Biological terms boundary identification by maximum entropy model
| Autor: | Wenwu Shao, Fei Zhu, Jian Wang |
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| Rok vydání: | 2011 |
| Předmět: |
Computer science
business.industry Entropy (statistical thermodynamics) Maximum-entropy Markov model Principle of maximum entropy Pattern recognition Maximum entropy spectral estimation Entropy (classical thermodynamics) Entropy (information theory) Artificial intelligence Entropy (energy dispersal) business Algorithm Entropy (arrow of time) Entropy (order and disorder) |
| Zdroj: | 2011 6th IEEE Conference on Industrial Electronics and Applications. |
| DOI: | 10.1109/iciea.2011.5976003 |
| Popis: | There are a large number of biological data which are produced by life science experiments. How to use these data to carry out life discussion effectively supported by mathematics, computer science is a significant problem. Biological terms identification is one of the important research issues in the area of Bioinformatics. Besides, Maximum entropy model is widely used in various fields. This noun sounds profound, but its principle is very simple. As a statistical method, it has many features: for instance, subtle features can be controlled and reusable, it is also understood easily and so on. This model was first introduced in the sentence segmentation. In this paper, an example of the introduction of the concept of maximum entropy model, about the maximum entropy model was applied to Biological text terms boundary identification. Additionally, compared to the general terms boundary identification to the ME model, to illustrate the advantages of the introduction of maximum entropy model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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