17540383. DATE AND TIME FEATURE IDENTIFICATION simplified abstract (INTERNATIONAL BUSINESS MACHINES CORPORATION)

DATE AND TIME FEATURE IDENTIFICATION

Organization Name

INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor(s)

Satoshi Masuda of Nerima-ku (JP)

TAKAAKI Tateishi of Yamato-shi (JP)

TOSHIHIRO Takahashi of Nakano-ku (JP)

DATE AND TIME FEATURE IDENTIFICATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 17540383 titled 'DATE AND TIME FEATURE IDENTIFICATION

Simplified Explanation

Methods and systems for text processing involve using column names and associated functions from a code base to build a knowledge base. Classifiers are trained using this knowledge base and cross-validated to determine accuracy scores. The selected classifier with the highest accuracy score is used to process text and identify date/time features.

• Building a knowledge base using column names and associated functions from a code base.
• Training classifiers using the knowledge base and cross-validating them to determine accuracy scores.
• Processing text using the classifier with the highest accuracy score to identify date/time features.

Potential Applications

• Natural language processing in various industries such as finance, healthcare, and customer service.
• Text analysis for sentiment analysis, topic modeling, and information extraction.
• Automated date/time extraction for scheduling, event management, and data analysis.

Problems Solved

• Manual extraction of date/time information from text can be time-consuming and error-prone.
• Traditional text processing methods may not accurately identify date/time features.
• Lack of a standardized approach for training classifiers and determining their accuracy.

Benefits

• Improved efficiency and accuracy in extracting date/time information from text.
• Enhanced text processing capabilities for various applications.
• Standardized approach for training and evaluating classifiers in text processing tasks.

Original Abstract Submitted

Methods and systems for text processing include building a knowledge base using column names and associated functions from a code base. Classifiers are trained using the knowledge base and are cross-validated to determine accuracy scores. Text is processed using a selected classifier having a highest accuracy score from the classifiers to determine date/time features.