Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Programming and Evolvable Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Genetic Programming For Attribute Construction In Data Mining
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Frequent Substructure-Based Approaches for Classifying Chemical Compounds
IEEE Transactions on Knowledge and Data Engineering
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection
Genetic Programming and Evolvable Machines
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Evolving Lucene search queries for text classification
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Just how mad are you? finding strong and weak opinion clauses
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Feature subsumption for opinion analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Identifying types of claims in online customer reviews
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Interactive annotation learning with indirect feature voting
SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
Generalizing dependency features for opinion mining
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Using feature construction to avoid large feature spaces in text classification
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Sentiment classification using word sub-sequences and dependency sub-trees
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Text representation using dependency tree subgraphs for sentiment analysis
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
Using a heterogeneous dataset for emotion analysis in text
Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in artificial intelligence
Assessing benefit from feature feedback in active learning for text classification
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
Modeling of stylistic variation in social media with stretchy patterns
DIALECTS '11 Proceedings of the First Workshop on Algorithms and Resources for Modelling of Dialects and Language Varieties
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In this work, we propose a novel representation of text based on patterns derived from linguistic annotation graphs. We use a subgraph mining algorithm to automatically derive features as frequent subgraphs from the annotation graph. This process generates a very large number of features, many of which are highly correlated. We propose a genetic programming based approach to feature construction which creates a fixed number of strong classification predictors from these subgraphs. We evaluate the benefit gained from evolved structured features, when used in addition to the bag-of-words features, for a sentiment classification task.