Abstract
Despite the growth of bilingual word embeddings, there is no work done so far, for directly evaluating them for English–Tamil language pair. In this paper, we present a data resource and evaluation for the English–Tamil bilingual word vector model. In this paper, we present dataset and the evaluation paradigm for English–Tamil bilingual language pair. This dataset contains words that covers a range of concepts that occur in natural language. The dataset is scored based on the similarity rather than association or relatedness. Hence, the word pairs that are associated but not literally similar have a low rating. The measures are quantified further to ensure consistency in the dataset, mimicking the cognitive phenomena. Henceforth, the dataset can be used by non-native speakers, with minimal effort. We also present some inferences and insights into the semantics captured by word vectors and human cognition.
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Notes
- 1.
Each word is appended with an id (:id) to understand the mapping between the sentences.
- 2.
All the participants were informed about this study, and they have provided their consent to be part of this.
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Sanjanasri, J.P., Menon, V.K., Rajendran, S., Soman, K.P., Anand Kumar, M. (2020). Intrinsic Evaluation for English–Tamil Bilingual Word Embeddings. In: Thampi, S., et al. Intelligent Systems, Technologies and Applications. Advances in Intelligent Systems and Computing, vol 910. Springer, Singapore. https://doi.org/10.1007/978-981-13-6095-4_3
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