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Related Experiment Video

Updated: Apr 27, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

A grammar-based semantic similarity algorithm for natural language sentences.

Ming Che Lee¹, Jia Wei Chang², Tung Cheng Hsieh³

¹Department of Computer and Communication Engineering, Ming Chuan University, Taoyuan 333, Taiwan.

Thescientificworldjournal

|July 2, 2014

Summary

This study introduces a novel sentence similarity algorithm using corpus-based ontology and grammar rules. It significantly improves natural language understanding for texts lacking obvious conceptual overlap.

Related Experiment Videos

Last Updated: Apr 27, 2026

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Experimental Paradigm for Measuring the Effect of Induced Emotion on Grammar Learning

Published on: January 29, 2020

Area of Science:

Natural Language Processing
Computational Linguistics
Information Retrieval

Background:

Traditional information retrieval methods struggle with sentence similarity when conceptual overlap is minimal.
Existing approaches like vector models and ontology-based methods have limitations in accurately matching sentences with arbitrary structures.

Purpose of the Study:

To propose a novel sentence similarity algorithm for natural language.
To address the limitations of traditional methods in identifying semantic similarity between sentences lacking explicit concept overlap.

Main Methods:

Developed a similarity algorithm leveraging corpus-based ontology and grammatical rules.
Utilized a grammar and semantic corpus for training and evaluation.

Main Results:

The proposed algorithm demonstrates significant performance improvements on benchmark datasets.
Achieved enhanced accuracy in matching sentences and short texts with diverse syntax and structure.

Conclusions:

The grammar and semantic corpus-based algorithm offers a robust solution for natural language sentence similarity.
This approach effectively overcomes limitations of traditional methods, particularly for texts with no obvious semantic relation.