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10.1186/1472-6947-15-S1-S9 http://scihub22266oqcxt.onion/10.1186/1472-6947-15-S1-S9 C4460617!4460617!26043907     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Med+Inform+Decis+Mak 2015 ; 15 (Suppl 1): S9 Nephropedia Template TP {{tp|p=26043907|t=2015. Developing a hybrid dictionary-based bio-entity recognition technique |pdf=|usr=}}{{26043907}} gab.com Text Developing a hybrid dictionary-based bio-entity recognition technique JO: BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=26043907 #FOAvip Background: Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods: This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results: The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions: The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall. Twit Text FOAVip Developing a hybrid dictionary-based bio-entity recognition technique ????BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=26043907 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26043907 #FOAvip English Wikipedia <ref>{{Cite pmid|26043907}}</ref> Developing a hybrid dictionary-based bio-entity recognition technique #MMPMID26043907 Song M; Yu H; Han WS BMC Med Inform Decis Mak 2015[]; 15 (Suppl 1): S9 PMID26043907show ga Background: Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods: This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results: The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions: The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall. äDeveloping a hybrid dictionary-based bio-entity recognition technique (epmc).pdf DeepDyve Pubget Overpricing