Feature engineering for MEDLINE citation categorization with MeSH
Background Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representa...
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| Published in: | BMC bioinformatics Vol. 16; no. 1; p. 113 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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BioMed Central
08.04.2015
BioMed Central Ltd |
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| ISSN: | 1471-2105, 1471-2105 |
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| Abstract | Background
Research in biomedical text categorization has mostly used the
bag-of-words
representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations.
Results
Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance.
Conclusions
We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the
bag-of-words
representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system. |
|---|---|
| AbstractList | Background
Research in biomedical text categorization has mostly used the
bag-of-words
representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations.
Results
Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance.
Conclusions
We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the
bag-of-words
representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system. Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance. We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system. Background Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations. Results Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance. Conclusions We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system. Keywords: Text categorization, Feature engineering, Biomedical literature, MeSH indexing BACKGROUNDResearch in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations.RESULTSTraditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance.CONCLUSIONSWe conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system. Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations. Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance. We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system. |
| ArticleNumber | 113 |
| Audience | Academic |
| Author | Aronson, Alan R Mork, James G Jimeno Yepes, Antonio Jose Carrillo-de-Albornoz, Jorge Plaza, Laura |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25887792$$D View this record in MEDLINE/PubMed |
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| Keywords | MeSH indexing Text categorization Feature engineering Biomedical literature |
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| References | DD Lewis (539_CR17) 1992 539_CR43 539_CR44 539_CR42 539_CR47 539_CR48 539_CR45 539_CR46 539_CR40 WW Cohen (539_CR31) 1995; 32 S Sohn (539_CR22) 2008; 15 C Apte (539_CR12) 1994; 12 539_CR18 539_CR19 539_CR16 P Ruch (539_CR7) 2007; 76 539_CR10 539_CR54 539_CR11 539_CR55 539_CR52 539_CR53 539_CR14 539_CR58 539_CR15 AR Aronson (539_CR21) 2004; 11 539_CR13 539_CR57 O Bodenreider (539_CR4) 2004; 32 539_CR50 539_CR51 CM Tan (539_CR38) 2002; 38 L Plaza (539_CR49) 2011; 53 539_CR2 539_CR3 539_CR29 539_CR5 539_CR6 539_CR28 539_CR8 539_CR9 539_CR20 539_CR25 AR Aronson (539_CR34) 2010; 17 539_CR26 539_CR23 539_CR24 539_CR39 A Jimeno-Yepes (539_CR56) 2012; 6 L Smith (539_CR41) 2004; 20 539_CR32 539_CR33 539_CR30 539_CR36 539_CR37 539_CR35 JR Herskovica (539_CR27) 2011; 80 F Sebastiani (539_CR1) 2002; 34 9929332 - Proc AMIA Symp. 1998;:815-9 17971238 - BMC Bioinformatics. 2007;8:423 15073016 - Bioinformatics. 2004 Sep 22;20(14):2320-1 16815739 - Int J Med Inform. 2007 Feb-Mar;76(2-3):195-200 14681409 - Nucleic Acids Res. 2004 Jan 1;32(Database issue):D267-70 22195224 - AMIA Annu Symp Proc. 2011;2011:1583-92 16779160 - AMIA Annu Symp Proc. 2005;:849-53 23725347 - BMC Bioinformatics. 2013;14:171 8563418 - Proc Annu Symp Comput Appl Med Care. 1995;:878-82 19025687 - BMC Bioinformatics. 2008;9 Suppl 11:S11 15360816 - Stud Health Technol Inform. 2004;107(Pt 1):268-72 23537461 - BMC Bioinformatics. 2013;14:113 16165395 - Int J Med Inform. 2006 Jun;75(6):488-95 23445074 - BMC Bioinformatics. 2013;14:71 16779043 - AMIA Annu Symp Proc. 2005;:266-70 20442139 - J Am Med Inform Assoc. 2010 May-Jun;17(3):229-36 11825149 - Proc AMIA Symp. 2001;:17-21 21752612 - Artif Intell Med. 2011 Sep;53(1):1-14 21439897 - Int J Med Inform. 2011 Jun;80(6):431-41 18436913 - J Am Med Inform Assoc. 2008 Jul-Aug;15(4):546-53 24551371 - AMIA Annu Symp Proc. 2013;2013:709-18 21464855 - J Med Libr Assoc. 2011 Apr;99(2):160-3 |
