Learning from syntax generalizations for automatic semantic annotation

Nowadays, there is a huge amount of textual data coming from on-line social communities like Twitter or encyclopedic data provided by Wikipedia and similar platforms. This Big Data Era created novel challenges to be faced in order to make sense of large data storages as well as to efficiently find specific information within them. In a more domain-specific scenario like the management of legal documents, the extraction of semantic knowledge can support domain engineers to find relevant information in more rapid ways, and to provide assistance within the process of constructing application-based legal ontologies. In this work, we face the problem of automatically extracting structured knowledge to improve semantic search and ontology creation on textual databases. To achieve this goal, we propose an approach that first relies on well-known Natural Language Processing techniques like Part-Of-Speech tagging and Syntactic Parsing. Then, we transform these information into generalized features that aim at capturing the surrounding linguistic variability of the target semantic units. These new featured data are finally fed into a Support Vector Machine classifier that computes a model to automate the semantic annotation. We first tested our technique on the problem of automatically extracting semantic entities and involved objects within legal texts. Then, we focus on the identification of hypernym relations and definitional sentences, demonstrating the validity of the approach on different tasks and domains.