Results 201 to 210 of about 10,729 (237)

Combining Taxonomies using Word2vec

Proceedings of the 2016 ACM Symposium on Document Engineering, 2016
Taxonomies have gained a broad usage in a variety of fields due to their extensibility, as well as their use for classification and knowledge organization. Of particular interest is the digital document management domain in which their hierarchical structure can be effectively employed in order to organize documents into content-specific categories ...
Tobias Swoboda, Matthias L. Hemmje, Mihai Dascalu, Stefan Trausan-Matu   +3 more
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word2vec or JoBimText?

Proceedings of the 7th Forum for Information Retrieval Evaluation, 2015
Exploration of distributional semantics for NLP tasks in Indian languages has been scarce. This work carries out a comparative analysis of two recent and high performing distributional semantics techniques namely word2vec and JoBimText. The task of lexical expansion of words in Hindi is considered for the analysis. A manual similarity assessment of the
Nitin Ramrakhiyani, Sachin Pawar, Girish Keshav Palshikar   +2 more
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Acceleration of Word2vec Using GPUs

2016
Word2vec is a widely used word embedding toolkit which generates word vectors by training input corpus. Since word vector can represent an exponential number of word cluster and enables reasoning of words with simple algebraic operations, it has become a widely used representation for the subsequent NLP tasks.
Seulki Bae, Youngmin Yi
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Critical Dimension of Word2Vec

2019 2nd International Conference on Innovations in Electronics, Signal Processing and Communication (IESC), 2019
Word embeddings are an efficient way of representing text such that they can be used by different Machine Learning Algorithms. Word2Vec is one such word embedding model. Although it is highly efficient, this model can take up a lot of space to store the vector representations.
Shuvayanti Das, Sohan Ghosh, Shubham Bhattacharya, Rajat Varma, Dinabandhu Bhandari   +4 more
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Keywords Extraction Based on Word2Vec and TextRank

Proceedings of the 2020 3rd International Conference on Big Data and Education, 2020
In order to improve the performance of keyword extraction by enhancing the semantic representations of documents, we propose a method of keyword extraction which exploits the document's internal semantic information and the semantic representations of words pre-trained by massive external documents.
Yong Zhang, Fen Chen, Wufeng Zhang, Haoyang Zuo, Fangyuan Yu   +4 more
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An Word2vec based on Chinese Medical Knowledge

2019 IEEE International Conference on Big Data (Big Data), 2019
Introducing a large amount of external prior domain knowledge will effectively improve the performance of the word embedded language model in downstream NLP tasks. Based on this assumption, we collect and collate a medical corpus data with about 36M (Million) characters and use the data of CCKS2019 as the test set to carry out multiple classifications ...
Jiayi Zhu, Pin Ni, Yuming Li, Junkun Peng, Zhenjin Dai, Gangmin Li, Xuming Bai   +6 more
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Optimizing Word2Vec Performance on Multicore Systems

Proceedings of the Seventh Workshop on Irregular Applications: Architectures and Algorithms, 2017
The Skip-gram with negative sampling (SGNS) method of Word2Vec is an unsupervised approach to map words in a text corpus to low dimensional real vectors. The learned vectors capture semantic relationships between co-occurring words and can be used as inputs to many natural language processing and machine learning tasks.
Vasudevan Rengasamy, Tao-Yang Fu, Wang-Chien Lee, Kamesh Madduri   +3 more
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Weighted word2vec based on the distance of words

2017 International Conference on Machine Learning and Cybernetics (ICMLC), 2017
Word2vec is a novel technique for the study and application of natural language processing(NLP). It trains a word embedding neural network model with a large training corpus. After the model is trained, each word is represented by a vector in the specified vector space.
Chia-Yang Chang, Shie-Jue Lee, Chih-Chin Lai   +2 more
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Adversarial Attacks on Word2vec and Neural Network

Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial Intelligence, 2018
The security of machine learning is of great importance. Image vulnerability has been known in the literature for a long time. This paper is concerned with the text vulnerability. The word2vec is widely used to produce the word embedding, which plays an important role in natural language processing. The quality of word embedding affects the performance
Yue Li 0010, Pengjian Xu, Minmin Pang
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Methodenbeitrag: word2vec

forTEXT Volume 1 Issue 10 ...
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