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A novel embedding approach to learn word vectors by weighting semantic relations: SemSpace

  • Published:2021-10 Issue: Volume:180 Page:115146
  • ISSN:0957-4174
  • Container-title:Expert Systems with Applications
  • language:en
  • Short-container-title:Expert Systems with Applications

Author:

Orhan UmutORCID,Tulu Cagatay NeftaliORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference43 articles.

1. Bilingual embeddings and word alignments for translation quality estimation;Abdelsalam,2016

2. Semantic text classification: a survey of past and recent advances;Altinel;Information Processing & Management,2018

3. Uncovering divergent linguistic information in word embeddings with lessons for intrinsic and extrinsic evaluation;Artetxe,2018

4. ITect:Scalable Information Theoretic Similarity for Malware Detection;Bhattacharya,2016

5. Enriching word vectors with subword information;Bojanowski;Transactions of the Association for Computational Linguistics,2017

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1. Extraction of the essential elements for urban systems modelling – A word-to-vector approach;City and Environment Interactions;2024-12

2. Semantic similarity and mutual information-based model for fake news detection;Multimedia Tools and Applications;2024-08-10

3. Application and research of English composition tangent model based on unsupervised semantic space;Journal of Intelligent Systems;2024-01-01

4. Spatial versus graphical representation of distributional semantic knowledge.;Psychological Review;2023-11-13

5. A Study on Different Methods in Sentiment Analysis from Text;2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA);2023-08-03
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