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Full-Text Articles in Physical Sciences and Mathematics
Using Agents For Unification Of Information Extraction And Data Mining, Sharjeel Imtiaz, Azmat Hussain, Dr. Sikandar Hiyat
Using Agents For Unification Of Information Extraction And Data Mining, Sharjeel Imtiaz, Azmat Hussain, Dr. Sikandar Hiyat
International Conference on Information and Communication Technologies
Early work for unification of information extraction and data mining is motivational and problem stated work. This paper proposes a solution framework for unification using intelligent agents. A Relation manager agent extracted feature with cross feedback approach and also provide a Unified Undirected graphical handle. An RPM agent an approach to minimize loop back proposes pooling and model utilization with common parameter for both text and entity level abstractions.
Collective Multi-Label Classification, Nadia Ghamrawi, Andrew Mccallum
Collective Multi-Label Classification, Nadia Ghamrawi, Andrew Mccallum
Computer Science Department Faculty Publication Series
Common approaches to multi-label classification learn independent classifiers for each category, and employ ranking or thresholding schemes for classification. Because they do not exploit dependencies between labels, such techniques are only well-suited to problems in which categories are independent. However, in many domains labels are highly interdependent. This paper explores multilabel conditional random field (CRF) classification models that directly parameterize label co-occurrences in multi-label classification. Experiments show that the models outperform their singlelabel counterparts on standard text corpora. Even when multilabels are sparse, the models improve subset classification error by as much as 40%.