Fracture Mechanics Method for Word Embedding Generation of Neural Probabilistic Linguistic Model
Joint Authors
Bi, Size
Liang, Xiao
Huang, Ting-lei
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-06
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Word embedding, a lexical vector representation generated via the neural linguistic model (NLM), is empirically demonstrated to be appropriate for improvement of the performance of traditional language model.
However, the supreme dimensionality that is inherent in NLM contributes to the problems of hyperparameters and long-time training in modeling.
Here, we propose a force-directed method to improve such problems for simplifying the generation of word embedding.
In this framework, each word is assumed as a point in the real world; thus it can approximately simulate the physical movement following certain mechanics.
To simulate the variation of meaning in phrases, we use the fracture mechanics to do the formation and breakdown of meaning combined by a 2-gram word group.
With the experiments on the natural linguistic tasks of part-of-speech tagging, named entity recognition and semantic role labeling, the result demonstrated that the 2-dimensional word embedding can rival the word embeddings generated by classic NLMs, in terms of accuracy, recall, and text visualization.
American Psychological Association (APA)
Bi, Size& Liang, Xiao& Huang, Ting-lei. 2016. Fracture Mechanics Method for Word Embedding Generation of Neural Probabilistic Linguistic Model. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099659
Modern Language Association (MLA)
Bi, Size…[et al.]. Fracture Mechanics Method for Word Embedding Generation of Neural Probabilistic Linguistic Model. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1099659
American Medical Association (AMA)
Bi, Size& Liang, Xiao& Huang, Ting-lei. Fracture Mechanics Method for Word Embedding Generation of Neural Probabilistic Linguistic Model. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1099659
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099659