Abstract
Concept extraction from text is a key step in concept-level text analysis. In this chapter, 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 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cambria E, Gastaldo P, Bisio F, Zunino R (2015) An ELM-based model for affective analogical reasoning. Neurocomputing 149:443–455
Cambria E, Hussain A (2015) Sentic computing: a common-sense-based framework for concept-level sentiment analysis, vol 1. Springer
Poria S, Gelbukh A, Cambria E, Yang P, Hussain A, Durrani T (2012) Merging senticnet and wordnet-affect emotion lists for sentiment analysis. In: 2012 IEEE 11th International Conference on Signal Processing (ICSP), vol 2. IEEE, pp 1251–1255
Poria S, Gelbukh A, Hussain A, Howard N, Das D, Bandyopadhyay S (2013) Enhanced senticnet with affective labels for concept-based opinion mining. IEEE Intell Syst 28(2): 31–38
Rajagopal D, Cambria E, Olsher D, Kwok K (2013) A graph-based approach to commonsense concept extraction and semantic similarity detection. In: Proceedings of the 22nd International Conference on World Wide Web. ACM, pp 565–570
Socher R, Perelygin A, Wu JY, Chuang J, Manning CD, Ng AY, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), vol 1631. Citeseer, p 1642
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Poria, S., Hussain, A., Cambria, E. (2018). Concept Extraction from Natural Text for Concept Level Text Analysis. In: Multimodal Sentiment Analysis. Socio-Affective Computing, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-95020-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-95020-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95018-1
Online ISBN: 978-3-319-95020-4
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)