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An Eclectic Approach for Enhancing Language Models Through Rich Embedding Features
- Source :
- IEEE Access, Vol 12, Pp 100921-100938 (2024)
- Publication Year :
- 2024
- Publisher :
- IEEE, 2024.
-
Abstract
- Text processing is a fundamental aspect of Natural Language Processing (NLP) and is crucial for various applications in fields such as artificial intelligence, data science, and information retrieval. It plays a core role in language models. Most text-processing approaches focus on describing and synthesizing, to a greater or lesser degree, lexical, syntactic, and semantic properties of text in the form of numerical vectors that induce a metric space, in which, it is possible to find underlying patterns and structures related to the original text. Since each approach has strengths and weaknesses, finding a single approach that perfectly extracts representative text properties for every task and application domain is hard. This paper proposes a novel approach capable of synthesizing information from heterogeneous state-of-the-art text processing approaches into a unified representation. Encouraging results demonstrate that using this representation in popular machine-learning tasks not only leads to superior performance but also offers notable advantages in memory efficiency and preservation of underlying information of the distinct sources involved in such a representation.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 12
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.0b82df7867f488c8e282c76dd220662
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2024.3422971