SeqVectorizer: Sequence Representation in Vector Space
Authors: 
Md. Kowsher, Avishek Das, Md. Murad Hossain Sarker, Anik Tahabilder, Md. Zahidul Islam SanjidAuthors Info & Claims
Article No.: 24, Pages 1 - 6
Abstract
The latest strategies for learning vector space portrayals of words have prevailed with regard to catching fine-grained semantic and syntactic consistencies utilizing vector arithmetic. However, the sequence representation is not present in these methods. As a result, to consider the sequence, we are utilizing the sequence neural networks like RNN or statistical techniques like HMM. To represent the sequence through every state vector, we propose a new term or word representation technique called SeqVectorizer, which stands for sequence vectorizer. In SeqVectorizer every state represents a combined vector of two separate joined states, and these are the previous sequence state and the current state probability. Comparing with other representation systems, it shows a state-of-the-art performance on some testing data-sets.
References
[1]
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics 5 (2017), 135–146.
[2]
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2016. Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759(2016).
[3]
Redwanul Karim, MA Islam, Sazid Rahman Simanto, Saif Ahmed Chowdhury, Kalyan Roy, Adnan Al Neon, Md Hasan, Adnan Firoze, Rashedur M Rahman, 2019. A step towards information extraction: Named entity recognition in Bangla using deep learning. Journal of Intelligent & Fuzzy SystemsPreprint (2019), 1–13.
[4]
Md Kowsher, M Ashraful Alam, Md Jashim Uddin, Md Rafiqul Islam, Nuruzzaman Pias, and Abu Rayhan Md Saifullah. 2019. Bengali Informative Chatbot. In 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2). IEEE, 1–7.
[5]
Md Kowsher, Imran Hossen, and SkShohorab Ahmed. 2019. Bengali Information Retrieval System (BIRS). International Journal on Natural Language Computing (IJNLC) 8, 5(2019).
[6]
Md Kowsher, MM Mahabubur Rahman, Sk Shohorab Ahmed, and Nusrat Jahan Prottasha. 2019. Bangla Intelligence Question Answering System Based on Mathematics and Statistics. In 2019 22nd International Conference on Computer and Information Technology (ICCIT). IEEE, 1–6.
[7]
Md Kowsher, Farhana Sharmin Tithi, M Ashraful Alam, Mohammad Nurul Huda, Mir Md Moheuddin, and Md Golam Rosul. 2019. Doly: Bengali Chatbot for Bengali Education. In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). IEEE, 1–6.
[8]
Kevin Lund and Curt Burgess. 1996. Producing high-dimensional semantic spaces from lexical co-occurrence. Behavior research methods, instruments, & computers 28, 2 (1996), 203–208.
[9]
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781(2013).
[10]
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111–3119.
[11]
Pritom Mojumder, Mahmudul Hasan, Md Faruque Hossain, and KM Azharul Hasan. 2020. A Study of fastText Word Embedding Effects in Document Classification in Bangla Language. In International Conference on Cyber Security and Computer Science. Springer, 441–453.
[12]
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 1532–1543.
[13]
Douglas LT Rohde, Laura M Gonnerman, and David C Plaut. 2006. An improved model of semantic similarity based on lexical co-occurrence. Commun. ACM 8, 627-633 (2006), 116.
[14]
Xia Sun, Yi Gao, Richard Sutcliffe, Shou-Xi Guo, Xin Wang, and Jun Feng. 2019. Word representation learning based on bidirectional grus with drop loss for sentiment classification. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2019).
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Published In
April 2021
410 pages
ISBN:9781450388719
DOI:10.1145/3454127
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Published: 26 November 2021
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NISS2021
NISS2021: The 4th International Conference on Networking, Information Systems & Security.
April 1 - 2, 2021
AA, KENITRA, Morocco
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