We have also built tools for our reporters that allow them to create self-service topic streams to find pieces of news about the companies or sectors they are responsible for covering.Īll of these core NLP tools stay strictly within the domain of text, but we have also built out significant functionality that connects text to other artifacts – either people or stock tickers. Additionally, we have built research systems for figure understanding that extract the underlying data from scatter plots. 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. The Monitor module provides real-time access to data and news specific to an industry.Throughout the life of the company, Bloomberg has always relied on text as a key underlying source of data for our clients. Market Share for Specialty Apparel Stores
The Data Library features data-sets specific for each industry and includes macro and industry factors and company-level operating, financial, and valuation statistics. īloomberg Industries covers over 100 industries across the following Sectors: Communications Consumer Discretionary Consumer Staples Energy Financials Health Care Industrials: Materials Technology & Utilities.Įach industry home page features individual modules - Analysis, Data Library, & Monitor - allowing users to locate more in-depth industry information.īI analysis incorporates data as it becomes available and presents it with live links to charts, reports, and other tools. Bloomberg Industries combines the insight from industry analysts with comprehensive data to provide users with a complete view of an industry and its key constituents.