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@MASTERSTHESIS{Rahmdel:1041549,
      author       = {Rahmdel, Sahand},
      title        = {{E}xploring {L}inguistic {P}roximity in {C}4 {M}ultilingual
                      {D}ata through {E}fficient {E}mbedding {M}odel {A}nalysis
                      and {V}isualization on {HPC}},
      school       = {Rheinisch-Westfälische Technische Hochschule Aachen},
      type         = {Bachelorarbeit},
      reportid     = {FZJ-2025-02306},
      pages        = {77 p.},
      year         = {2025},
      note         = {Bachelorarbeit, Rheinisch-Westfälische Technische
                      Hochschule Aachen, 2025},
      abstract     = {This thesis investigates the proximity of different
                      languages and different language families by analysing how
                      multilingual text data are represented in a shared latent
                      space, focusing on the Colossal Clean Crawled Corpus (C4)
                      with a multilingual extension (mC4). The main focus is to
                      determine whether embeddings of different languages group
                      together based on their linguistic families, topical
                      content, or both. This is achieved through a
                      high-performance computing (HPC) system to embed 6.1TB of
                      textual data from 24 diverse languages. The BAAI bge-m3
                      embedding model served to create embeddings of dimension
                      1,024, which were stored in a vector database using ChromaDB
                      to facilitate scalable analysis and querying.Subsequent
                      dimensionality reduction with t-distributed Stochastic
                      Neighbor Embedding (t-SNE) allowed for the visualization of
                      language clusters in two-dimensional space for a simpler and
                      better understanding. Results reveal that similar thematic
                      or topical content often drives the embedding model to
                      generate vectors that lie close together, even from
                      different languages. However, certain clusters reflect
                      linguistic closeness—especially among languages from the
                      same family—indicating that the model also recognizes
                      linguistic features. Overall, the thesis uses multilingual
                      embeddings to check the existence of any relation between
                      the semantic representation of texts as vectors (embeddings)
                      and the linguistic structure of the origin languages,
                      demonstrating how HPC resources, combined with advanced
                      embedding models, can efficiently handle large datasets and
                      offer deeper insights into language proximity and topic
                      similarity analysis.},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)2},
      doi          = {10.34734/FZJ-2025-02306},
      url          = {https://juser.fz-juelich.de/record/1041549},
}