Email Abstract Generation via Meaningless Lexical Unit Segmentation
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Solution Overview
Problem
Conventional email systems are inadequate in determining the priority of email messages, as the subject field and preview of the first lines may not accurately reflect the message's significance, especially when the topic is vague or when multiple messages have similar topics, leading to inefficiencies in user communication.
Innovation Solution
A method for computer processing of email messages that involves syntax analysis to identify meaningful and meaningless lexical units, with databases generated from previous messages to users and a group of users, to create an abstract that excludes meaningless units, focusing on the most significant logical blocks of HTML code containing meaningful units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the subject field and preview of the first lines are used to determine message priority, then the user can quickly familiarize with received messages, but the accuracy of priority determination deteriorates when the topic is vague or when multiple messages have similar topics
Solution Approach 1:
The message text is divided into lexical units (words, phrases, sentences) that are individually analyzed for meaningfulness. The system segments the text into meaningful lexical units and meaningless lexical units, then generates an abstract containing only meaningful units. This segmentation allows the system to process messages efficiently while accurately identifying significant content, resolving the contradiction between processing speed and priority determination accuracy.
Solution Approach 2:
The system extracts and removes meaningless lexical units (such as filler words, common phrases, and non-informative elements) from the message text. By taking out these meaningless units and creating an abstract that contains only meaningful lexical units, the system provides a condensed representation that accurately reflects message priority while maintaining fast processing capability.
2Ease of operation
If the first lines of the message are reviewed to determine priority, then the user can get a quick overview, but insignificant words and phrases hide the main point of the message
Solution Approach 1:
The system extracts meaningless lexical units (insignificant words and phrases) from the message and removes them to create a refined abstract. This extraction process eliminates the hiding effect of insignificant words while preserving the main point, allowing users to review messages conveniently with clear, focused content that highlights the essential information.
Solution Approach 2:
The system creates a composite representation of the message by combining meaningful lexical units into an abstract that preserves the essential information while excluding meaningless elements. This composite structure maintains ease of review while ensuring the main point remains visible and prominent.
3Measurement precision
If syntax analysis is performed on the entire message to identify meaningful lexical units, then the accuracy of content representation improves, but the processing time increases
Solution Approach 1:
The system segments the message into lexical units and processes them individually through syntax analysis. By dividing the message into discrete units (words, phrases, sentences) and analyzing each for meaningfulness, the system achieves accurate content representation while maintaining efficient processing through parallelizable, modular analysis of individual lexical units rather than analyzing the entire message as a single unit.
Data Source
AI summary
There is provided a system and a method for detecting meaningless lexical units in a text message, the method comprising: performing a syntax analysis of the text message and determining at least one lexical unit as a first potential meaningless lexical unit; determining a control sum of the first potential meaningless lexical unit; matching using a first parameter, the first potential meaningless lexical unit with lexical units from a plurality of lexical units from a lexical units database, wherein matching using the first parameter comprises matching a control sum of the first potential meaningless lexical unit with control sums of meaningless lexical units from the lexical units database; determining the first potential meaningless lexical unit is a meaningless lexical unit if the lexical units database includes at least one meaningless lexical unit with a control sum corresponding to the control sum of the first potential meaningless lexical unit.