Viewing morphology as an inference process
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
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
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Prototype-driven learning for sequence models
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Active Learning with Feedback on Features and Instances
The Journal of Machine Learning Research
Learning from labeled features using generalized expectation criteria
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Modeling annotators: a generative approach to learning from annotator rationales
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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 automatically extracted subgraph features
CAAGET '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text
Assessing benefit from feature feedback in active learning for text classification
CoNLL '11 Proceedings of the Fifteenth Conference on Computational Natural Language Learning
| Hi-index | 0.00 |
We demonstrate that a supervised annotation learning approach using structured features derived from tokens and prior annotations performs better than a bag of words approach. We present a general graph representation for automatically deriving these features from labeled data. Automatic feature selection based on class association scores requires a large amount of labeled data and direct voting can be difficult and error-prone for structured features, even for language specialists. We show that highlighted rationales from the user can be used for indirect feature voting and same performance can be achieved with less labeled data. We present our results on two annotation learning tasks for opinion mining from product and movie reviews.