Word embedding for social sciences: an interdisciplinary survey
Machine learning models learn low-dimensional representations from complex high-dimensional data. Not only computer science but also social science has benefited from the advancement of these powerful tools. Within such tools, word embedding is one of the most popular methods in the literature. Howe...
Saved in:
| Main Authors: | Akira Matsui, Emilio Ferrara |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2024-12-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-2562.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Toward the Development of Large-Scale Word Embedding for Low-Resourced Language
by: Shahzad Nazir, et al.
Published: (2022-01-01) -
Advancing Arabic Word Embeddings: A Multi-Corpora Approach with Optimized Hyperparameters and Custom Evaluation
by: Azzah Allahim, et al.
Published: (2024-11-01) -
Codifying and Validating Management Accounting Practices Measurement Using Content Analysis and Word Embedding Model Using Word2Vec Method
by: Zahra Khorramdel Masouleh, et al.
Published: (2024-09-01) -
Enhancing Word Embeddings for Improved Semantic Alignment
by: Julian Szymański, et al.
Published: (2024-12-01) -
Sentiment Classification Performance Analysis Based on Glove Word Embedding
by: Yasin Kırelli, et al.
Published: (2021-06-01)