One piece of these are table detection and segmentation tools that enable our analysts to increase their scope of ingested data. We have also built out a large suite of tools for structured data. In the law domain, we have built a legal principles engine that enables lawyers to uncover the underlying case law argumentation that supports a particular decision.īeyond these core functions, we have built sophisticated fact extractors (or relationship extractors), that pick out specific information from documents in order to ease our ingestion flow. Beyond that, our topic classification engine (e.g., NI OIL) automatically tags documents with normalized topics to make retrieval and monitoring straight-forward. These named entity extractors are crucial for enabling our sentiment analysis (BSV and TREN) derived indicators that estimate how positive a piece of news is for a particular company. On top of this core tool set, we have built named entity extractors that detect people, companies, tickers and organizations in natural text, which is deployed across our news and social text databases. At the core of this program is a proprietary, robust real-time NLP library that performs low-level text resolution tasks such as tokenization, chunking and parsing. Our engineering teams have built state-of-the-art NLP technology for core document understanding, recommendation, and customer-facing systems.Īt the heart of our NLP program is technology that extracts structured information from documents - sometimes known as digitization or normalization. Over the past decade, we have increased our investment in statistical natural language processing (NLP) techniques that extend our capabilities. NOTE: If “DR” mode is activated and are using your university email but still do not see this tab, email us at with your BMC account information.Throughout the life of the company, Bloomberg has always relied on text as a key underlying source of data for our clients. Click this tab and follow the instructions. If the university has DR mode activated, users should see a tab at the top of their dashboard called “Terminal Access”. Users with a BMC account tied to their university e-mail can login with their BMC username and password. AFTER SETTING UP ACCOUNTe-mail: and so we can approve, Subject of e-mail: Bloomberg BMC. Can take up to 1 business day to complete. Be sure to use your university e-mail when creating a BMC account.
Option 2: Students and faculty that do not have an existing Bloomberg Terminal login, can request a Bloomberg username and password at our BMC web portal.
Option 1: Visit the library and sign up for an account on one of the three Bloomberg Terminals - this gets you immediate access in the library or remotely. Help! I don't have an existing Bloomberg Terminal username or password If this information is not already on file with Bloomberg, contact customer support:
Step 3: Verify identity with a code sent to the enrolled student email or mobile device. NOTE: Bloomberg Terminal credentials should be used. Step 2: Enter personal Bloomberg username and password (what you would enter if you were at a terminal in the library) Once a student or professor has a Bloomberg login (different from a BMC login), users may login to Bloomberg remotely in the following way: During the Pandemic (through Feb 28, 2022) you can either access the three terminals in the library or we have arranged for access to the Bloomberg Terminals via Bloomberg's Disaster Recovery (DR) service.*