In addition to language models, several other techniques could be explored to develop effective evaluation functions for research question extraction from complex technical documents:
Rule-Based Systems: Developing rule-based systems that utilize predefined linguistic patterns and heuristics can help identify research questions based on their structural characteristics. These systems can be particularly effective in domains with well-defined question formats.
Information Retrieval Techniques: Leveraging information retrieval methods, such as keyword extraction and semantic similarity measures, can enhance the identification of relevant research questions. Techniques like TF-IDF (Term Frequency-Inverse Document Frequency) and BM25 can be employed to rank potential questions based on their relevance to the document.
Graph-Based Approaches: Utilizing graph-based models to represent relationships between concepts within the text can aid in understanding the context and connections between different components of research questions. This approach can help in identifying questions that are contextually relevant.
Crowdsourcing: Engaging crowdsourcing platforms to gather diverse evaluations from a larger pool of annotators can provide a broader range of insights into research question quality. This method can help capture varying interpretations and improve the robustness of evaluations.
Expert Review Panels: Forming panels of subject matter experts to review and evaluate extracted research questions can provide authoritative assessments of quality. These experts can offer insights based on their deep understanding of the field, ensuring that evaluations are grounded in domain-specific knowledge.
Hybrid Approaches: Combining multiple techniques, such as integrating rule-based systems with machine learning models, can create a more comprehensive evaluation function. This hybrid approach can leverage the strengths of different methods to improve overall performance.
By exploring these techniques, researchers can develop more effective evaluation functions for research question extraction, enhancing the ability to assess and improve the quality of questions derived from complex technical documents.