Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The Dynamics of Language Understanding
LEC '02 Proceedings of the Language Engineering Conference (LEC'02)
Learning Knowledge Bases for Information Extraction from Multiple Text Based Web Sites
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Combining Topic Models and Social Networks for Chat Data Mining
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
IEICE - Transactions on Information and Systems
Text classification by labeling words
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A semi-supervised algorithm for pattern discovery in information extraction from textual data
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Information extraction for the semantic web
Proceedings of the First international conference on Reasoning Web
A comparison of textual data mining methods for sex identification in chat conversations
AIRS'08 Proceedings of the 4th Asia information retrieval conference on Information retrieval technology
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Chat mediums are becoming an important part of human life in societies and provide quite useful information about people such as their current interests, habits, social behaviors and tendencies. In this study, we have presented an identification system to identify the sex of a person in a Turkish chat medium. Here, the sex identification is taken as a base study in the information mining in chat mediums. This system acquires data from a chat medium, and then automatically detects the chatter's sex from the information exchanged between chatters and compares them with the known identities of the chatters. To do this task, a simple discrimination function is used to determine the sex of the chatters. A semantic analysis method is also proposed to enhance the performance of the system. The system with the semantic analyzer has achieved accuracy over 90% in the sex identification in the real chat medium.