| Autor: |
Altan Cakir, Mert Gurkan |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Machine Learning with Applications, Vol 14, Iss , Pp 100509- (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2666-8270 |
| DOI: |
10.1016/j.mlwa.2023.100509 |
| Popis: |
This work addresses an alternative approach for query expansion (QE) using a generative adversarial network (GAN) to enhance the effectiveness of information search in e-commerce. We propose a modified QE conditional GAN (mQE-CGAN) framework, which resolves keywords by expanding the query with a synthetically generated query that proposes semantic information from text input. we train a sequence-to-sequence transformer model as the generator to produce keywords and use a recurrent neural network model as the discriminator to classify an adversarial output with the generator. with the modified CGAN framework, Various forms of semantic insights gathered from the query-document corpus are introduced to the generation process. We leverage these insights as conditions for the generator model and discuss their effectiveness for the query expansion task. our experiments demonstrate that the utilization of condition structures within the mQE-CGAN framework can increase the semantic similarity between generated sequences and reference documents up to nearly 10% compared to baseline models. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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