Academic Journal
Text Algorithms in Economics
| العنوان: | Text Algorithms in Economics |
|---|---|
| المؤلفون: | Ash, Elliott, Hansen, Stephen |
| المصدر: | Annual Review of Economics, 15 (1) |
| بيانات النشر: | Annual Reviews |
| سنة النشر: | 2023 |
| المجموعة: | ETH Zürich Research Collection |
| مصطلحات موضوعية: | text as data, topic model, word embeddings, large language models, transformer models |
| الوصف: | This article provides an overview of the methods used for algorithmic text analysis in economics, with a focus on three key contributions. First, we introduce methods for representing documents as high-dimensional count vectors over vocabulary terms, for representing words as vectors, and for representing word sequences as embedding vectors. Second, we define four core empirical tasks that encompass most text-as-data research in economics and enumerate the various approaches that have been taken so far to accomplish these tasks. Finally, we flag limitations in the current literature, with a focus on the challenge of validating algorithmic output. ; ISSN:1941-1383 ; ISSN:1941-1391 |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/application/pdf |
| اللغة: | English |
| Relation: | info:eu-repo/semantics/altIdentifier/wos/001066019400025; http://hdl.handle.net/20.500.11850/593179 |
| DOI: | 10.3929/ethz-b-000593179 |
| الاتاحة: | https://hdl.handle.net/20.500.11850/593179 https://doi.org/10.3929/ethz-b-000593179 |
| Rights: | info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ ; Creative Commons Attribution 4.0 International |
| رقم الانضمام: | edsbas.E31CCF08 |
| قاعدة البيانات: | BASE |
| DOI: | 10.3929/ethz-b-000593179 |
|---|