Abstract
We introduce a crowd-powered approach for the creation of a lexicon for any theme given a set of seed words that cover a variety of concepts within the theme. Terms are initially sorted by automatically clustering their embeddings and subsequently rearranged by crowd workers in order to create a tree structure. This type of organization captures hierarchical relationships between concepts and allows for a tunable level of specificity when using the lexicon to collect measurements from a piece of text. We use a lexicon expansion method to increase the overall coverage of the produced resource. Using our proposed approach, we create a hierarchical lexicon of personal values and evaluate its internal and external consistency. We release this novel resource to the community as a tool for measuring value content within text corpora.
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
Notes
- 1.
This new values lexicon, along with code that can be used to build an initial hierarchy, manage the human-powered sorting, and expand the sorted hierarchy can be found at: http://nlp.eecs.umich.edu/downloads.html.
- 2.
References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Boyd, R.L., Wilson, S.R., Pennebaker, J.W., Kosinski, M., Stillwell, D.J., Mihalcea, R.: Values in words: using language to evaluate and understand personal values. In: ICWSM, pp. 31–40 (2015)
Chang, J., Gerrish, S., Wang, C., Boyd-Graber, J.L., Blei, D.M.: Reading tea leaves: how humans interpret topic models. In: Advances in Neural Information Processing Systems, pp. 288–296 (2009)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, Hoboken (2012)
Fast, E., Chen, B., Bernstein, M.S.: Empath: understanding topic signals in large-scale text. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 4647–4657. ACM (2016)
Graham, J., Haidt, J., Nosek, B.A.: Liberals and conservatives rely on different sets of moral foundations. J. Pers. Soc. Psychol. 96(5), 1029 (2009)
Igo, S.P., Riloff, E.: Corpus-based semantic lexicon induction with web-based corroboration. In: Proceedings of the Workshop on Unsupervised and Minimally Supervised Learning of Lexical Semantics, pp. 18–26. Association for Computational Linguistics (2009)z
Magnini, B., Cavaglia, G.: Integrating subject field codes into wordnet. In: LREC, pp. 1413–1418 (2000)
Mohammad, S.M., Turney, P.D.: Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29(3), 436–465 (2013)
Morstatter, F., Liu, H.: A novel measure for coherence in statistical topic models. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Short Papers), vol. 2, pp. 543–548 (2016)
Mrkšić, N., Séaghdha, D.O., Thomson, B., Gašić, M., Rojas-Barahona, L., Su, P.H., Vandyke, D., Wen, T.H., Young, S.: Counter-fitting word vectors to linguistic constraints. arXiv preprint arXiv:1603.00892 (2016)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The development and psychometric properties of liwc2015. Technical report (2015)
Rao, D., Ravichandran, D.: Semi-supervised polarity lexicon induction. In: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics, pp. 675–682. Association for Computational Linguistics (2009)
Stone, P.J., Bales, R.F., Namenwirth, J.Z., Ogilvie, D.M.: The general inquirer: a computer system for content analysis and retrieval based on the sentence as a unit of information. Syst. Res. Behav. Sci. 7(4), 484–498 (1962)
Thelen, M., Riloff, E.: A bootstrapping method for learning semantic lexicons using extraction pattern contexts. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 214–221. Association for Computational Linguistics (2002)
Wieting, J., Bansal, M., Gimpel, K., Livescu, K.: Towards universal paraphrastic sentence embeddings. arXiv preprint arXiv:1511.08198 (2015)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354. Association for Computational Linguistics (2005)
Acknowledgements
This material is based in part upon work supported by the Michigan Institute for Data Science, by the National Science Foundation (grant #1344257), and by the John Templeton Foundation (grant #48503). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the Michigan Institute for Data Science, the National Science Foundation, or the John Templeton Foundation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wilson, S.R., Shen, Y., Mihalcea, R. (2018). Building and Validating Hierarchical Lexicons with a Case Study on Personal Values. In: Staab, S., Koltsova, O., Ignatov, D. (eds) Social Informatics. SocInfo 2018. Lecture Notes in Computer Science(), vol 11185. Springer, Cham. https://doi.org/10.1007/978-3-030-01129-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-01129-1_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01128-4
Online ISBN: 978-3-030-01129-1
eBook Packages: Computer ScienceComputer Science (R0)