CLRG ChemTMiner @ Biocreative V
نویسندگان
چکیده
This paper describes our system developed for Biocreative V Chemical entity mention in patent (CEMP) task for identifying the chemical mentions from patent data. We have presented two systems which use Support vector machines (SVMs) and Conditional random fields (CRFs) algorithms. In this work we used a rich feature set that includes linguistic, orthographical and lexical clue features. We obtained an F-score of 0.817 using CRFs and F-score of 0.877 using SVMs on the development data. And on test data we obtained an Fscore of 0.7532, 0.74523 for SVM and CRFs respectively.
منابع مشابه
COMMON FIXED POINT THEOREM IN INTUITIONISTIC FUZZY METRIC SPACE USING THE PROPERTY (CLRg)
The main purpose of this paper is to give common fixed point theorem in intuitionistic fuzzy metric spaces under strict contractive conditions for mappings satisfying (CLRg) property .Our result improves and generalizes the result of Sharma, P and Sharma, S. MSC: 47H10, 54H25
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By means of weakening conditions of the gauge function φ and the CLRg property, some common fixed point theorems are established in fuzzy metric spaces. The two mappings considered here are assumed to be weakly compatible. Our results extend and improve very recent theorems in the related literature. c ©2016 All rights reserved.
متن کاملCommon Fixed Point Results using Weakly Compatible Maps along with (CLRg) Property in G-Metric Spaces
In this paper, first we prove common fixed point theorems for a pair of weakly compatible maps. Secondly, we prove a fixed point theorem for weakly compatible maps along with (CLRg) property. In fact, our results generalize the results of Z. Mustafa et al. [10]. 2000 AMS Classification: 47H10, 54H25
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In this paper, we introduce (CLRg) property for a hybrid pair of maps in fuzzy metric spaces and utilize the same to prove two unique common coupled fixed point theorems for two hybrid pairs of maps satisfying ψ+φ contractive condition in fuzzy metric spaces. AMS Mathematics Subject Classification (2010): 54H25, 47H10.
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Disease plays a central role in many areas of biomedical research and healthcare. However, the rapid growth of disease and treatment research creates barriers to the knowledge aggregation of PubMed database. Thus, a framework of disease mention recognition and normalization has become increasingly important for biomedical text mining. In this work, we utilize conditional random fields (CRFs) to...
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