Summary of Homonym Sense Disambiguation in the Georgian Language, by Davit Melikidze et al.
Homonym Sense Disambiguation in the Georgian Language
by Davit Melikidze, Alexander Gamkrelidze
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel approach to Word Sense Disambiguation (WSD) in the Georgian language, leveraging supervised fine-tuning of a pre-trained Large Language Model (LLM). The approach utilizes a filtered dataset from the Georgian Common Crawls corpus to train a classifier for words with multiple senses. Additionally, experimental results demonstrate the effectiveness of Long Short-Term Memory (LSTM) networks for WSD tasks. Accurately disambiguating homonyms is crucial in natural language processing, particularly in agglutinative languages like Georgian, where unique challenges arise. The proposed techniques achieve 95% accuracy for predicting lexical meanings of homonyms using a hand-classified dataset of over 7500 sentences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem called Word Sense Disambiguation (WSD) in the Georgian language. WSD is important because it helps computers understand words that have multiple meanings. The researchers used a special kind of AI model called a Large Language Model to help solve this problem. They created a dataset by filtering through a big collection of text and then trained a classifier to recognize words with different meanings. The results show that their approach was able to accurately identify the meaning of over 95% of words tested. |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Lstm » Natural language processing » Supervised
