Abstract
In this paper we compare different context selection approaches to improve the creation of Emotive Vector Space Models (VSMs). The system is based on the results of an existing approach that showed the possibility to create and update VSMs by exploiting crowdsourcing and human annotation. Here, we introduce a method to manipulate the contexts of the VSMs under the assumption that the emotive connotation of a target word is a function of both its syntagmatic and paradigmatic association with the various emotions. To study the differences among the proposed spaces and to confirm the reliability of the system, we report on two experiments: in the first one we validated the best candidates extracted from each model, and in the second one we compared the models’ performance on a random sample of target words. Both experiments have been implemented as crowdsourcing tasks.- Anthology ID:
- L16-1347
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 2185–2191
- Language:
- URL:
- https://aclanthology.org/L16-1347
- DOI:
- Cite (ACL):
- Lucia C. Passaro and Alessandro Lenci. 2016. Evaluating Context Selection Strategies to Build Emotive Vector Space Models. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2185–2191, Portorož, Slovenia. European Language Resources Association (ELRA).
- Cite (Informal):
- Evaluating Context Selection Strategies to Build Emotive Vector Space Models (Passaro & Lenci, LREC 2016)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/L16-1347.pdf