| References_xml | – ident: 539_CR51 doi: 10.1007/BFb0026683 – volume: 20 start-page: 2320 issue: 14 year: 2004 ident: 539_CR41 publication-title: Bioinformatics (Oxford, England) doi: 10.1093/bioinformatics/bth227 – volume: 32 start-page: D267 issue: suppl 1 year: 2004 ident: 539_CR4 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkh061 – ident: 539_CR9 doi: 10.1186/1471-2105-14-171 – ident: 539_CR29 – ident: 539_CR6 doi: 10.3115/1273073.1273160 – ident: 539_CR3 – ident: 539_CR40 – ident: 539_CR25 – ident: 539_CR2 doi: 10.1145/2110363.2110450 – ident: 539_CR11 – ident: 539_CR30 – volume: 34 start-page: 1 year: 2002 ident: 539_CR1 publication-title: ACM Comput Surveys (CSUR) doi: 10.1145/505282.505283 – volume-title: Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’92 year: 1992 ident: 539_CR17 – ident: 539_CR45 doi: 10.6028/NIST.SP.500-255.genomics-u.hospitalgeneva – ident: 539_CR57 – ident: 539_CR19 – ident: 539_CR46 doi: 10.1016/j.ijmedinf.2005.06.007 – volume: 12 start-page: 233 year: 1994 ident: 539_CR12 publication-title: ACM Trans Inf Syst doi: 10.1145/183422.183423 – ident: 539_CR18 doi: 10.3115/1118693.1118704 – ident: 539_CR20 – ident: 539_CR28 – volume: 32 start-page: 124 year: 1995 ident: 539_CR31 publication-title: Advances in inductive logic programming – ident: 539_CR48 doi: 10.3163/1536-5050.99.2.009 – ident: 539_CR10 – ident: 539_CR26 doi: 10.1186/1471-2105-14-113 – ident: 539_CR24 doi: 10.1007/978-1-4471-2099-5_20 – volume: 11 start-page: 268 issue: Pt 1 year: 2004 ident: 539_CR21 publication-title: Medinfo – ident: 539_CR14 – ident: 539_CR39 – ident: 539_CR35 – ident: 539_CR52 – ident: 539_CR23 – volume: 38 start-page: 529 issue: 4 year: 2002 ident: 539_CR38 publication-title: Inf Process Manage doi: 10.1016/S0306-4573(01)00045-0 – volume: 15 start-page: 546 issue: 4 year: 2008 ident: 539_CR22 publication-title: J Am Med Informatics Assoc doi: 10.1197/jamia.M2431 – volume: 17 start-page: 229 issue: 3 year: 2010 ident: 539_CR34 publication-title: J Am Med Informatics Assoc doi: 10.1136/jamia.2009.002733 – ident: 539_CR42 – ident: 539_CR5 – ident: 539_CR8 – volume: 6 start-page: 151 issue: 2 year: 2012 ident: 539_CR56 publication-title: JCSE doi: 10.5626/JCSE.2012.6.2.151 – ident: 539_CR36 doi: 10.1186/1471-2105-8-423 – ident: 539_CR55 – ident: 539_CR13 doi: 10.1145/288627.288651 – volume: 80 start-page: 431 issue: 6 year: 2011 ident: 539_CR27 publication-title: Int J Med Informatics doi: 10.1016/j.ijmedinf.2011.02.008 – volume: 76 start-page: 195 issue: 2 year: 2007 ident: 539_CR7 publication-title: Int J Med Informatics doi: 10.1016/j.ijmedinf.2006.05.002 – volume: 53 start-page: 1 year: 2011 ident: 539_CR49 publication-title: Artif Intelligence Med doi: 10.1016/j.artmed.2011.06.005 – ident: 539_CR15 doi: 10.1145/215206.215365 – ident: 539_CR32 doi: 10.1186/1471-2105-9-S11-S11 – ident: 539_CR47 doi: 10.1186/1471-2105-14-71 – ident: 539_CR43 – ident: 539_CR53 doi: 10.1145/1102351.1102399 – ident: 539_CR58 – ident: 539_CR54 – ident: 539_CR44 doi: 10.1109/ITA.2013.114 – ident: 539_CR33 – ident: 539_CR16 – ident: 539_CR37 – ident: 539_CR50 – reference: 9929332 - Proc AMIA Symp. 1998;:815-9 – reference: 15073016 - Bioinformatics. 2004 Sep 22;20(14):2320-1 – reference: 21439897 - Int J Med Inform. 2011 Jun;80(6):431-41 – reference: 8563418 - Proc Annu Symp Comput Appl Med Care. 1995;:878-82 – reference: 16815739 - Int J Med Inform. 2007 Feb-Mar;76(2-3):195-200 – reference: 11825149 - Proc AMIA Symp. 2001;:17-21 – reference: 23725347 - BMC Bioinformatics. 2013;14:171 – reference: 21752612 - Artif Intell Med. 2011 Sep;53(1):1-14 – reference: 21464855 - J Med Libr Assoc. 2011 Apr;99(2):160-3 – reference: 16779160 - AMIA Annu Symp Proc. 2005;:849-53 – reference: 24551371 - AMIA Annu Symp Proc. 2013;2013:709-18 – reference: 18436913 - J Am Med Inform Assoc. 2008 Jul-Aug;15(4):546-53 – reference: 17971238 - BMC Bioinformatics. 2007;8:423 – reference: 15360816 - Stud Health Technol Inform. 2004;107(Pt 1):268-72 – reference: 16165395 - Int J Med Inform. 2006 Jun;75(6):488-95 – reference: 23537461 - BMC Bioinformatics. 2013;14:113 – reference: 19025687 - BMC Bioinformatics. 2008;9 Suppl 11:S11 – reference: 23445074 - BMC Bioinformatics. 2013;14:71 – reference: 14681409 - Nucleic Acids Res. 2004 Jan 1;32(Database issue):D267-70 – reference: 22195224 - AMIA Annu Symp Proc. 2011;2011:1583-92 – reference: 20442139 - J Am Med Inform Assoc. 2010 May-Jun;17(3):229-36 – reference: 16779043 - AMIA Annu Symp Proc. 2005;:266-70 |
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| Snippet | Background
Research in biomedical text categorization has mostly used the
bag-of-words
representation. Other more sophisticated representations of text based... Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on... Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using... Background Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based... BACKGROUNDResearch in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on... |
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| SubjectTerms | Abstracting and Indexing as Topic - methods Algorithms Analysis Artificial Intelligence Bioinformatics Biomedical and Life Sciences Comparative analysis Computational Biology/Bioinformatics Computer Appl. in Life Sciences Data mining Humans Information Storage and Retrieval Knowledge-based analysis Life Sciences Medical Subject Headings MEDLINE Microarrays Research Article Semantics |
| Title | Feature engineering for MEDLINE citation categorization with MeSH |
| URI | https://link.springer.com/article/10.1186/s12859-015-0539-7 https://www.ncbi.nlm.nih.gov/pubmed/25887792 https://www.proquest.com/docview/1675869858 https://pubmed.ncbi.nlm.nih.gov/PMC4407321 |
| Volume | 16 |
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