Communications of the ACM
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Machine learning in automated text categorisation
Machine learning in automated text categorisation
Machine learning for information extraction in informal domains
Machine learning for information extraction in informal domains
The generic information extraction system
MUC5 '93 Proceedings of the 5th conference on Message understanding
Relational learning via propositional algorithms: an information extraction case study
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
CRYSTAL inducing a conceptual dictionary
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Automatically generating extraction patterns from untagged text
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
| Hi-index | 0.00 |
Information extraction is concerned with applying natural language processing to automatically extract the essential details from text documents. A great disadvantage of current approaches is their intrinsic dependence to the application domain and the target language. Several machine learning techniques have been applied in order to facilitate the portability of the information extraction systems. This paper describes a general method for building an information extraction system using regular expressions along with supervised learning algorithms. In this method, the extraction decisions are lead by a set of classifiers instead of sophisticated linguistic analyses. The paper also shows a system called TOPO that allows to extract the information related with natural disasters from newspaper articles in Spanish language. Experimental results of this system indicate that the proposed method can be a practical solution for building information extraction systems reaching an F-measure as high as 72%.