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Automatic detection of semantic primitives using optimization based on genetic algorithm.

Yevhen Kostiuk¹, Obdulia Pichardo-Lagunas², Anton Malandii³

¹Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City, Mexico.

Peerj. Computer Science

|June 22, 2023

Summary

This summary is machine-generated.

We developed a novel method to automatically extract semantic primitive words from dictionaries using a genetic algorithm and PageRank. This approach improves upon existing techniques for semantic analysis.

Keywords:

Computational lexicography Differential evolution Explanatory dictionary Lexicography Natural language processing PageRank Semantic primes Semantic primitives

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Area of Science:

Computational Linguistics
Natural Language Processing
Lexicography

Background:

Semantic primitives are fundamental units of meaning.
Extracting these primitives from large dictionaries is challenging.
Existing methods lack efficiency and accuracy.

Purpose of the Study:

To propose an automated method for retrieving semantic primitive words.
To introduce a new evaluation procedure for the retrieved primitives.
To improve the state-of-the-art in semantic primitive extraction.

Main Methods:

Representing dictionaries as directed graphs.
Utilizing a genetic algorithm with PageRank for optimization.
Defining the problem as a constrained subset selection task.
Minimizing PageRank scores while adhering to cardinality limits and cycle prevention.

Main Results:

Successfully retrieved sets of semantic primitive words from WordNet.
The proposed method demonstrates improved performance over previous approaches.
The evaluation procedure validates the quality of the extracted primitives.

Conclusions:

The developed method offers an effective and efficient way to automatically identify semantic primitives.
This work advances the field of computational lexicography.
The approach is adaptable to different dictionaries and languages.