An Incrementally Trainable Statistical Approach to Information Extraction - Based on Token Classification and Rich Context Model

Most of the information stored in digital form is hidden in natural language texts. The purpose of Information Extraction (IE) is to find desired pieces of information in unstructured or weakly structured texts and store them in a form that is suitable for automatic querying and processing. This book presents a innovative approach to statistical information extraction. It introduces a new algorithm which supports functionality not available in previous IE systems, such as interactive incremental training to reduce the human training effort. The system also utilizes new sources of information, employing rich tree-based context representations to combine document structure (HTML or XML markup) with linguistic and semantic information. The resulting IE system is designed as a generic framework for statistical information extraction. All core components can be modified or exchanged independently of each other. This book is of interest for professionals who have to deal with large amounts of weakly structured information and seek ways to automate this process, as well as for researchers and practitioners active in the fields of text mining and text classification.