Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance

Yıl: 2018 Cilt: 23 Sayı: 1 Sayfa Aralığı: 31 - 40 Metin Dili: İngilizce DOI: 10.17482/uumfd.318615 İndeks Tarihi: 30-09-2019

Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance

Öz:
This study revisits the problem of maximizing the performance of mathematical word representations for a given task. It is aimed to improve performance in analogy and similarity tasks by suggesting innovative weights instead of the counting weights used conventionally in counting-based methods of generating word representations (adding the statistics of word co-occurrences to the account). The language of study was selected as Turkish. The root structures of Turkish words were managed during the compilation of corpus such that each word having a suffix was considered as a new word. The performance of the proposed co-occurrence weights are analyzed with respect to the varying parameter and the results are presented within the paper.
Anahtar Kelime:

KELİME TEMSİLLERİ İÇİN TEST PERFORMANSINI GELİŞTİRMEYE YÖNELİK EŞDİZİMLİLİK AĞIRLIKLARININ SEÇİMİ

Öz:
Bu çalışma, matematiksel kelime temsillerinin belirli bir görev için performanslarının en iyilenmesi problemini yeniden ele almaktadır. Sayma tabanlı (kelimelerin eşdizimlilik istatistiklerini hesaba katan) kelime temsili oluşturma yöntemlerinde klasik olarak kullanılan sayma ağırlıkları yerine yenilikçi ağırlıklar önererek analoji ve benzerlik bulma görevlerinde performans artışı sağlamak hedeflenmektedir. Çalışma dili olarak Türkçe seçilmiş, derlem oluşturulurken Türkçe’ye has ek-kök yapıları ek alan her kelime yeni bir kelime gibi kabul edilecek şekilde yorumlanmıştır. Önerilen eşdizimlilik ağırlıklarının performansı değişen parametreye göre analiz edilerek sonuçlar çalışma içerisinde paylaşılmıştır.
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • Bahdanau, D., Cho, K. and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv: 1409.0473.
  • Bengio, Y., Ducharme, R., Vincent, P., and Jauvin C. (2003). A neural probabilistic language model. Journal of machine learning research, 1137 – 1155. doi: 10.1162/153244303322533223
  • Bojanowski, P., Grave, E., Joulin, A., ve Mikolov, T. (2016). Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606.
  • Faruqui, M., Dodge, J. , Jauhar, S. K., Dyer, C., Hovy, E. ve Smith, N. A. (2014) Retrofitting word vectors to semantic lexicons, arXiv preprint arXiv:1411.4166. doi: 10.3115/v1/N15-1184
  • Firth, J. R., (1957). A synopsis of linguistic theory 1930-1955. In Studies in linguistic analysis, 1-32. Oxford:Blackwell.
  • Huth, A.G., de Heer, W.A., Griffiths, T.L., Theunissen, F.E. and Gallant, J.L. (2016) Natural speech reveals the semantic map that tile human cerebral cortex. Nature, vol. 532, no. 7600, 453 – 458. doi:10.1038/nature17637
  • Karpathy, A. and Fei-Fei, L. (2016). Deep visual-semantic alignments for generating image descriptions. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39 (4), 664-676. doi: 10.1109/TPAMI.2016.2598339
  • Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural netwroks. In Advances in neural information processing systems, 1097-1105. doi: 10.1145/3065386
  • Le, Q. V. ve Mikolov, T. (2014) Distributed representations of sentences and documents, ICML, vol. 14, 1188–1196.
  • Luong, T., Socher, R. ve Manning, C.D. (2013) Better word representations with recursive neural networks for morphology, CoNLL, 104–113.
  • Mikolov, T., Karafiat, M., Burget, L., Cernocky, J. and Khudanpur S. (2010) Recurrent neural network based language model, Interspeech, Vol 2, 3.
  • Mikolov, T., Chen, K., Corrado, G. ve Dean, J. (2013a) Efficient estimation of word representations in vector space, arXiv preprint arXiv:1301.3781.
  • Mikolov, T., Le, Q.V. and Sutskever, I. (2013b) Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168.
