Large document collections can be hard to explore if the user presents her information need in a limited set of keywords. Ambiguous intents arising out of these short queries often result in long-winded query sessions and many query reformulations. To alleviate this problem, in this work, we propose the novel concept of semantic aspects (e.g., ⟨{𝗆𝗂𝖼𝗁𝖺𝖾𝗅\𝗍𝖾𝗑𝗍 {-}𝗉𝗁𝖾𝗅𝗉𝗌},{𝖺𝗍𝗁𝖾𝗇𝗌, 𝖻𝖾𝗂𝗃𝗂𝗇𝗀, 𝗅𝗈𝗇𝖽𝗈𝗇},[2004,2016]⟩ for the ambiguous query Open image in new window) and present the xFactor algorithm that generates them from annotations in documents. Semantic aspects uplift document contents into a meaningful structured representation, thereby allowing the user to sift through many documents without the need to read their contents. The semantic aspects are created by the analysis of semantic annotations in the form of temporal, geographic, and named entity annotations. We evaluate our approach on a novel testbed of over 5,000 aspects on Web-scale document collections amounting to more than 450 million documents. Our results show the xFactor algorithm finds relevant aspects for highly ambiguous queries.