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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Multidimensional Latent Semantic Networks for Text Humor Recognition.

Siqi Xiong1,2, Rongbo Wang1,2, Xiaoxi Huang1,2

  • 1College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China.

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|July 28, 2022
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Summary

This study introduces a novel humor recognition model (MLSN) for artificial intelligence. The model accurately identifies humor in text by analyzing semantic features, advancing natural language processing and discourse understanding.

Keywords:
deep learningdiscourse understandinghuman-computer interactionhumor recognitionhumorous semantic features

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Humor is a complex human expression vital for communication, conveying nuanced emotions.
  • Current artificial intelligence research in discourse understanding lacks robust humor recognition capabilities.
  • Developing AI that can understand humor is a significant challenge in natural language processing.

Purpose of the Study:

  • To propose a novel humor recognition model (MLSN) for identifying humorous expressions in text.
  • To leverage humor theory and deep learning techniques for enhanced AI understanding of language.
  • To improve the accuracy of AI systems in recognizing and interpreting jokes.

Main Methods:

  • Developed a humor recognition model (MLSN) integrating humor theory and deep learning.
  • The model captures semantic features like inconsistency, phonetic characteristics, and ambiguity.
  • Utilized three publicly available wisecrack datasets for model training and evaluation.

Main Results:

  • The MLSN model demonstrated superior humor recognition accuracy compared to state-of-the-art language models.
  • Experimental results validated the model's effectiveness on diverse humor datasets.
  • The model successfully identified humor by analyzing key semantic features.

Conclusions:

  • The proposed MLSN model offers a significant advancement in humor recognition for AI.
  • This research contributes to the broader field of discourse understanding and natural language processing.
  • The model's ability to capture humor's semantic nuances enhances AI's communicative capabilities.