Statistical Models for Text Segmentation
Machine Learning - Special issue on natural language learning
Learning dictionaries for information extraction by multi-level bootstrapping
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
DEADLINER: building a new niche search engine
Proceedings of the ninth international conference on Information and knowledge management
Data mining: concepts and techniques
Data mining: concepts and techniques
Automatic segmentation of text into structured records
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Machine Learning
An approach to automatic classification of text for information retrieval
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Active Learning for Natural Language Parsing and Information Extraction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
X-tract: Structure Extraction from Botanical Textual Descriptions
SPIRE '99 Proceedings of the String Processing and Information Retrieval Symposium & International Workshop on Groupware
TextTiling: A Quantitative Approach to Discourse
TextTiling: A Quantitative Approach to Discourse
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Text segmentation based on similarity between words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Automatic acquisition of domain knowledge for Information Extraction
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 2
A Dynamic Programming Algorithm for Linear Text Segmentation
Journal of Intelligent Information Systems
Using collocations for topic segmentation and link detection
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Automating semantic markup of semi-structured text via an induced knowledge base: a case study using floras
Unsupervised structure discovery for biodiversity information
Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries
Unsupervised semantic markup of literature for biodiversity digital libraries
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
An application for semantic markup of biodiversity documents
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
Semantic annotation of biosystematics literature without training examples
Journal of the American Society for Information Science and Technology
Information fusion in taxonomic descriptions
Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing
| Hi-index | 0.00 |
To automatically convert legacy data of taxonomic descriptions into extensible markup language (XML) format, the authors designed a machine-learning-based approach. In this project three corpora of taxonomic descriptions were selected to prove the hypothesis that domain knowledge and conventions automatically induced from some semistructured corpora (i.e., base corpora) are useful to improve the markup performance of other less-structured, quite different corpora (i.e., evaluation corpora). The “structuredness” of the three corpora was carefully measured. Basing on the structuredness measures, two of the corpora were used as the base corpora and one as the evaluation corpus. Three series of experiments were carried out with the MARTT (markuper of taxonomic treatments) system the authors developed to evaluate the effectiveness of different methods of using the n-gram semantic class association rules, the element relative position probabilities, and a combination of the two types of knowledge mined from the automatically marked-up base corpora. The experimental results showed that the induced knowledge from the base corpora was more reliable than that learned from the training examples alone, and that the n-gram semantic class association rules were effective in improving the markup performance, especially on the elements with sparse training examples. The authors also identify a number of challenges for any automatic markup system using taxonomic descriptions. © 2007 Wiley Periodicals, Inc.