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(2016), which detail that the isotropy has a \u2018\u2018purification\u2019\u2019 effect that mitigates the (rather large) approximation error in the PMI models (Church and Hanks, 1990), and under-scores the power of high-dimensional geometry to retain structure through isotropic regularization in embeddings."]},{"citingPaper":{"authors":[{"authorId":"2305887709","name":"Mustafa Keskin"},{"authorId":"2305892352","name":"Enis Teper"},{"authorId":"9244800","name":"Sinan Ke\u00e7eci"}],"citationStyles":{"bibtex":"@Inproceedings{Keskin2025MatchingWM,\n author = {Mustafa Keskin and Enis Teper and Sinan Ke\u00e7eci},\n booktitle = {International Symposium on INnovations in Intelligent SysTems and Applications},\n title = {Matching What Matters: Using LLMs to Improve Product Compatibility Recommendations},\n year = {2025}\n}\n"},"paperId":"561048c61eceb55456f065196aafde4517a159d6","title":"Matching What Matters: Using LLMs to Improve Product Compatibility Recommendations","venue":"International Symposium on INnovations in Intelligent SysTems and Applications","year":2025},"contexts":["Based on these fundamental statistics, we derive and apply three main filtering steps to systematically determine relevant category links: 1) Positive Pointwise Mutual Information (PMI) Filtering: We compute the PMI [13] for each category pair and retain only those with a PMI greater than zero."]},{"citingPaper":{"authors":[{"authorId":"2386636232","name":"Ming Li"},{"authorId":"2382404283","name":"Yue Xiao"},{"authorId":"2387400865","name":"Shaoheng Ding"},{"authorId":"2305013033","name":"Qingcheng Zhang"},{"authorId":"2358272128","name":"Haitao Xiong"},{"authorId":"2343282836","name":"Jin Ding"}],"citationStyles":{"bibtex":"@Inproceedings{Li2025CrudeOP,\n author = {Ming Li and Yue Xiao and Shaoheng Ding and Qingcheng Zhang and Haitao Xiong and Jin Ding},\n booktitle = {Journal of King Saud University: Computer and Information Sciences},\n title = {Crude oil price fluctuation forecasting incorporating news sentiment based on improved sentiment lexicon},\n year = {2025}\n}\n"},"paperId":"311ef462bab0ece6df77da46d932c59376f14eed","title":"Crude oil price fluctuation forecasting incorporating news sentiment based on improved sentiment lexicon","venue":"Journal of King Saud University: Computer and Information Sciences","year":2025},"contexts":["This measures the proximity between a new word with an unknown oil price forecasting sentiment polarity and a seed word with a known oil price forecasting sentiment polarity, thus determining the oil price forecasting sentiment category to which the new word belongs (Church and Hanks 1989).","3."]},{"citingPaper":{"authors":[{"authorId":"2261318433","name":"A. 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(2016), which details that the isotropy has a \u201cpu-rification\u201d effect that mitigates the (rather large) approximation error in the PMI models (Church and Hanks, 1990), and underscores the power of high-dimensional geometry to retain structure through isotropic regularisation in embeddings."]},{"citingPaper":{"authors":[{"authorId":"79626262","name":"Suci Ramadhani Arifin"},{"authorId":"20515593","name":"A. A. Ilham"},{"authorId":"145284379","name":"I. Areni"}],"citationStyles":{"bibtex":"@Inproceedings{Arifin2025NLPBasedEO,\n author = {Suci Ramadhani Arifin and A. A. Ilham and I. 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