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Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models.

, , , and . Insights, page 32-40. Association for Computational Linguistics, (2020)

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An Empirical Analysis of Topic Models: Uncovering the Relationships between Hyperparameters, Document Length and Performance Measures., and . RANLP, page 1408-1416. INCOMA Ltd., (2021)OCTIS 2.0: Optimizing and Comparing Topic Models in Italian Is Even Simpler!, and . CLiC-it, volume 3033 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)OCTIS: Comparing and Optimizing Topic models is Simple!, , , , and . EACL (System Demonstrations), page 263-270. Association for Computational Linguistics, (2021)Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence., , and . ACL/IJCNLP (2), page 759-766. Association for Computational Linguistics, (2021)Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence., , and . CoRR, (2020)Which Matters Most? Comparing the Impact of Concept and Document Relationships in Topic Models., , , and . Insights, page 32-40. Association for Computational Linguistics, (2020)Word Embedding-Based Topic Similarity Measures., , and . NLDB, volume 12801 of Lecture Notes in Computer Science, page 33-45. Springer, (2021)Cross-lingual Contextualized Topic Models with Zero-shot Learning., , , , and . EACL, page 1676-1683. Association for Computational Linguistics, (2021)Cross-lingual Contextualized Topic Models with Zero-shot Learning., , , , and . CoRR, (2020)Contrastive Language-Image Pre-training for the Italian Language., , , , , and . CLiC-it, volume 3596 of CEUR Workshop Proceedings, CEUR-WS.org, (2023)