Decision-making in finance involves discovering and summarizing relevant information, generating trade ideas, finding liquidity and counterparties, performing post-hoc analyses, and publishing reports. At Bloomberg, we leverage recent developments in machine learning, knowledge graphs, and language technology to enable intelligent ways for our clients to obtain market advantage in every step of their decision making process, at scale, and with high precision and low latency. In this talk, we cover the use of the Bloomberg Knowledge Graph and advanced natural language processing (NLP) techniques in the following areas: (a) information and relationship extraction to assist our journalists with automated news generation, (b) named entity recognition and linking, topic classification, clustering, and summarization to assist our clients in consuming content, (c) language modeling and semantic parsing to facilitate natural-language-based discovery of content, (d) dialog understanding to aid in structuring instant messages, the primary medium for over-the-counter trading, and (e) sentiment analysis to generate structured time-series signals for alpha generation. Throughout the talk, we will highlight articles published by our group that can serve as further reading material.