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LSA Is not Dead: Improving Results of Domain-Specific Information Retrieval System Using Stack Overflow Questions Tags
Abstract
The paper presents the approach to using tags from Stack Overflow questions as a data source in the process of building domain-specific unsupervised term embeddings. Using a huge dataset of Stack Overflow posts, our solution employs the LSA algorithm to learn latent representations of information technology terms. The paper also presents the Teamy.ai system, currently developed by Scalac company, which serves as a platform that helps match IT project inquiries with potential candidates. The heart of the system is the information retrieval module that searches for the best-matching candidates according to the project requirements. In the paper, we used our pre-trained embeddings to enhance the search queries using the query expansion algorithm from the neural information retrieval domain. The proposed solution improves the precision of the retrieval compared to the basic variant without query expansion.
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Authors (5)
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Piotr Malak
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Agnieszka Letowska
- Scalac sp. z o.o.
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Olewniczak S., Szymański J., Malak P., Komar R., Letowska A.: LSA Is not Dead: Improving Results of Domain-Specific Information Retrieval System Using Stack Overflow Questions Tags// / : , 2024,
- DOI:
- Digital Object Identifier (open in new tab) 10.5220/0012358400003636
- Sources of funding:
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- The National Centre for Research and Development within the project POIR.01.01.01-00-0761/20 "System sztucznej inteligencji korelujacy zespoły pracownikow z projektami IT"
- Verified by:
- Gdańsk University of Technology
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