Abstract
Bag-of-Concepts, a model that counts the frequency of clustered word embeddings (i.e., concepts) in a document, has demonstrated the feasibility of leveraging clustered word embeddings to create features for document representation. However, information is lost as the word embeddings themselves are not used in the resulting feature vector. This paper presents a novel text representation method, Vectors of Locally Aggregated Concepts (VLAC). Like Bag-of-Concepts, it clusters word embeddings for its feature generation. However, instead of counting the frequency of clustered word embeddings, VLAC takes each cluster’s sum of residuals with respect to its centroid and concatenates those to create a feature vector. The resulting feature vectors contain more discriminative information than Bag-of-Concepts due to the additional inclusion of these first order statistics. The proposed method is tested on four different data sets for single-label classification and compared with several baselines, including TF-IDF and Bag-of-Concepts. Results indicate that when combining features of VLAC with TF-IDF significant improvements in performance were found regardless of which word embeddings were used.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Retrieved from https://code.google.com/archive/p/word2vec/.
- 2.
Retrieved from https://nlp.stanford.edu/projects/glove/.
- 3.
Retrieved from http://nilc.icmc.usp.br/embeddings.
- 4.
Code and results of this study can be found at https://github.com/MaartenGr/VLAC.
References
Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: NetVLAD: CNN architecture for weakly supervised place recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5297–5307 (2016)
Arandjelovic, R., Zisserman, A.: All about VLAD. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1578–1585 (2013)
Arora, S., Liang, Y., Ma, T.: A simple but tough-to-beat baseline for sentence embeddings. In: International Conference for Learning Representations (2017)
Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M.: The balanced accuracy and its posterior distribution. In: Proceedings of the 20th International Conference on Pattern Recognition, pp. 3121–3124. IEEE (2010)
Cardoso-Cachopo, A.: Improving methods for single-label text categorization. Ph.D thesis, Instituto Superior Tecnico, Universidade Tecnica de Lisboa (2007)
Dai, A.M., Le, Q.V.: Semi-supervised sequence learning. In: Advances in Neural Information Processing Systems, pp. 3079–3087 (2015)
Delhumeau, J., Gosselin, P.H., Jégou, H., Pérez, P.: Revisiting the VLAD image representation. In: Proceedings of the 21st International Conference on Multimedia, pp. 653–656. ACM (2013)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Jegou, H., Douze, M., Schmid, C., Pérez, P.: Aggregating local descriptors into a compact image representation. In: Computer Vision and Pattern Recognition, pp. 3304–3311. IEEE (2010)
Jegou, H., Perronnin, F., Douze, M., Sánchez, J., Perez, P., Schmid, C.: Aggregating local image descriptors into compact codes. Trans. Pattern Anal. Mach. Intell. 34(9), 1704–1716 (2012)
Joachims, T.: A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In: International Conference on Machine Learning, pp. 143–151 (1996)
Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683
Kelleher, J.D., Mac Namee, B., D’Arcy, A.: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. MIT Press, Cambridge (2015)
Kim, H.K., Kim, H., Cho, S.: Bag-of-concepts: comprehending document representation through clustering words in distributed representation. Neurocomputing 266, 336–352 (2017)
Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 142–150 (2011)
McCallum, A., Nigam, K., et al.: A comparison of event models for Naive Bayes text classification. In: AAAI-98 Workshop on Learning for Text Categorization, vol. 752, pp. 41–48. Citeseer (1998)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Nowak, E., Jurie, F., Triggs, B.: Sampling strategies for bag-of-features image classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 490–503. Springer, Heidelberg (2006). https://doi.org/10.1007/11744085_38
Pedregosa, F., et al.: Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532–1543 (2014)
Perronnin, F., Sánchez, J., Mensink, T.: Improving the Fisher Kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15561-1_11
Peters, M.E., et al.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)
Picard, D., Gosselin, P.H.: Improving image similarity with vectors of locally aggregated tensors. In: International Conference on Image Processing, pp. 669–672. IEEE (2011)
Ramos, J., et al.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242, pp. 133–142 (2003)
Ramyachitra, D., Manikandan, P.: Imbalanced dataset classification and solutions: a review. Int. J. Comput. Bus. Res. 5(4), 1–29 (2014)
Wallach, H.M.: Topic modeling: beyond bag-of-words. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 977–984. ACM (2006)
Yang, J., Jiang, Y.G., Hauptmann, A.G., Ngo, C.W.: Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the International Workshop on Workshop on Multimedia Information Retrieval, pp. 197–206. ACM (2007)
Zhang, Y., Jin, R., Zhou, Z.H.: Understanding bag-of-words model: a statistical framework. Int. J. Mach. Learn. and Cybern. 1(1–4), 43–52 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Grootendorst, M., Vanschoren, J. (2020). Beyond Bag-of-Concepts: Vectors of Locally Aggregated Concepts. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Lecture Notes in Computer Science(), vol 11907. Springer, Cham. https://doi.org/10.1007/978-3-030-46147-8_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-46147-8_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46146-1
Online ISBN: 978-3-030-46147-8
eBook Packages: Computer ScienceComputer Science (R0)