Dynamic Entity Recognition for Online Article Supplemental Content
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Solution Overview
Problem
Current methods for providing users with contextually relevant information about entities in online content are labor-intensive, require manual editing, and fail to consider related entities, leading to a suboptimal user experience due to inconvenient steps and uncertainty about information quality.
Innovation Solution
A system that dynamically generates and displays supplemental content by analyzing the content, identifying primary and related entities, and retrieving information from various search engines and databases, eliminating the need for manual editing and enhancing user engagement without requiring users to send queries or click on links.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual searching and information gathering is performed by content providers, then information quality and relevance can be ensured, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system enables automatic entity recognition and information supplementation without manual intervention. The content management system automatically identifies entities in content, queries search engines for related information, and integrates supplemental content, allowing the system to serve itself rather than requiring content providers to manually search and gather information.
Solution Approach 2:
The manual mechanical process of searching and information gathering is replaced with an automated electronic system. The content management system uses software algorithms to recognize entities, automated query mechanisms to search external sources, and programmatic methods to integrate results, substituting human manual labor with automated computational processes.
2Loss of information
If users manually search for entity information, then comprehensive information can be obtained, but user convenience and time efficiency deteriorate
Solution Approach 1:
The system performs information gathering and entity recognition in advance, before the user needs the information. When content is displayed to the user, the supplemental information about entities is already prepared and integrated, eliminating the need for users to perform subsequent search actions.
Solution Approach 2:
The content management system acts as an intermediary between the user and external information sources. Instead of users directly searching multiple sources, the system automatically queries search engines and databases, retrieves relevant information, and presents it integrated with the content, simplifying the user's interaction to passive consumption.
3Ease of manufacture
If dictionary-based entity highlighting is implemented, then entity identification can be achieved, but adaptability to changing context and related entities is lost
Solution Approach 1:
The system transitions from static dictionary-based entity identification to dynamic context-aware entity recognition. The content management system analyzes the actual content to identify entities, allowing the system to adapt to different contexts, topics, and related entities based on the specific content being processed, rather than relying on predetermined dictionaries.
Data Source
AI summary
An online article is enhanced by displaying, in association with the article, supplemental content that includes entities that are extracted from the article and/or entities that are related to entities that are extracted from the article. The supplemental content further includes information about each of the entities. The information about an entity may be obtained by searching for the entity in one or more searchable repositories of data. For example, the supplemental content may include, for each entity, video, image, web, and/or news search results. The supplemental content may further include information such as stock quotes, abstracts, maps, scores, and so on. The entities are selected using a variety of analysis and ranking techniques based on contextual factors such as user-specific information, time-sensitive popularity trends, grammatical features, search result quality, and so on. The entities may further be selected for purposes such as generating ad-based revenue.