Bridging Text Embeddings for Unconventional Linguistic Contexts
Abstract
This document explores a novel extension of word embedding techniques to facilitate contextual information extraction from atypical textual sources. Utilizing a corpus derived from the tvtropes dataset, this work introduces two distinct models: an N-Grams Permutation approach and a Database-Like Reinforcement methodology. A comparative evaluation demonstrates that the artificially generated corpus, when processed by these models, significantly enhances accuracy by up to 45.2% over human-curated linguistic representations for this specialized application of Natural Language Processing.
Fields of Study & Subjects
Topic Modeling
Natural Language Processing Techniques
Sentiment Analysis and Opinion Mining
Publication details
- Journal / source
- International Journal of Artificial Intelligence Data Science and Machine Learning
- Volume
- 7
- Issue
- 1
- Pages
- 21–25
- Publication date
- 1 January 2026
- Language
- EN