@inproceedings{lakmal-etal-2020-word,
title = "Word Embedding Evaluation for {S}inhala",
author = "Lakmal, Dimuthu and
Ranathunga, Surangika and
Peramuna, Saman and
Herath, Indu",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.231",
pages = "1874--1881",
abstract = "This paper presents the first ever comprehensive evaluation of different types of word embeddings for Sinhala language. Three standard word embedding models, namely, Word2Vec (both Skipgram and CBOW), FastText, and Glove are evaluated under two types of evaluation methods: intrinsic evaluation and extrinsic evaluation. Word analogy and word relatedness evaluations were performed in terms of intrinsic evaluation, while sentiment analysis and part-of-speech (POS) tagging were conducted as the extrinsic evaluation tasks. Benchmark datasets used for intrinsic evaluations were carefully crafted considering specific linguistic features of Sinhala. In general, FastText word embeddings with 300 dimensions reported the finest accuracies across all the evaluation tasks, while Glove reported the lowest results.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<abstract>This paper presents the first ever comprehensive evaluation of different types of word embeddings for Sinhala language. Three standard word embedding models, namely, Word2Vec (both Skipgram and CBOW), FastText, and Glove are evaluated under two types of evaluation methods: intrinsic evaluation and extrinsic evaluation. Word analogy and word relatedness evaluations were performed in terms of intrinsic evaluation, while sentiment analysis and part-of-speech (POS) tagging were conducted as the extrinsic evaluation tasks. Benchmark datasets used for intrinsic evaluations were carefully crafted considering specific linguistic features of Sinhala. In general, FastText word embeddings with 300 dimensions reported the finest accuracies across all the evaluation tasks, while Glove reported the lowest results.</abstract>
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%0 Conference Proceedings
%T Word Embedding Evaluation for Sinhala
%A Lakmal, Dimuthu
%A Ranathunga, Surangika
%A Peramuna, Saman
%A Herath, Indu
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F lakmal-etal-2020-word
%X This paper presents the first ever comprehensive evaluation of different types of word embeddings for Sinhala language. Three standard word embedding models, namely, Word2Vec (both Skipgram and CBOW), FastText, and Glove are evaluated under two types of evaluation methods: intrinsic evaluation and extrinsic evaluation. Word analogy and word relatedness evaluations were performed in terms of intrinsic evaluation, while sentiment analysis and part-of-speech (POS) tagging were conducted as the extrinsic evaluation tasks. Benchmark datasets used for intrinsic evaluations were carefully crafted considering specific linguistic features of Sinhala. In general, FastText word embeddings with 300 dimensions reported the finest accuracies across all the evaluation tasks, while Glove reported the lowest results.
%U https://aclanthology.org/2020.lrec-1.231
%P 1874-1881
Markdown (Informal)
[Word Embedding Evaluation for Sinhala](https://aclanthology.org/2020.lrec-1.231) (Lakmal et al., LREC 2020)
ACL
- Dimuthu Lakmal, Surangika Ranathunga, Saman Peramuna, and Indu Herath. 2020. Word Embedding Evaluation for Sinhala. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1874–1881, Marseille, France. European Language Resources Association.