This research delves into approaches for identifying semantic links in natural language by analyzing how symbols, concepts, and patterns are close to each other.Two computational techniques were applied: cosine similarity and TF-IDF.Cosine similarity, using a text-word matrix, assessed how often terms appear together within the corpus, while TF-IDF highlighted terms that are both frequent and significant in context.The outcomes were visualized as semantic networks, indicating weighted connections among words.To illustrate, in the sports domain, sportsmen (0.59), badminton (0.49), culture (0.48), and physical education (0.48) adduced the strongest associations with the main concept "sport."These networks grouped terms into categories such as participants, sporting disciplines, related fields, cultural components, and mobility activities.The study ensures that integrating cosine similarity and TF-IDF effectively captures contextual meaning and supports the development of semantic models useful for machine translation, information retrieval, and natural language processing.
| Jurnal | ТАТУ хабарлари |
|---|---|
| Nashr sanasi | 2024-09-09 |
| DOI | 10.61663/242tuitmct65 |
DOI: 10.61663/242tuitmct65 · Maqolaning asl sahifasi
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