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Xin Liu
Researcher at Carnegie Mellon University
Publications - 5
Citations - 3331
Xin Liu is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Document classification & Event (computing). The author has an hindex of 5, co-authored 5 publications receiving 3242 citations.
Papers
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Proceedings ArticleDOI
A re-examination of text categorization methods
Yiming Yang,Xin Liu +1 more
TL;DR: The results show that SVM, kNN and LLSF signi cantly outperform NNet and NB when the number of positive training instances per category are small, and that all the methods perform comparably when the categories are over 300 instances.
Journal ArticleDOI
Learning approaches for detecting and tracking news events
TL;DR: The authors extend existing supervised-learning and unsupervised-clustering algorithms to allow document classification based on the information content and temporal aspects of news events to be classified using manually segmented documents.
CMU Report on TDT-2: Segmentation, Detection and Tracking
TL;DR: This paper reports the results achieved by Carnegie Mellon University on the Topic Detection and Tracking Project’s secondyear evaluation for the segmentation, detection, and tracking tasks.
Learning Approaches to Topic Detection and Tracking
Abstract: This paper studies the eeective use of information retrieval and machine learning techniques in a new task, event detection and tracking. The objective is to automatically detect novel events from chronologically-ordered streams of news stories, and track events of interest over time. We extended existing supervised learning and unsupervised clustering algorithms to allow document classiication based on both information content and temporal aspects of events. A task-oriented evaluation was conducted using Reuters and CNN news stories. We found agglomerative document clustering highly eeective (82% in the F 1 measure) for retrospective event detection, and single-pass clustering with time windowing a better choice for on-line alerting of novel events. We also observed robust learning behavior for k-nearest neighbor (kNN) classiication and a decision-tree approach in event tracking, under the diicult condition when the number of positive training examples is extremely small.
CMU Approach to TDT-2: Segmentation, Detection, and Tracking
TL;DR: This paper reports the results achieved by Carnegie Mellon University on the Topic Detection and Tracking Project’s secondyear evaluation for the segmentation, detection, and tracking tasks.