The growing availability of on-line textual sources and the potential number of applications of knowledge acquisition approaches from textual data, such as Information Extraction (IE), has lead to an increase in IE research. Some examples of these applications are the generation of data bases from documents, as well as the acquisition of knowledge useful for emerging technologies like question answering and information integration, among others related to text mining. However, one of the main drawbacks of the application of IE refers to the intrinsic language and domain dependence. For the sake of reducing the high cost of manually adapting IE applications to new domains and languages, different Machine Learning (ML) techniques have been applied by the research community. This survey describes and compares the main approaches to IE and the different ML techniques used to achieve adaptable IE technology, as of today.