In addition to formation trees, there are several other types of structural or hierarchical information that could be incorporated into the character representation learning process to enhance performance on a broader range of tasks:
Syntactic Hierarchies: Incorporating syntactic structures, such as part-of-speech tags or dependency relationships, can provide valuable linguistic information that can improve language understanding tasks like parsing or sentiment analysis.
Semantic Hierarchies: Utilizing semantic hierarchies, such as WordNet or knowledge graphs, can help capture the relationships between words or concepts, enabling more nuanced semantic understanding and reasoning.
Contextual Hierarchies: Modeling contextual hierarchies, such as discourse structures or narrative arcs, can enhance the model's ability to understand and generate coherent text in natural language processing tasks.
Multi-level Representations: Integrating multi-level representations, including character-level, word-level, and document-level features, can capture hierarchical information at different granularities, enabling the model to learn complex patterns and relationships in text data.
By incorporating these diverse types of structural and hierarchical information into the character representation learning process, models can achieve a more comprehensive understanding of language and improve performance on a wide range of language-related tasks.