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Information Processing and Management: an International Journal
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
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Combining labeled and unlabeled data with co-training
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Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
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Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
The use of bigrams to enhance text categorization
Information Processing and Management: an International Journal
Semi-Naive Bayesian Classifier
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Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
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Artificial Intelligence
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IEEE Transactions on Audio, Speech, and Language Processing
Annotation and verification of sense pools in OntoNotes
Information Processing and Management: an International Journal
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ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Multidimensional text classification for drug information
IEEE Transactions on Information Technology in Biomedicine
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IEEE Transactions on Information Technology in Biomedicine
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IEEE Transactions on Information Theory
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Purpose: Negative life events, such as the death of a family member, an argument with a spouse or the loss of a job, play an important role in triggering depressive episodes. Therefore, it is worthwhile to develop psychiatric services that can automatically identify such events. This study describes the use of association language patterns, i.e., meaningful combinations of words (e.g., ), as features to classify sentences with negative life events into predefined categories (e.g., Family, Love, Work). Methods: This study proposes a framework that combines a supervised data mining algorithm and an unsupervised distributional semantic model to discover association language patterns. The data mining algorithm, called association rule mining, was used to generate a set of seed patterns by incrementally associating frequently co-occurring words from a small corpus of sentences labeled with negative life events. The distributional semantic model was then used to discover more patterns similar to the seed patterns from a large, unlabeled web corpus. Results: The experimental results showed that association language patterns were significant features for negative life event classification. Additionally, the unsupervised distributional semantic model was not only able to improve the level of performance but also to reduce the reliance of the classification process on the availability of a large, labeled corpus.