Constructing and validating word similarity datasets by integrating methods from psychology, brain science and computational linguistics
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Human-scored word similarity gold-standard datasets are normally composed of word pairs with corresponding similarity scores. These datasets are popular resources for evaluating word similarity models which are the essential components for many natural language processing tasks. This paper proposes a novel multidisciplinary method for constructing and validating word similarity gold-standard datasets. The proposed method is different from the previous ones in that it introduces methods from three different disciplines, i.e., psychology, brain science and computational linguistics to validate the soundness of the constructed datasets. Specifically, to the best of our knowledge, this is the first time event-related potentials experiments are incorporated to validate the word similarity datasets. Using the proposed method, we finally constructed a Chinese gold-standard word similarity dataset with 260 word pairs and showed its soundness using the interdisciplinary validating methods. It should be noted that, although the paper only focused on constructing Chinese standard dataset, the proposed method is applicable to other languages.
KeywordsWord similarity Dataset construction and validation Multidisciplinary method Computational linguistics Psychology ERPs
This work was supported by National Natural Science Foundation of China (No. 61573294), National Social Science Foundation of China (No. 16AZD049) and Fujian Province 2011 Collaborative Innovation Center of TCM Health Management.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- Agirre E, Alfonseca E, Hall K, Kravalova J, Pasca M, Soroa A (2009) A study on similarity and relatedness using distributional and wordnet-based approaches. In: Proceedings of human language technologies: the 2009 annual conference of the north American chapter of the ACL, pp 19–27Google Scholar
- Collobert R, Weston J (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning, pp 160–167Google Scholar
- Dong Z, Dong Q, Hao C (2010) Hownet and its computation of meaning. In: Proceedings of the 23rd international conference on computational linguistics, pp 53–56Google Scholar
- Finkelstein L, Gabrilovich E, Matias Y, Rivlin E, Solan Z, Wolfman G, Ruppin E (2001) Placing search in context: the concept revisited. In: Proceedings of the 10th international conference on world wide web, pp 406–414Google Scholar
- Huang EH, Socher R, Manning CD, Ng AY (2012) Improving word representations via global context and multiple word prototypes. In: Proceedings of the 50th annual meeting of the association for computational linguistics: long papers, pp 873–882Google Scholar
- Jin P, Wu YF (2012) Semeval-2012 task 4: evaluating chinese word similarity. In: Proceedings of the 6th international workshop on semantic evaluation, pp 374–377Google Scholar
- Jurgens D, Stevens K (2010) The s-space package: an open source package for word space models. In: Proceedings of the ACL 2010 system demonstrations, pp 30–35Google Scholar
- Liu Q, Li S (2002) Word similarity computing based on how-net. In: Proceedings of the 3rd Chinese lexical semantics workshop, pp 59–76Google Scholar
- Liu Y (2009) A review of Chinese vocabulary statistic studies. Chin Lang Learn 1:62–69Google Scholar
- Mikolov T, Chen K, Corrado G, Dean J (2013a) Efficient estimation of word representations in vector space. In: Proceedings of international conference of learning representationsGoogle Scholar
- Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013b) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst 26:3111–3119Google Scholar
- Rohde DLT, Gonnerman LM, Plaut DC (2006) An improved model of semantic similarity based on lexical co-occurrence. Commun ACM 8:627–633Google Scholar
- Turian J, Ratinov LA, Bengio Y (2010) Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th annual meeting of the association for computational linguistics, pp 384–394Google Scholar
- Wang X, Jia Y, Zhou B, Ding ZY, Liang Z (2011) Computing semantic relatedness using chinese wikipedia links and taxonomy. J Chin Comput Syst 32(11):2237–2242Google Scholar