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Coelho, J., Neto, A., Tavares, M., Coutinho, C., Ribeiro, R. & Batista, F. (2021). Semantic search of mobile applications using word embeddings. In Queirós. R., Pinto, M., Simões, A., Portela, F., & Pereira, M. J. (Ed.), 10th Symposium on Languages, Applications and Technologies (SLATE 2021). Vila do Conde/Póvoa de Varzim: Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing.

J. Coelho et al.,  "Semantic search of mobile applications using word embeddings", in 10th Symp. on Languages, Applications and Technologies (SLATE 2021), Queirós. R., Pinto, M., Simões, A., Portela, F., & Pereira, M. J., Ed., Vila do Conde/Póvoa de Varzim, Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2021, vol. 94

@inproceedings{coelho2021_1734866174894,
	author = "Coelho, J. and Neto, A. and Tavares, M. and Coutinho, C. and Ribeiro, R. and Batista, F.",
	title = "Semantic search of mobile applications using word embeddings",
	booktitle = "10th Symposium on Languages, Applications and Technologies (SLATE 2021)",
	year = "2021",
	editor = "Queirós. R., Pinto, M., Simões, A., Portela, F., & Pereira, M. J.",
	volume = "94",
	number = "",
	series = "",
	doi = "10.4230/OASIcs.SLATE.2021.12",
	publisher = "Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing",
	address = "Vila do Conde/Póvoa de Varzim",
	organization = "Escola Superior de Media Artes e Design do Instituto Politécnico do Porto",
	url = "http://slate-conf.org/2021/home"
}

TY  - CPAPER
TI  - Semantic search of mobile applications using word embeddings
T2  - 10th Symposium on Languages, Applications and Technologies (SLATE 2021)
VL  - 94
AU  - Coelho, J.
AU  - Neto, A.
AU  - Tavares, M.
AU  - Coutinho, C.
AU  - Ribeiro, R.
AU  - Batista, F.
PY  - 2021
SN  - 2190-6807
DO  - 10.4230/OASIcs.SLATE.2021.12
CY  - Vila do Conde/Póvoa de Varzim
UR  - http://slate-conf.org/2021/home
AB  - This paper proposes a set of approaches for the semantic search of mobile applications, based on their name and on the unstructured textual information contained in their description. The proposed approaches make use of word-level, character-level, and contextual word-embeddings that have been trained or fine-tuned using a dataset of about 500 thousand mobile apps, collected in the scope of this work. The proposed approaches have been evaluated using a public dataset that includes information about 43 thousand applications, and 56 manually annotated non-exact queries. Our results show that both character-level embeddings trained on our data, and fine-tuned RoBERTa models surpass the performance of the other existing retrieval strategies reported in the literature.
ER  -