentation and Semantic Similarity
KNN-Defense: Defense against 3D Adversarial Point Clouds using Nearest-Neighbor Search
토론
이 논문과 연결된 포럼 글이 아직 없습니다.