International business machines corporation (20240119239). WORD-TAG-BASED LANGUAGE SYSTEM FOR SENTENCE ACCEPTABILITY JUDGMENT simplified abstract
Contents
- 1 WORD-TAG-BASED LANGUAGE SYSTEM FOR SENTENCE ACCEPTABILITY JUDGMENT
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 WORD-TAG-BASED LANGUAGE SYSTEM FOR SENTENCE ACCEPTABILITY JUDGMENT - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
WORD-TAG-BASED LANGUAGE SYSTEM FOR SENTENCE ACCEPTABILITY JUDGMENT
Organization Name
international business machines corporation
Inventor(s)
Issei Yoshida of Setagaya-ku (JP)
WORD-TAG-BASED LANGUAGE SYSTEM FOR SENTENCE ACCEPTABILITY JUDGMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119239 titled 'WORD-TAG-BASED LANGUAGE SYSTEM FOR SENTENCE ACCEPTABILITY JUDGMENT
Simplified Explanation
Methods and systems for sentence acceptability judgment involve obtaining a word frequency distribution for a predetermined textual data set. A replacement rate is determined based on this distribution, and words with a frequency lower than the replacement rate are replaced with corresponding tags in the training data set. Language models are then trained with the revised text, and the best performing model is selected to rate the acceptability of candidate sentences.
- Word frequency distribution obtained for a textual data set
- Replacement rate determined based on word frequency distribution
- Words with frequency lower than replacement rate replaced with tags in training data set
- Language models trained with revised text
- Best performing model selected to rate acceptability of sentences
Potential Applications
This technology could be applied in:
- Automated essay grading systems
- Natural language processing tools for improving sentence structure
Problems Solved
This technology helps in:
- Enhancing the accuracy of language models
- Improving the quality of automated text evaluation systems
Benefits
The benefits of this technology include:
- Streamlining the process of sentence acceptability judgment
- Enhancing the performance of language models
Potential Commercial Applications
Potential commercial applications of this technology include:
- Educational technology companies
- Text analysis software developers
Possible Prior Art
One possible prior art for this technology could be:
- Existing language modeling techniques used in natural language processing systems
Unanswered Questions
How does this technology compare to existing automated essay grading systems?
This technology focuses on sentence acceptability judgment, which is a specific aspect of essay grading. It may offer more precise evaluation of individual sentences, but its overall impact on essay grading systems is unclear.
What are the limitations of using word frequency distribution for sentence acceptability judgment?
While word frequency distribution can provide valuable insights, it may not capture the nuances of language usage and context. The technology's effectiveness in handling complex sentence structures and diverse writing styles remains to be seen.
Original Abstract Submitted
methods and systems for sentence acceptability judgment. a word frequency distribution for a predetermined textual data set is obtained. a replacement rate is determined based on an obtained word frequency distribution for a predetermined textual data set and every occurrence of a word having a frequency lower than the replacement rate is replaced in text of a training data set with a corresponding tag to generate revised text of the training data set (the training data set comprising at least a portion of the predetermined textual data set). a plurality of language models are trained with the revised text and a best performing trained language model of the plurality of trained language models is selected. an acceptability of each sentence of a plurality of candidate sentences is rated using the selected trained language model and a best sentence of the plurality of candidate sentences is selected based on the ratings.
