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1 nwjc2vec: word2vec nwjc2vec nwjc2vec nwjc2vec 2 nwjc2vec 7 nwjc2vec word2vec nwjc2vec: Word Embedding Data Constructed from NINJAL Web Japanese Corpus Hiroyuki Shinnou, Masayuki Asahara, Kanako Komiya and Minoru Sasaki We constructed word embedding data (named as nwjc2vec ) using the NINJAL Web Japanese Corpus and word2vec software, and released it publicly. In this report, nwjc2vec is introduced, and the result of two types of experiments that were conducted to evaluate the quality of nwjc2vec is shown. In the first experiment, the evaluation based on word similarity is considered. Using a word similarity dataset, we calculate Spearman s rank correlation coefficient. In the second experiment, the evaluation based on task is considered. As the task, we consider word sense disambiguation (WSD) and language model construction using Recurrent Neural Network (RNN). The results obtained using the nwjc2vec were compared with the results obtained using word embedding constructed from the article data of newspaper for seven years. The nwjc2vec is shown to be high quality. Key Words: Word Embedding, NINJAL Web Japanese Corpus, word2vec, Department of Computer and Information Sciences, Ibaraki University, National Institute for Japanese Language and Linguistics
2 Vol. 24 No. 5 December one-hot N N w i N i 1 0 w one-hot Mikolov word2vec (Mikolov, Sutskever, Chen, Corrado, and Dean 2013b; Mikolov, Chen, Corrado, and Dean 2013a) ( 2016) 1 word2vec 2 GloVe 3 NWJC (Asahara, Maekawa, Imada, Kato, and Konishi 2014) nwjc2vec 4 NWJC ,050 5 NWJC 1,200 nwjc2vec 1 mecab-owakati word2vec unidic 706
3 nwjc2vec: nwjc2vec nwjc2vec nwjc2vec Recurrent Neural Network, RNN 7 nwjc2vec 2 nwjc2vec 2.1 NWJC NWJC 100 Heritrix URL nwc-toolkit MeCab UniDic CaboCha UniDic 11 URL (Asahara, Kawahara, Takei, Masuoka, Ohba, Torii, Morii, Tanaka, Maekawa, Kato, and Konishi 2016) NWJC Q CaboCha./configure --with-posset=unidic UniDic 707
4 Vol. 24 No. 5 December word2vec 1 NWJC Q word2vec 12 CBOW 2 word2vec 13 word 14 mrph nwjc2vec 2 nwjc2vec nwjc2vec 1 1 e_1 e_2 e_200 1 : NWJC Q URL 83,992,556 8,399 URL 3,885,889, ,463,142, ,836,947, word2vec CBOW or skip-gram -cbow 1 -size 200 -window 8 -negative 25 softmax -hs 0 -sample 1e-4 -iter word2vec demo-word.sh NWJC nwjc2vec 3 14 unidic-mecab kana-accent dicrc
5 nwjc2vec: e_i i,,,,*,*,*,,,,,,,,*,*,*, ,,,,*,*,*,,,,,,,,*,*,*, 1 word2vec 15 1 nwjc2vec 1,738, ,541,651 nwjc2vec 3 etc 3 (%) 1,570, , , , , , , , , etc 2, ,738, L2-16 header 1 1,738,
6 Vol. 24 No. 5 December etc , ,,,,,,,,,,,,,,,,,,,,,,,,,,,
7 nwjc2vec: 3 nwjc2vec ( 2017) nwjc2vec 2 7 nwjc2vec 3.1 mai2vec nwjc2vec ,791,403 MeCab UniDic word2vec mai2vec word2vec nwjc2vec 2 mai2vec 132,
8 Vol. 24 No. 5 December ,102 1, mai2vec nwjc2vec mai2vec nwjc2vec 5 mai2vec nwjc2vec 18 6 nwjc2vec mai2vec mai2vec Sugawara (Sugawara, Takamura, Sasano, and Okumura 2015) Sugawara 2 / / / / / / / / / / / / / / / / / / / V Sugawara 2 4 V V V V V
9 nwjc2vec: 7 (%) baseline mai2vec nwjc2vec mai2vec-0 nwjc2vec V V V V nwjc2vec mai2vec nwjc2vec - / - / - / - / - / - / - / - / - / - / - / - / - / - / - / - / - / - / - SemEval-2 (Okumura, Shirai, Komiya, and Yokono 2011) baseline SemEval-2 mai2vec mai2vec nwjc2vec nwjc2vec 1 1 word2vec mai2vec-0 nwjc2vec-0 SVM 19 nwjc2vec nwjc2vec RNN RNN t s t w t 19 cjlin/libsvm/ 713
