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Register a new userText Document Clustering: Wordnet vs. TF-IDF vs. Word Embeddings
تجميع المستندات النصية: WordNet VS. TF-IDF مقابل Word Embeddings
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في الورقة، نتعامل مع مشكلة تجميع وثائق النص غير المدعومة باللغة البولندية.هدفنا هو مقارنة النهج الحديثة بناء على نمذجة اللغة (DOC2VEC و BERT) مع تلك الكلاسيكية، I.E.، TF-IDF و WordNet-تتم التجارب على ثلاث مجموعات بيانات تحتوي على أوصاف مؤهلات.أظهرت نتائج التجارب أن تدابير التشابه القائمة على WordNet يمكن أن تنافس وحتى التوفيق بين النهج القائمة على التضمين.
In the paper, we deal with the problem of unsupervised text document clustering for the Polish language. Our goal is to compare the modern approaches based on language modeling (doc2vec and BERT) with the classical ones, i.e., TF-IDF and wordnet-based. The experiments are conducted on three datasets containing qualification descriptions. The experiments' results showed that wordnet-based similarity measures could compete and even outperform modern embedding-based approaches.
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