Knowledge automatically extracted from text captures instances, classes of instances and relations among them. In particular, the acquisition of class attributes (e.g., "top speed", "body style" and "number of cylinders" for the class of "sports cars") from text is a particularly appealing task and has received much attention recently, given its natural fit as a building block towards the far-reaching goal of constructing knowledge bases from text. This tutorial provides an overview of extraction methods developed in the area of Web-based information extraction, with the purpose of acquiring attributes of open-domain classes. The attributes are extracted for classes organized either as a flat set or hierarchically. The extraction methods operate over unstructured or semi-structured text available within collections of Web documents, or over relatively more intriguing data sources consisting of anonymized search queries. The methods take advantage of weak supervision provided in the form of seed examples or small amounts of annotated data, or draw upon knowledge already encoded within human-compiled resources (e.g., Wikipedia). The more ambitious methods, aiming at acquiring as many accurate attributes from text as possible for hundreds or thousands of classes covering a wide range of domains of interest, need to be designed to scale to Web collections. This restriction has significant consequences on the overall complexity and choice of underlying tools, in order for the extracted attributes to ultimately aid information retrieval in general and Web search in particular, by producing relevant attributes for open-domain classes, along with other types of relations among instances or among classes.