10 Vol. 24 No. 5 December 2017 w t+1 RNN Long Short-Term Memory LSTM (Gers, Schmidhuber, and Cummins 2000) LSTM t 2 t w t w t LSTM LSTM t + 1 LSTM w 0 w t h t c t y t W one-hot W y t w t+1 w t w t LSTM mai2vec (mai2vec-lm) nwjc2vec (nwjc2vec-lm) nwjc2vec LSTM (base-lm) 2 LSTM t 714
11 nwjc2vec: (Maekawa, Yamazaki, Ogiso, Maruyama, Ogura, Kashino, Koiso, Yamaguchi, Tanaka, and Den 2014) Yahoo! Yahoo! 7,330 7, epoch epoch base-lm mai2vec-lm nwjc2vec-lm ,
12 Vol. 24 No. 5 December 2017 mai2vec-lm nwjc2vec-lm base-lm nwjc2vec-lm mai2vec-lm nwjc2vec mai2vec 4 mai2vec nwjc2vec nwjc2vec mai2vec mai2vec ( 2017) mai2vec nwjc2vec nwjc2vec mai2vec SemEval-2 baseline baseline SemEval-2 baseline baseline 0.2% ( 2015) 77.28% nwjc2vec 0.43% Yamaki wikipedia 77.10% (Yamaki, Shinnou, Komiya, and Sasaki 2016) mai2vec mai2vec nwjc2vec baseline nwjc2vec mai2vec 0.64% 0.64% mai2vec nwjc2vec 175,302 15,082 mai2vec 7,424 3,204 nwjc2vec nwjc2vec mai2vec 716
13 nwjc2vec: 9 fine-tuning epoch nwjc2vec-lm fine-tuning nwjc2vec fine-tuning fine-tuning fine-tuning nwjc2vec 21 ( 2016) nwjc2vec mai2vec 30 nwjc2vec fine-tuning LSTM 9 4 epoch fine-tuning fine-tuning 5 nwjc2vec nwjc2vec word2vec nwjc2vec 2 nwjc2vec 21 window 5 Negative Sample 20 SkipGram 717
14 Vol. 24 No. 5 December fine-tuning nwjc2vec nwjc2vec nwjc2vec fine-tuning ( ) ( ) all-words WSD ( ) Asahara, M., Kawahara, K., Takei, Y., Masuoka, H., Ohba, Y., Torii, Y., Morii, T., Tanaka, Y., Maekawa, K., Kato, S., and Konishi, H. (2016). BonTen Corpus Concordance System for NINJAL Web Japanese Corpus. In Proceedings of COLING 2016, the 26th International 718
15 nwjc2vec: Conference on Computational Linguistics: System Demonstrations, pp Asahara, M., Maekawa, K., Imada, M., Kato, S., and Konishi, H. (2014). Archiving and Analysing Techniques of the Ultra-large-scale Web-based Corpus Project of NINJAL, Japan. Alexandria: The Journal of National and International Library and Information Issues, 25 (1 2), pp (2017). nwjc2vec:. 23, pp Gers, F. A., Schmidhuber, J., and Cummins, F. (2000). Learning to Forget: Continual Prediction with LSTM. Neural Computation, 12 (10), pp Maekawa, K., Yamazaki, M., Ogiso, T., Maruyama, T., Ogura, H., Kashino, W., Koiso, H., Yamaguchi, M., Tanaka, M., and Den, Y. (2014). Balanced Corpus of Contemporary Written Japanese. Language Resources and Evaluation, 48 (2), pp Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013a). Efficient Estimation of Word Representations in Vector Space. In ICLR Workshop Paper. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013b). Distributed Representations of Words and Phrases and Their Compositionality. In Advances in Neural Information Processing Systems, pp (2016).., 31 (2), pp Okumura, M., Shirai, K., Komiya, K., and Yokono, H. (2011). On SemEval-2010 Japanese WSD Task., 18 (3), pp (2016). Chainer.. (2015).. 21, pp Sugawara, H., Takamura, H., Sasano, R., and Okumura, M. (2015). Context Representation with Word Embeddings for WSD. In PACLING-2015, pp Yamaki, S., Shinnou, H., Komiya, K., and Sasaki, M. (2016). Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings. In PACLIC-30, pp
16 Vol. 24 No. 5 December
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