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» Aspect Extraction through Semi-Supervised Modeling
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ACL
2012
11 years 7 months ago
Aspect Extraction through Semi-Supervised Modeling
Aspect extraction is a central problem in sentiment analysis. Current methods either extract aspects without categorizing them, or extract and categorize them using unsupervised t...
Arjun Mukherjee, Bing Liu 0001
SDM
2011
SIAM
269views Data Mining» more  SDM 2011»
12 years 7 months ago
Semi-Supervised Convolution Graph Kernels for Relation Extraction
Extracting semantic relations between entities is an important step towards automatic text understanding. In this paper, we propose a novel Semi-supervised Convolution Graph Kerne...
Xia Ning, Yanjun Qi
ICDM
2009
IEEE
233views Data Mining» more  ICDM 2009»
13 years 11 months ago
Semi-Supervised Sequence Labeling with Self-Learned Features
—Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...
LOBJET
2006
196views more  LOBJET 2006»
13 years 4 months ago
Mapping High-Level Business Rules To and Through Aspects
Many object-oriented software applications contain implicit business rules. Although there exist many approaches that advocate the separation of rules, the rules' connections ...
María Agustina Cibrán, Maja D'Hondt,...
AOSD
2007
ACM
13 years 8 months ago
Efficiently mining crosscutting concerns through random walks
Inspired by our past manual aspect mining experiences, this paper describes a random walk model to approximate how crosscutting concerns can be discovered in the absence of domain...
Charles Zhang, Hans-Arno Jacobsen