maximum marginal relevance原理 最大边际相关性(Maximum Marginal Relevance,简称MMR)原理是一种用于文本摘要和信息检索的方法。它的目标是选择最相关且多样化的文本片段,以提高用户对搜索结果的满意度。 MMR原理基于两个关键概念:相关性(Relevance)和多样性(Diversity)。相关性指的是文本片段与查询的匹配程度,而多样性...
maximum marginal relevance 最大边缘相关 relevance 英['reləvəns] 美[ˈrɛləvəns]n. 相关性，关联; 实用性; [计] 资料检索能力;[网络] 相关性; 关联; 关联性;[例句]Politicians 'private lives have no relevance to their public roles.政...
最大边际相关，或者最大边缘相关
Maximum Marginal Relevance (MMR) Summarization of text is very important in grasping quickly long articles particularly for people who are very busy. In this paper, we use LDA to give topic queries for news articles, which then become inputs to the MMR method. Accordin...
Adaptive maximum marginal relevance based multi-email summarization[C] // proceeding of AICI09, 2- 009.Wang, B., B. Liu, C. Sun, X. Wang, et B. Li (2009). Adaptive maximum marginal relevance based multi-email summarization. In Proceedings of the International Conference on Arti- ficial ...
Winda YulitaYulita W. and Pribadi F. S., "The Implementation of Maximum Marginal Relevance Method on Online National and Local News Portal," Proceeding of International Conference on Green Technology, 2015, Semarang, Indonesia.
The method used for text summarization is Maximum Marginal Relevance (MMR) by combining two selection factors, namely relevance and diversity. It is often found today that news titles in online articles do not fully represent the content of the news or called clickbait, to avoid...
maximal marginal relevance‐based rankingpublic opinionword embeddingVarious online contents on Internet platforms or search engines are related to the corporatereputation. Facing the huge amount of online contents, we need a mining method that canautomatically extract and analyze a large number of ...
For each setting of our search space, the model is trained on the training set and evaluated on the validation set. The configuration with the highest validation accuracy is chosen as the optimal model and further evaluated on the testing set. The validation search space for different parameter ...
Note that if we attempt to use a technique similar to the complete data case here, we will obtain a bound that is governed by the covering number of the log of the marginal feature functions F ( y ) = ∫ z ∈ Z exp ( 〈 λ , f ( y , z ) 〉 ) μ ( d z ) . ...