A Boosting Algorithm for Classification of Semi-Structured Text

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How researchers see this paper

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Text Categorization as a Graph Classification Problem (2015, methodology): “The works of Jiang et al. (2010) and Arora et al. (2010) are also related but their representations are different and closer to parse and dependency trees used as base features for text categorization by Kudo and Matsumoto (2004) and Matsumoto et al. (2005).”
Approaches, Tools and Applications for Sentiment Analysis Implementation (2015, methodology): “While the study provided by [29] used the sentence level classification approach.”
Assigning Polarity Scores to Reviews Using Machine Learning Techniques (2005, background): “Kudo [8] developed decision stumps, which can capture substructures embedded in text (such as word-based dependency), and suggested that subtree features are important for opinion/modality classification.”
Dependency-based Convolutional Neural Networks for Sentence Embedding (2015, background): “…are consecutive on the surface string and neglect longdistance dependencies, while the latter play an important role in many linguistic phenomena such as negation, subordination, and wh-extraction, all of which might dully affect the sentiment, subjectivity, or other categorization of the sentence.”
Sentiment Classification Using Word Sub-sequences and Dependency Sub-trees (2005, methodology): “[9] used subtrees of word dependency trees as features for sentence-wise sentiment polarity classification.”