Book contents
- Legal Informatics
- Legal Informatics
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Part I Introduction to Legal Informatics
- Part II Legal Informatics
- Part III Use Cases in Legal Informatics
- A. Contracts and Patents
- B. Litigation and E-discovery
- C. Legal Research, Government Data, and Access to Legal Information
- 3.8 Fastcase, and the Visual Understanding of Judicial Precedents
- 3.9 Mining Information from Statutory Texts in a Public Health Domain
- 3.10 Gov2Vec
- 3.11 Representation and Automation of Legal Information
- D. Dispute Resolution and Access to Justice
- Part IV Legal Informatics in the Industrial Context
3.10 - Gov2Vec
A Case Study in Text Model Application to Government Data1
from C. - Legal Research, Government Data, and Access to Legal Information
Published online by Cambridge University Press: 04 February 2021
- Legal Informatics
- Legal Informatics
- Copyright page
- Contents
- Figures
- Tables
- Contributors
- Part I Introduction to Legal Informatics
- Part II Legal Informatics
- Part III Use Cases in Legal Informatics
- A. Contracts and Patents
- B. Litigation and E-discovery
- C. Legal Research, Government Data, and Access to Legal Information
- 3.8 Fastcase, and the Visual Understanding of Judicial Precedents
- 3.9 Mining Information from Statutory Texts in a Public Health Domain
- 3.10 Gov2Vec
- 3.11 Representation and Automation of Legal Information
- D. Dispute Resolution and Access to Justice
- Part IV Legal Informatics in the Industrial Context
Summary
If an event of interest is correlated with text data, we can learn models of text that predict the event outcome. For example, researchers have predicted financial risk with regression models that use the text of company financial disclosures.2 Topic models can predict outcomes as a function of the proportions of a document that are devoted to the automatically discovered topics,3 and this technique has been used to develop, for example, a topic model that forecasts roll call votes using the text of congressional bills.4 An advantage of the topic model prediction approach is that the model learns interpretable topics and the relationships between the learned topics and outcomes. A disadvantage of the topic model approach is that other, less interpretable text models often exhibit higher predictive power.
- Type
- Chapter
- Information
- Legal Informatics , pp. 393 - 396Publisher: Cambridge University PressPrint publication year: 2021