Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Textual Data Mining to Support Science and Technology Management
Journal of Intelligent Information Systems
Proceedings of the ninth international conference on Information and knowledge management
Data mining: concepts and techniques
Data mining: concepts and techniques
An Evaluation of Statistical Approaches to Text Categorization
Information Retrieval
Probabilistic combination of text classifiers using reliability indicators: models and results
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
On Effective Conceptual Indexing and Similarity Search in Text Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Better Rules, Few Features: A Semantic Approach to Selecting Features from Text
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Text analysis and knowledge mining system
IBM Systems Journal
Automated generation of model cases for help-desk applications
IBM Systems Journal
Expert Systems with Applications: An International Journal
A fuzzy clustering approach for finding similar documents using a novel similarity measure
Expert Systems with Applications: An International Journal
Grouping of TRIZ Inventive Principles to facilitate automatic patent classification
Expert Systems with Applications: An International Journal
Seeding the survey and analysis of research literature with text mining
Expert Systems with Applications: An International Journal
A new approach on search for similar documents with multiple categories using fuzzy clustering
Expert Systems with Applications: An International Journal
A systematic approach to new mobile service creation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Automated email answering by text pattern matching
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
Expert Systems with Applications: An International Journal
On enhancing the performance of spam mail filtering system using semantic enrichment
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
| Hi-index | 12.07 |
In text mining, the applications domain of text classification techniques is very broad to include text filtering, word identification, and web page classification, etc. Through text classification techniques, documents can be placed into previously defined classifications in order to save on time costs especially when manual document search methods are employed. This research uses text classification techniques applied to e-mail reply template suggestions in order to lower the burden of customer service personnel in responding to e-mails. Suggested templates allows customer service personnel, using a pre-determined number of templates, to find the needed reply template, and not waste time in searching for relevant answers from too much information available. Current text classification techniques are still single-concept based. This research hopes to use a multiple concept method to integrate the relationship between concepts and classifications which will thus allow easy text classification. Through integration of different concepts and classifications, a dynamically unified e-mail concept can recommend different appropriate reply templates. In so doing, the differences between e-mails can be definitely determined, effectively improving the accuracy of the suggested template. In addition, for e-mails with two or more questions, this research tries to come up with an appropriate reply template. Based on experimental verification, the method proposed in this research effectively proposes a template for e-mails of multiple questions. Therefore, using multiple concepts to display the document topic is definitely a clearer way of extracting information that a document wants to convey when the vector of similar documents is used.