Stop Word Identification via Session Query Changes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information retrieval systems face challenges in accurately identifying stop words due to high production costs of manually compiled stop word lists and an inability to adapt to complex user search behaviors, leading to inefficiencies in retrieval efficiency and accuracy.
Innovation Solution
A method and apparatus for stop word identification that utilize change-based features between consecutive user queries within the same session, incorporating statistical features and an identification model to improve precision, by analyzing features such as part-of-speech, position, and punctuation changes, and training the model with positive and negative samples based on user satisfaction with search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a manually compiled stop word list is used for stop word identification, then the system can identify stop words, but the production cost is high and the system cannot adapt to complex user search behaviors
Solution Approach 1:
The system automatically generates and updates stop word lists by analyzing user query patterns and search behaviors without requiring manual expert compilation. The stop word identification model learns from actual user interactions and continuously adapts to new search patterns, making the system self-updating and eliminating the need for costly manual maintenance
Solution Approach 2:
The stop word identification system transitions from a static manually compiled list to a dynamic model that continuously learns and adapts to changing user search behaviors. The model is trained on historical query data and can evolve to recognize new stop words and patterns that emerge from actual user interactions
2Measurement precision
If a static stop word list is used, then the implementation is simple, but the identification precision cannot adapt to increasingly complicated user search behaviors
Solution Approach 1:
The system incorporates feedback mechanisms where user interactions with search results are analyzed to continuously improve stop word identification. The model learns from successful and unsuccessful search queries, adjusting its parameters to better distinguish between meaningful keywords and stop words in various search contexts
Solution Approach 2:
The stop word identification model uses multiple features including term frequency, inverse document frequency, position information, and contextual patterns. These parameters are dynamically adjusted based on learned patterns from training data, allowing the system to adapt to different search scenarios and improve identification precision
3Reliability
If manual expert compilation of stop word lists is performed, then comprehensive stop word coverage can be achieved, but the production cost and time consumption are high
Solution Approach 1:
The system performs preliminary automated analysis of large volumes of query data to identify potential stop words before they are formally incorporated into the identification model. By pre-processing and analyzing user interactions, the system prepares comprehensive stop word coverage automatically without requiring time-consuming manual expert review
Data Source
AI summary
The present application relates to the field of computer technologies, and in particular, to a stop word identification method used in an information retrieval system. In a stop word identification method, after a first query input by a user is acquired, a second query that belongs to a same session as the first query is acquired, and a stop word in the first query is identified according to a change-based feature of each word in the first query relative to the second query. According to the solution provided by the present application, a stop word in a query can be identified more accurately, and efficiency and precision of an information retrieval system are improved.