Extended Pre-Processing Pipeline For Text Classification: On the Role of Meta-Features, Sparsification and Selective Sampling
Resumo
Pipelines for Text Classification are sequences of tasks needed to be performed to classify documents. The pre-processing phase of these pipelines involves different ways of manipulating documents for the learning phase. This Master Thesis introduces three new steps into the traditional pre-processing phase: 1) Meta-Features Generation; 2) Sparsification; and 3) Selective Sampling. Our experimental results, based on more than 5.600 measurements, show that our proposal can achieve significant gains in effectiveness when compared to the traditional TF-IDF representation (up to 52%) and word embeddings (up to 46%), at a much lower cost (9.7x faster). Our Master Thesis also includes a thorough and rigorous evaluation of the trade-offs between cost and effectiveness associated with the introduction of these new steps into the pipeline, as well as a comprehensive comparative experimental evaluation of many alternatives. This thesis falls under the topics of (i) Document Management and Classification, (ii) Information Retrieval Models and Techniques, (iii) and Text Database of the SBBD Call for Papers.
Palavras-chave:
pipeline, preprocessing, text classification, sampling
Referências
B.-Naranjo, M., Martínez-Merino, L. I., and Rodríguez-Chía, A. M. (2021). A robust svm-based approach with feat. selection and outliers detection for classification problems. Expert Systems with Applications.
Canuto, S., Sousa, D. X., Gonçalves, M. A., and Rosa, T. C. (2018). A thorough evaluation of distancebased meta-features for automated text classification. IEEE TKDE.
Cunha,W., Canuto, S., Viegas, F., Salles, T., Gomes, C., Mangaravite, V., Resende, E., Rosa, T., Gonçalves, M., and Rocha, L. (2020). Extended pre-processing pipeline for text classification: On the role of metafeature representations, sparsification and selective sampling. IP&M.
Cunha, W., Mangaravite, V., Gomes, C., Canuto, S., Resende, E., Nascimento, C., Viegas, F., França, C., Martins, W. S., Almeida, J. M., Rosa, T., Rocha, L., and Gonçalves, M. A. (2021). On the cost-effectiveness of neural and non-neural approaches and representations for text classification. IP&M.
Cunha, W., Viegas, F., Alencar, R., Mourão, F., Salles, T., Carvalho, D., Gonçalves, M. A., and Rocha, L. (2018). A feature-oriented sentiment rating for mobile app reviews. In WWW’18.
Dacrema, M. F., Cremonesi, P., and Jannach, D. (2019). Are we really making much progress? In RecSys.
Kastrati, Z., Imran, A. S., and Yayilgan, S. Y. (2019). The impact of deep learning on document classification using semantically rich representations. IP&M.
Louppe, G., Wehenkel, L., Sutera, A., and Geurts, P. (2013). Understanding variable importances in forests of randomized trees. In Neural Information Processing Systems NIPS’13.
Mendes, L. F., Gonçalves, M., Cunha, W., Rocha, L., Couto-Rosa, T., and Martins, W. (2020). “Keep it simple, lazy” – MetaLazy: A new MetaStrategy for lazy text Classification. In ACM CIKM’20.
Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., and Joulin, A. (2018). Advances in pre-training distributed word representations. In International Conf. on Language Resources and Evaluation LREC’18.
Schoenfeld, B., Giraud-Carrier, C. G., Poggemann, M., Christensen, J., and Seppi, K. D. (2018). Preprocessor selection for machine learning pipelines. CoRR, abs/1810.09942.
Viegas, F., Canuto, S., Gomes, C., Luiz, W., Rosa, T., Ribas, S., Rocha, L., and Gonçalves, M. A. (2019). Cluwords: Exploiting semantic word clustering representation for enhanced topic modeling. In WSDM.
Viegas, F., Cunha, W., Gomes, C., Pereira, A., Rocha, L., and Goncalves, M. (2020). CluHTM. In ACL’20.
