Entity Reference Ranking for Unstructured Question Answering
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Solution Overview
Problem
Existing question answering systems rely on manually generated databases and struggle to provide accurate and automated answers to natural language queries, particularly for queries involving entity references, as they lack the ability to efficiently process and rank entity references in unstructured data.
Innovation Solution
A system that retrieves search results, identifies entity references within those results, ranks them using frequency and topicality scores, and selects the most commonly occurring entity as the answer, leveraging unstructured data from the internet to provide automated and continuously updated responses to queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manually generated databases are used for question answering, then answer accuracy can be maintained, but the system cannot provide automated and continuously updated responses
Solution Approach 1:
The patent introduces an intermediary processing layer that includes entity reference identification, data extraction, and ranking mechanisms. This intermediary layer processes unstructured search results to extract relevant entity information, thereby enabling automated question answering while maintaining answer reliability through systematic processing and verification steps.
Solution Approach 2:
The patent replaces manual database generation and maintenance with an automated system that uses search engines, entity reference identification algorithms, and ranking mechanisms. This substitution transforms the mechanical process of manual database creation into an automated information extraction and processing system, enabling continuous updates without sacrificing answer quality.
2Productivity
If unstructured data from the internet is processed to identify entity references, then continuously updated answers can be provided, but the complexity of processing and ranking entity references increases
Solution Approach 1:
The patent segments the complex task of question answering into distinct modules: search result retrieval, entity reference identification, data extraction, ranking, and answer selection. Each module handles a specific aspect of the processing pipeline, reducing overall system complexity while improving processing speed and efficiency through specialized handling of each task.
Solution Approach 2:
The patent performs preliminary actions by pre-processing search results to identify and extract entity references before the actual question answering process. This preliminary extraction and ranking of entity references from unstructured data simplifies subsequent processing steps and accelerates the overall question answering response time.
3Measurement precision
If entity references are ranked using frequency and topicality scores, then accurate answers can be selected, but the time required to process and rank multiple entity references increases
Solution Approach 1:
The patent applies partial action by ranking only the top N entity references from search results rather than processing all possible entities. This selective ranking approach maintains answer accuracy by focusing computational resources on the most relevant entities while significantly reducing processing time through limited scope analysis.
Data Source
AI summary
Methods, systems, and computer-readable media are provided for collective reconciliation. In some implementations, a query is received, wherein the query is associated at least in part with a type of entity. Top-ranked search results are caused to be generated based at least in part on the query. An entity of the type of entity is identified in the top-ranked results and a response to the query is generated from the top-ranked results, the response including a reference to the entity that includes text distinct from the terms of the query.