Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
An algorithm for pronominal anaphora resolution
Computational Linguistics
Journal of Interactive Learning Research - Special double issue on concept mapping
Knowledge discovery from texts: a concept frame graph approach
Proceedings of the eleventh international conference on Information and knowledge management
ICDT '97 Proceedings of the 6th International Conference on Database Theory
Robust pronoun resolution with limited knowledge
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Anaphora for everyone: pronominal anaphora resoluation without a parser
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
The clustering power of low frequency words in academic Webs: Brief Communication
Journal of the American Society for Information Science and Technology
Improved automatic keyword extraction given more linguistic knowledge
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Text mining without document context
Information Processing and Management: an International Journal - Special issue: Informetrics
CogNIAC: high precision coreference with limited knowledge and linguistic resources
ANARESOLUTION '97 Proceedings of a Workshop on Operational Factors in Practical, Robust Anaphora Resolution for Unrestricted Texts
Information Processing and Management: an International Journal
Aircraft interior failure pattern recognition utilizing text mining and neural networks
Journal of Intelligent Information Systems
Expert Systems with Applications: An International Journal
Using a concept map knowledge management system to enhance the learning of biology
Computers & Education
Hi-index | 0.00 |
Natural Language Processing (NLP) techniques have been successfully used to automatically extract information from unstructured text through a detailed analysis of their content, often to satisfy particular information needs. In this paper, an automatic concept map construction technique, Fuzzy Association Concept Mapping (FACM), is proposed for the conversion of abstracted short texts into concept maps. The approach consists of a linguistic module and a recommendation module. The linguistic module is a text mining method that does not require the use to have any prior knowledge about using NLP techniques. It incorporates rule-based reasoning (RBR) and case based reasoning (CBR) for anaphoric resolution. It aims at extracting the propositions in text so as to construct a concept map automatically. The recommendation module is arrived at by adopting fuzzy set theories. It is an interactive process which provides suggestions of propositions for further human refinement of the automatically generated concept maps. The suggested propositions are relationships among the concepts which are not explicitly found in the paragraphs. This technique helps to stimulate individual reflection and generate new knowledge. Evaluation was carried out by using the Science Citation Index (SCI) abstract database and CNET News as test data, which are well known databases and the quality of the text is assured. Experimental results show that the automatically generated concept maps conform to the outputs generated manually by domain experts, since the degree of difference between them is proportionally small. The method provides users with the ability to convert scientific and short texts into a structured format which can be easily processed by computer. Moreover, it provides knowledge workers with extra time to re-think their written text and to view their knowledge from another angle.