Viegas, F., Luiz, W., Gomes, C., Khatibi, A., Canuto, S., Mourão, F., Salles, T., Rocha, L., and Gonçalves, M. A. (2018). Semantically-enhanced topic modeling. In ACM CIKM’18.
Zamani, H., Dehghani, M., Croft, W. B., Learned-Miller, E., and Kamps, J. (2018). From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing. In CIKM’18.
Canuto, S., Sousa, D. X., Gonçalves, M. A., and Rosa, T. C. (2018). A thorough evaluation of distancebased meta-features for automated text classification. IEEE TKDE.
Cunha,W., Canuto, S., Viegas, F., Salles, T., Gomes, C., Mangaravite, V., Resende, E., Rosa, T., Gonçalves, M., and Rocha, L. (2020). Extended pre-processing pipeline for text classification: On the role of metafeature representations, sparsification and selective sampling. IP&M.
Cunha, W., Mangaravite, V., Gomes, C., Canuto, S., Resende, E., Nascimento, C., Viegas, F., França, C., Martins, W. S., Almeida, J. M., Rosa, T., Rocha, L., and Gonçalves, M. A. (2021). On the cost-effectiveness of neural and non-neural approaches and representations for text classification. IP&M.
Cunha, W., Viegas, F., Alencar, R., Mourão, F., Salles, T., Carvalho, D., Gonçalves, M. A., and Rocha, L. (2018). A feature-oriented sentiment rating for mobile app reviews. In WWW’18.
Dacrema, M. F., Cremonesi, P., and Jannach, D. (2019). Are we really making much progress? In RecSys.
Kastrati, Z., Imran, A. S., and Yayilgan, S. Y. (2019). The impact of deep learning on document classification using semantically rich representations. IP&M.
Louppe, G., Wehenkel, L., Sutera, A., and Geurts, P. (2013). Understanding variable importances in forests of randomized trees. In Neural Information Processing Systems NIPS’13.
Mendes, L. F., Gonçalves, M., Cunha, W., Rocha, L., Couto-Rosa, T., and Martins, W. (2020). “Keep it simple, lazy” – MetaLazy: A new MetaStrategy for lazy text Classification. In ACM CIKM’20.
Mikolov, T., Grave, E., Bojanowski, P., Puhrsch, C., and Joulin, A. (2018). Advances in pre-training distributed word representations. In International Conf. on Language Resources and Evaluation LREC’18.
Schoenfeld, B., Giraud-Carrier, C. G., Poggemann, M., Christensen, J., and Seppi, K. D. (2018). Preprocessor selection for machine learning pipelines. CoRR, abs/1810.09942.
Viegas, F., Canuto, S., Gomes, C., Luiz, W., Rosa, T., Ribas, S., Rocha, L., and Gonçalves, M. A. (2019). Cluwords: Exploiting semantic word clustering representation for enhanced topic modeling. In WSDM.
Viegas, F., Cunha, W., Gomes, C., Pereira, A., Rocha, L., and Goncalves, M. (2020). CluHTM. In ACL’20.
Viegas, F., Luiz, W., Gomes, C., Khatibi, A., Canuto, S., Mourão, F., Salles, T., Rocha, L., and Gonçalves, M. A. (2018). Semantically-enhanced topic modeling. In ACM CIKM’18.
Zamani, H., Dehghani, M., Croft, W. B., Learned-Miller, E., and Kamps, J. (2018). From neural re-ranking to neural ranking: Learning a sparse representation for inverted indexing. In CIKM’18.
Publicado
04/10/2021
Como Citar
CUNHA, Washington; ROCHA, Leonardo; GONÇALVES, Marcos A..
Extended Pre-Processing Pipeline For Text Classification: On the Role of Meta-Features, Sparsification and Selective Sampling. In: CONCURSO DE TESES E DISSERTAÇÕES (CTDBD) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 36. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 165-170.
DOI: https://doi.org/10.5753/sbbd_estendido.2021.18180.
