TY - JOUR

T1 - Systematic feature evaluation for gene name recognition

AU - Hakenberg, Jörg

AU - Bickel, Steffen

AU - Plake, Conrad

AU - Brefeld, Ulf

AU - Zahn, Hagen

AU - Faulstich, Lukas

AU - Leser, Ulf

AU - Scheffer, Tobias

PY - 2005/5/24

Y1 - 2005/5/24

N2 - In task 1A of the BioCreAtIvE evaluation, systems had to be devised that recognize words and phrases forming gene or protein names in natural language sentences. We approach this problem by building a word classification system based on a sliding window approach with a Support Vector Machine, combined with a pattern-based post-processing for the recognition of phrases. The performance of such a system crucially depends on the type of features chosen for consideration by the classification method, such as pre- or postfixes, character n-grams, patterns of capitalization, or classification of preceding or following words. We present a systematic approach to evaluate the performance of different feature sets based on recursive feature elimination, RFE. Based on a systematic reduction of the number of features used by the system, we can quantify the impact of different feature sets on the results of the word classification problem. This helps us to identify descriptive features, to learn about the structure of the problem, and to design systems that are faster and easier to understand. We observe that the SVM is robust to redundant features. RFE improves the performance by 0.7%, compared to using the complete set of attributes. Moreover, a performance that is only 2.3% below this maximum can be obtained using fewer than 5% of the features.

AB - In task 1A of the BioCreAtIvE evaluation, systems had to be devised that recognize words and phrases forming gene or protein names in natural language sentences. We approach this problem by building a word classification system based on a sliding window approach with a Support Vector Machine, combined with a pattern-based post-processing for the recognition of phrases. The performance of such a system crucially depends on the type of features chosen for consideration by the classification method, such as pre- or postfixes, character n-grams, patterns of capitalization, or classification of preceding or following words. We present a systematic approach to evaluate the performance of different feature sets based on recursive feature elimination, RFE. Based on a systematic reduction of the number of features used by the system, we can quantify the impact of different feature sets on the results of the word classification problem. This helps us to identify descriptive features, to learn about the structure of the problem, and to design systems that are faster and easier to understand. We observe that the SVM is robust to redundant features. RFE improves the performance by 0.7%, compared to using the complete set of attributes. Moreover, a performance that is only 2.3% below this maximum can be obtained using fewer than 5% of the features.

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=33947304479&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/483dea0a-b292-3915-b21a-ee2b226bc166/

U2 - 10.1186/1471-2105-6-S1-S9

DO - 10.1186/1471-2105-6-S1-S9

M3 - Journal articles

C2 - 15960843

AN - SCOPUS:33947304479

VL - 6

JO - BMC Bioinformatics

JF - BMC Bioinformatics

SN - 1471-2105

IS - SUPPL.1

M1 - S9

ER -