Contextual Answer Generation Through Linguistic Query Analysis
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Solution Overview
Problem
Existing question and answer systems, particularly non-factoid-based systems, struggle to provide contextually and grammatically correct answers to queries due to inaccurate passage retrieval and nonsensical document summaries, leading to inefficient user search for relevant information.
Innovation Solution
A system that analyzes the linguistic and syntactic structure of a query to generate contextually, grammatically, and conversationally correct answers by pairing questions with candidate answers, extracting features, and scoring them using a ranking model to provide ranked answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If passage retrieval is used to answer non-factoid questions, then relevant text spans are identified, but the accuracy deteriorates because the system does not consider the actual context of the question
Solution Approach 1:
The system segments the answer generation process into multiple independent components: passage retrieval, linguistic analysis, feature extraction, and ranking. Each component handles a specific aspect of the problem, allowing the system to consider contextual factors without overwhelming complexity.
Solution Approach 2:
The system performs preliminary linguistic and syntactic analysis of both the question and candidate answers before final ranking. This advance processing extracts contextual features that inform the subsequent scoring and selection of the most accurate answer.
2Loss of information
If document summarization is used to answer questions, then relevant information is extracted, but the quality deteriorates because summaries are often nonsensical and not related to the actual context
Solution Approach 1:
The system uses feedback mechanisms where the linguistic analysis of the question informs the selection and scoring of candidate answers. The ranking model continuously refines answer selection based on how well candidate answers match the contextual features extracted from the question, ensuring summaries remain relevant to the actual query context.
Solution Approach 2:
The system changes the parameters used to evaluate answers from simple relevance matching to comprehensive linguistic and syntactic feature matching. By analyzing multiple linguistic parameters including grammatical structure, semantic relationships, and contextual coherence, the system ensures that summarized information maintains high quality and contextual relevance.
3Productivity
If natural language generation is used to create answers, then responses are generated, but the quality deteriorates because results often look like word salad and are not contextually correct
Solution Approach 1:
The system performs preliminary linguistic and syntactic analysis of the question before generating answers. This advance understanding of the question's structure, context, and requirements guides the natural language generation process, ensuring that generated answers are contextually correct rather than random word combinations.
Solution Approach 2:
The system employs multiple parameters for evaluating and refining generated answers, including grammatical correctness, semantic coherence, and contextual relevance. By monitoring and optimizing across these multiple parameters, the system ensures high contextual correctness while maintaining generation efficiency.
4Reliability
If users must search through identified passages to find answers, then comprehensive search is performed, but the efficiency deteriorates because users spend excessive time searching through irrelevant information
Solution Approach 1:
The system extracts and analyzes key linguistic and syntactic features from the question that are essential for identifying the correct answer. By focusing on these extracted features rather than performing exhaustive searches through all passages, the system maintains comprehensive search reliability while significantly reducing the time users need to spend searching.
Solution Approach 2:
The system changes the search parameters from broad keyword matching to targeted linguistic feature matching. By using extracted linguistic features as search parameters, the system quickly identifies relevant passages without requiring users to manually search through irrelevant information, thus reducing search time while maintaining comprehensive coverage.
Data Source
AI summary
The present disclosure relates to systems and methods for generating contextually, grammatically, and conversationally correct answers to input questions. Embodiments provide for linguistic and syntactic structure analysis of a submitted question in order to determine whether the submitted question may be answered by at least one headnote. The question is then further analyzed to determine more details about the intent and context of the question. A federated search process, based on the linguistic and syntactic structure analysis, and the additional analysis of the question is used to identify candidate question-answer pairs from a corpus of previously created headnotes. Machine learning models are used to analyze the candidate question-answer pairs, additional rules are applied to rank the candidate answers, and dynamic thresholds are applied to identify the best potential answers to provide to a user as a response to the submitted question.