Skip to main content
Top

2019 | OriginalPaper | Chapter

Short Text Similarity Measurement Based on Coupled Semantic Relation and Strong Classification Features

Authors : Huifang Ma, Wen Liu, Zhixin Li, Xianghong Lin

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Measuring the similarity between short texts is made difficult by the fact that two texts that are semantically related may not contain any words in common. In this paper, we propose a novel short text similarity measure which aggregates coupled semantic relation (CSR) and strong classification features (SCF) to provide a richer semantic context. On the one hand, CSR considers both intra-relation (i.e. co-occurrence of terms based on the modified weighting strategy) and inter-relation (i.e. dependency of terms via paths that connect linking terms) between a pair of terms. On the other hand, Based on SCF for similarity measure is established based on the idea that the more similar two texts are, the more features of strong classification they share. Finally, we combine the above two techniques to address the semantic sparseness of short text. We carry out extensive experiments on real world short texts. The results demonstrate that our method significantly outperforms baseline methods on several evaluation metrics.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Li, C.L., Wang, H.R., Zhang, Z.Q., Sun, A.: Topic modeling for short texts with auxiliary word embeddings. In: 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 17–21. SIGIR, Pisa (2016) Li, C.L., Wang, H.R., Zhang, Z.Q., Sun, A.: Topic modeling for short texts with auxiliary word embeddings. In: 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 17–21. SIGIR, Pisa (2016)
2.
go back to reference Wang, C., Song, Y., Elkishky, A., Zhang, M.: Incorporating world knowledge to document clustering via heterogeneous information networks. In: 21th Knowledge Discovery and Data Mining. KDD, pp. 1215–1224. ACM, Sydney (2015) Wang, C., Song, Y., Elkishky, A., Zhang, M.: Incorporating world knowledge to document clustering via heterogeneous information networks. In: 21th Knowledge Discovery and Data Mining. KDD, pp. 1215–1224. ACM, Sydney (2015)
3.
go back to reference Li, P.P., Wang, H., Zhu, K.Q., Wang, Z.: A large probabilistic semantic network based approach to compute term similarity. IEEE Trans. Knowl. Data Eng. 27(10), 2604–2617 (2015)CrossRef Li, P.P., Wang, H., Zhu, K.Q., Wang, Z.: A large probabilistic semantic network based approach to compute term similarity. IEEE Trans. Knowl. Data Eng. 27(10), 2604–2617 (2015)CrossRef
4.
go back to reference Cheng, X., Miao, D., Wang, C., Cao, L.: Coupled term-term relation analysis for document clustering. In: The 2013 International Joint Conference on Neural Networks, pp. 1–8. IEEE, Dallas (2013) Cheng, X., Miao, D., Wang, C., Cao, L.: Coupled term-term relation analysis for document clustering. In: The 2013 International Joint Conference on Neural Networks, pp. 1–8. IEEE, Dallas (2013)
5.
go back to reference Kusner, M.J., Sun, Y., Kolkin, N.I., Weinberger, K.Q.: From word embeddings to document distances. In: 32nd International Conference on International Conference on Machine Learning, pp. 957–966. ICML, Lille (2015) Kusner, M.J., Sun, Y., Kolkin, N.I., Weinberger, K.Q.: From word embeddings to document distances. In: 32nd International Conference on International Conference on Machine Learning, pp. 957–966. ICML, Lille (2015)
6.
7.
go back to reference Chen, Q.Q., Hu, L., Xu, J., Liu, W.: Document similarity analysis via involving both explicit and implicit semantic couplings. In: 2015 International Conference on Data Science and Advanced Analytics, pp. 1–10. IEEE, Paris (2015) Chen, Q.Q., Hu, L., Xu, J., Liu, W.: Document similarity analysis via involving both explicit and implicit semantic couplings. In: 2015 International Conference on Data Science and Advanced Analytics, pp. 1–10. IEEE, Paris (2015)
8.
go back to reference Zhang, L., Gao, Y., Hong, C., Zhu, J.: Feature correlation hypergraph: exploiting high-order potentials for multimodal recognition. IEEE Trans. Cybern. 44(8), 1408–1419 (2017)CrossRef Zhang, L., Gao, Y., Hong, C., Zhu, J.: Feature correlation hypergraph: exploiting high-order potentials for multimodal recognition. IEEE Trans. Cybern. 44(8), 1408–1419 (2017)CrossRef
9.
go back to reference MacKay, D.J.C.: Information Theory, Inference, and Learning Algorithms, 1st edn. Cambridge University Press, Cambridge (2003)MATH MacKay, D.J.C.: Information Theory, Inference, and Learning Algorithms, 1st edn. Cambridge University Press, Cambridge (2003)MATH
12.
go back to reference Liu, W., Ma, H., Tuo, T., Chen, H.B.: Co-occurrence distance and discrimination based similarity measure on short text. Comput. Eng. Sci. 40(7), 1281–1286 (2018) Liu, W., Ma, H., Tuo, T., Chen, H.B.: Co-occurrence distance and discrimination based similarity measure on short text. Comput. Eng. Sci. 40(7), 1281–1286 (2018)
13.
go back to reference Wen, H., Xiao, N.: A semi-supervised text clustering based on strong classification features affinity propagatione. Pattern Recognit. Artif. Intell. 27(7), 646–654 (2014) Wen, H., Xiao, N.: A semi-supervised text clustering based on strong classification features affinity propagatione. Pattern Recognit. Artif. Intell. 27(7), 646–654 (2014)
Metadata
Title
Short Text Similarity Measurement Based on Coupled Semantic Relation and Strong Classification Features
Authors
Huifang Ma
Wen Liu
Zhixin Li
Xianghong Lin
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-16148-4_11

Premium Partner