White Paper: Natural Language Processing (NLP) for Text Analytics
Information-rich text is everywhere—emails, chat transcripts, academic journals, newspaper articles—but extracting insights from unstructured text data poses a challenge for many organizations.
Annually, organizations spend millions of dollars manually reviewing text documents; however, the manual review process is often error-prone, inconsistent, and unable to scale. Fortunately, recent advances in technology have enabled machines to understand and parse text more accurately and efficiently than humans. Equipping human text reviewers with text analytics tools can save organizations time and money while providing more thorough and systematic document reviews.
One such use case where text analytics tools can help is with organizations’ requirements to review the thousands of public comments on proposed rulemaking. For example, the Financial Accounting Standard Board (FASB) solicits comment letters from the public on proposed Accounting Standard Updates (ASUs). FASB bases their reasoning for the updates in part on the comments provided. The comments may contain persuasive new data or policy arguments or pose challenging questions and criticisms. Organizations must also filter out fraudulent comments generated by bots or other automated software.
How should organizations proceed to analyze large volumes of public comments in a systematic way? Entities that do not use text analytics tools may find that their reviewers can only check a small sample of documents, and their reviews are conducted haphazardly, without consistency, controls, or audit trails. This white paper describes our end-to-end text analytics solution that speeds up and enhances manual review while reducing costs. Our solution combines an automated system’s accuracy, speed, and scale with the control and precision of manual review. Our intuitive tools help reviewers extract, parse, and organize large amounts of text, freeing the workforce to focus on the most complex and nuanced text documents.
To download a copy of the entire white paper that discusses how Natural Language Processing can help solve your agencies text analytics challenges, please fill out the form below to receive a copy: