Abstract
Concept-level text analysis is superior to word-level analysis as it preserves the semantics associated with multi-word expressions. It offers a better understanding of text and helps to significantly increase the accuracy of many text mining tasks. Concept extraction from text is a key step in concept-level text analysis. In this paper, we propose a ConceptNet-based semantic parser that deconstructs natural language text into concepts based on the dependency relation between clauses. Our approach is domain-independent and is able to extract concepts from heterogeneous text. Through this parsing technique, 92.21% accuracy was obtained on a dataset of 3,204 concepts. We also show experimental results on three different text analysis tasks, on which the proposed framework outperformed state-of-the-art parsing techniques.
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Agirre, E., Ansa, O., Hovy, E., Martínez, D.: Enriching very large ontologies using the www. arXiv preprint cs/0010026 (2000)
Cambria, E., Gastaldo, P., Bisio, F., Zunino, R.: An ELM-based model for affective analogical reasoning. Neurocomputing (2014)
Cambria, E., Hussain, A., Havasi, C., Eckl, C., Munro, J.: Towards crowd validation of the uk national health service. In: WebSci, Raleigh (2010)
Cambria, E., White, B.: Jumping NLP curves: A review of natural language processing research. IEEE Computational Intelligence Magazine 9(2) (2014)
Cao, C., Feng, Q., Gao, Y., Gu, F., Si, J., Sui, Y., Tian, W., Wang, H., Wang, L., Zeng, Q., et al.: Progress in the development of national knowledge infrastructure. Journal of Computer Science and Technology 17(5), 523–534 (2002)
Çelebi, A., Özgür, A.: N-gram parsing for jointly training a discriminative constituency parser. Polibits 48, 5–12 (2013)
Chen, W.L., Zhu, J.B., Yao, T.S., Zhang, Y.X.: Automatic learning field words by bootstrapping. In: Proc. of the JSCL, vol. 72. Tsinghua University Press, Beijing (2003)
De Marneffe, M.-C., Manning, C.D.: The stanford typed dependencies representation. In: Coling 2008: Proceedings of the Workshop on Cross-Framework and Cross-Domain Parser Evaluation, pp. 1–8. Association for Computational Linguistics (2008)
Du, B., Tian, H.F., Wang, L., Lu, R.Z.: Design of domain-specific term extractor based on multi-strategy. Computer Engineering 31(14), 159–160 (2005)
Gelfand, B., Wulfekuler, M., Punch, W.F.: Automated concept extraction from plain text. In: AAAI 1998 Workshop on Text Categorization, pp. 13–17 (1998)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, pp. 1–12 (2009)
Harlambous, Y., Klyuev, V.: Thematically reinforced explicit semantic analysis. International Journal of Computational Linguistics and Applications 4(1), 79–94 (2013)
Havasi, C., Speer, R., Alonso, J.: Conceptnet 3: A flexible, multilingual semantic network for common sense knowledge. In: Recent Advances in Natural Language Processing, pp. 27–29 (2007)
Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: Proceedings of the 14th Conference on Computational Linguistics, vol. 2, pp. 539–545. Association for Computational Linguistics (1992)
Howard, N., Cambria, E.: Intention awareness: Improving upon situation awareness in human-centric environments. Human-centric Computing and Information Sciences 3(9) (2013)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: Theory and applications. Neurocomputing 70(1), 489–501 (2006)
Liu, L., Cao, C., Wang, H.: Acquiring hyponymy relations from large chinese corpus. WSEAS Transactions on Business and Economics 2(4), 211 (2005)
Liu, L., Cao, C.-G., Wang, H.-T., Chen, W.: A method of hyponym acquisition based on “isa” pattern. Journal of Computer Science, 146–151 (2006)
Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K.: Using linguistic cues for the automatic recognition of personality in conversation and text. J. Artif. Intell. Res. (JAIR) 30, 457–500 (2007)
Nakata, K., Voss, A., Juhnke, M., Kreifelts, T.: Collaborative concept extraction from documents. In: Proceedings of the 2nd Int. Conf. on Practical Aspects of Knowledge Management (PAKM 1998). Citeseer (1998)
Paice, C.D.: Another stemmer. ACM SIGIR Forum 24(3), 56–61 (1990)
Poria, S., Cambria, E., Hussain, A., Bin, G.-H.: Big multimodal data analysis. Elsevier Neural Networks (2014)
Poria, S., Gelbukh, A., Agarwal, B., Cambria, E., Howard, N.: Common sense knowledge based personality recognition from text. In: Castro, F., Gelbukh, A., González, M. (eds.) MICAI 2013, Part II. LNCS, vol. 8266, pp. 484–496. Springer, Heidelberg (2013)
Poria, S., Gelbukh, A., Cambria, E., Das, D., Bandyopadhyay, S.: Enriching senticnet polarity scores through semi-supervised fuzzy clustering. In: 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), pp. 709–716. IEEE (2012)
Poria, S., Gelbukh, A., Cambria, E., Yang, P., Hussain, A., Durrani, T.: Merging senticnet and wordnet-affect emotion lists for sentiment analysis. In: 2012 IEEE 11th International Conference on Signal Processing (ICSP), vol. 2, pp. 1251–1255. IEEE (2012)
Poria, S., Gelbukh, A., Hussain, A., Howard, N., Das, D., Bandyopadhyay, S.: Enhanced senticnet with affective labels for concept-based opinion mining. IEEE Intelligent Systems 28(2), 31–38 (2013)
Poria, S., Winterstein, G., Cambria, E., Gelbukh, A., Hussain, A., Bin, G.-H.: Dependency-based rules for concept-level sentiment analysis. Elsevier Knowledge-Based Systems (2014)
Rajagopal, D., Cambria, E., Olsher, D., Kwok, K.: A graph-based approach to commonsense concept extraction and semantic similarity detection. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 565–570. International World Wide Web Conferences Steering Committee (2013)
Ramirez, P.M., Mattmann, C.A.: Ace: Improving search engines via automatic concept extraction. In: Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration (IRI 2004), pp. 229–234. IEEE (2004)
Sidorov, G.: Non-continuous syntactic n-grams. Polibits 48, 67–75 (2013)
Sidorov, G.: Syntactic dependency based n-grams in rule based automatic English as second language grammar correction. International Journal of Computational Linguistics and Applications 4(2), 169–188 (2013)
Sidorov, G., Velasquez, F., Stamatatos, E., Gelbukh, A., Chanona-Hernández, L.: Syntactic n-grams as machine learning features for natural language processing. Expert Systems with Applications 41(3), 853–860 (2014)
Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: Conference on Empirical Methods in Natural Language Processing (EMNLP). EMNLP (2013)
Velardi, P., Fabriani, P., Missikoff, M.: Using text processing techniques to automatically enrich a domain ontology. In: Proceedings of the International Conference on Formal Ontology in Information Systems, vol. 2001, pp. 270–284. ACM (2001)
Yuntao, Z., Ling, G., Yongcheng, W., Zhonghang, Y.: An effective concept extraction method for improving text classification performance. Geo-Spatial Information Science 6(4), 66–72 (2003)
Zheng, J.H., Lu, J.L.: Study of an improved keywords distillation method. Computer Engineering 31(18), 194–196 (2005)
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Poria, S., Agarwal, B., Gelbukh, A., Hussain, A., Howard, N. (2014). Dependency-Based Semantic Parsing for Concept-Level Text Analysis. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8403. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54906-9_10
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DOI: https://doi.org/10.1007/978-3-642-54906-9_10
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