Semantic Matching of Documents from Heterogeneous Collections: A Simple and Transparent Method for Practical Applications
원문 초록
We present a very simple, unsupervised method for the pairwise matching of documents from heterogeneous collections. We demonstrate our method with the Concept-Project matching task, which is a binary classification task involving pairs of documents from heterogeneous collections. Although our method only employs standard resources without any domain- or task-specific modifications, it clearly outperforms the more complex system of the original authors. In addition, our method is transparent, because it provides explicit information about how a similarity score was computed, and efficient, because it is based on the aggregation of (pre-computable) word-level similarities.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LLSF - Learning Label Specific Features for Multi-Label Classifcation
Inverse Scene Text Removal
EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
PatchDEMUX: A Certifiably Robust Framework for Multi-label Classifiers Against Adversarial Patches
LGAR: Zero-Shot LLM-Guided Neural Ranking for Abstract Screening in Systematic Literature Reviews
Rhapsody: A Dataset for Highlight Detection in Podcasts
토론
이 논문과 연결된 포럼 글이 아직 없습니다.