End-to-End Relation Extraction via Syntactic Structures and Semantic Resources

Information Extraction (IE) aims at mapping texts into fixed structure representing the key information. A typical IE system will try to answer the questions like who are present in the text, what events happen and when these events happen. The task is making possible significant advances in applications that require deep understanding capabilities such as questionanswering engines, dialogue systems, or the semantic web. Due to the huge effort and time consumation of developping extraction systems by domain experts, our approach focuses on machine learning methods that can accurately infer an extraction model by training on a dataset. The goal of this research is to design and implement models with improved performance by learning the combination of different algorithms or by inventing novel structures that are able to exploit kinds of evidence that have not been explored in the literature. A basic component of an IE system is named entity recognition (NER) whose purpose is to locate objects that can be referred by names, belonging to a predefined set of categories. We approach this task by proposing a novel reranking framework that employs two learning phases to pick the