  • Mikolov, T., Sutskever, I, Chen, K., Corrado, G.S. ve Dean J. (2013c) Distributed representations of words and phrases and their compositionality, Advances in neural information processing systems, 3111–3119.
  • Mnih, A., ve Hinton, G., (2007) Three new graphical models for statistical language modelling, Proceedings of the 24th International Conference on Machine Learning. ACM, 641–648. doi:10.1145/1273496.1273577
  • Pennington, J., Socher, R. ve Manning, C.D. (2014) Glove: Global vectors for word representation, Empirical Methods in Natural Language Processing (EMNLP), 1532–1543. doi: 10.3115/v1/D14-1162
  • Ravichandran, D., Pantel, P. ve Hovy, E. (2005) Randomized algorithms and nlp: Using locality sensitive hash function for high speed noun clustering, Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, 622–629. doi: 10.3115/1219840.1219917
  • Salton, G., Wong, A., and Yang, C.S. (1975) A vector space model for automatic indexing. Communications of the ACM, 18(11), 613-620. doi: 10.1145/361219.361220
  • Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., ve Potts C., (2013) Recursive deep models for semantic compositionality over a sentiment treebank, Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 1631-1642.
  • Şenel, L.K., Yücesoy, V., Koç, A. and Çukur, T. (2017) Measuring cross-lingual semantic similarity across European languages. In International Conference on Telecommunications and Signal Processing (TSP), 359-363. doi: 10.1109/TSP.2017.8076005
  • Şenel, L. K., Yücesoy, V., Koç, A., Çukur, T. (2017b). Semantic similarity between Turkish and european languages using word embeddings. 25th Signal Processing and Communications Applications Conference, 1-4. doi: 10.1109/SIU.2017.7960365
  • Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T. and Qin, B. (2014) Learning sentiment specific word embedding for twitter sentiment classification. ACL, 1555 – 1565. doi: 10.3115/v1/P14-1146
  • Yücesoy, V., Koç A. (2017). Effect of co-occurrence weighting to English word embeddings. 25th Signal Processing and Communications Applications Conference, 1-4. doi: 10.1109/SIU.2017.7960385
APA Koç A, YÜCESOY V (2018). Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance. , 31 - 40. 10.17482/uumfd.318615
Chicago Koç Aykut,YÜCESOY Veysel Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance. (2018): 31 - 40. 10.17482/uumfd.318615
MLA Koç Aykut,YÜCESOY Veysel Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance. , 2018, ss.31 - 40. 10.17482/uumfd.318615
AMA Koç A,YÜCESOY V Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance. . 2018; 31 - 40. 10.17482/uumfd.318615
Vancouver Koç A,YÜCESOY V Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance. . 2018; 31 - 40. 10.17482/uumfd.318615
IEEE Koç A,YÜCESOY V "Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance." , ss.31 - 40, 2018. 10.17482/uumfd.318615
ISNAD Koç, Aykut - YÜCESOY, Veysel. "Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance". (2018), 31-40. https://doi.org/10.17482/uumfd.318615
APA Koç A, YÜCESOY V (2018). Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 23(1), 31 - 40. 10.17482/uumfd.318615
Chicago Koç Aykut,YÜCESOY Veysel Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 23, no.1 (2018): 31 - 40. 10.17482/uumfd.318615
MLA Koç Aykut,YÜCESOY Veysel Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol.23, no.1, 2018, ss.31 - 40. 10.17482/uumfd.318615
AMA Koç A,YÜCESOY V Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi. 2018; 23(1): 31 - 40. 10.17482/uumfd.318615
Vancouver Koç A,YÜCESOY V Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi. 2018; 23(1): 31 - 40. 10.17482/uumfd.318615
IEEE Koç A,YÜCESOY V "Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance." Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 23, ss.31 - 40, 2018. 10.17482/uumfd.318615
ISNAD Koç, Aykut - YÜCESOY, Veysel. "Co-occurrence Weight Selection for Word Embeddings to Enhance Test Performance". Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 23/1 (2018), 31-40. https://doi.org/10.17482/uumfd.318615