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10.1186/s12859-015-0539-7 http://scihub22266oqcxt.onion/10.1186/s12859-015-0539-7 C4407321!4407321 !25887792     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=25887792 &cmd=llinks): Failed to open stream: HTTP request failed! 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Feature engineering for MEDLINE citation categorization with MeSH |pdf=|usr=}}{{25887792 }} gab.com Text Feature engineering for MEDLINE citation categorization with MeSH JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=25887792 #FOAvip 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. Twit Text FOAVip Feature engineering for MEDLINE citation categorization with MeSH ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=25887792 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25887792 #FOAvip English Wikipedia <ref>{{Cite pmid|25887792 }}</ref> Feature engineering for MEDLINE citation categorization with MeSH #MMPMID25887792 Jimeno Yepes AJ ; Plaza L ; Carrillo-de-Albornoz J ; Mork JG ; Aronson AR BMC Bioinformatics 2015[Apr]; 16 (ä): 113 PMID25887792 show ga 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. |*Algorithms [MESH] |*Information Storage and Retrieval [MESH] |*MEDLINE [MESH] |*Medical Subject Headings [MESH] |Abstracting and Indexing/*methods [MESH] |Artificial Intelligence [MESH] |Humans [MESH]Feature engineering for MEDLINE citation categorization with MeSH (epmc).pdf DeepDyve Pubget Overpricing