Start: 2017/10/30
Finish: 2017/10/??
Very Similar Papers on context of supervised term weight
- 2015, New term weighting schemes with combination of multiple classifier for sentiment analysis PDF READ
- 2007, Supervised and Traditional Term Weighting Methods for Automatic Text Categorization PDF
- 2005, Learn to Weight Terms in Information Retrieval Using Category Information PDF READ
- 2009, Learning term-weighting functions for similarity measures PDF
IDEAs of Application
- some supervised global term weighting can be useful to score term when genre-filtering
ABSTRACT
- Supervised term weighting for text categorization.
- Proposed a new concept of Over-weighting, in context of controlling over-weighting and under-weighting
- over-weighting: assign larger weight to terms with more imbalanced distributions across categories
- under-weighting: ratios between term weights becomes too small. Because sublinear scaling and bias term shrink the ratios
- Proposed a new supervised term weighting scheme, regularized entropy (re)
- Present 3 regularization techniques: add-on smoothing, sublinear scaling, bias term
- Evaluated on a lots of datasets on both tasks of topical classification and sentiment classification
- Other terminology:
- regularization and over-weighting & under-weighting
- scaling functions
- bias term
INTRODUCTION
REVIEW of TERM WEIGHTING SCHEMES
| weight(term, doc) = local_weight_of_term x global_weight_of_term x normalization_factor_of_doc |
- Local term weighting
- Global term weighting
