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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 15 of 15 1 A dataset for open event extraction in English Nguyen, K.-H.; Tannier, X.; Ferret, Olivier... In: 10th International Conference on Language Resources and Evaluation, LREC 2016 ; https://hal-cea.archives-ouvertes.fr/cea-01843179 ; 10th International Conference on Language Resources and Evaluation, LREC 2016, May 2016, Portoroz, Slovenia. pp.1939-1943 (2016) BASE Show details 2 Religious Beliefs on Social Media: Large Dataset of Tumblr Posts and Bloggers Consisting of Religion Based Tags ... Agarwal, Swati. - : Mendeley, 2016 BASE Show details 3 SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2) Bordea, Georgeta; Lefever, Els; Buitelaar, Paul. - : Insight Centre for Data Analytics, 2016 BASE Show details 4 Exploratory relation extraction in large multilingual data ... : Explorative Relationsextraktion in mehrsprachigen Massendaten ... Akbik, Alan. - : Technische Universität Berlin, 2016 BASE Show details 5 Arabic Light Stemmer For Better Search Accuracy ... Khedr, Sahar; Sayed, Dina; Hanafy, Ayman. - : Zenodo, 2016 BASE Show details 6 Arabic Light Stemmer For Better Search Accuracy ... Khedr, Sahar; Sayed, Dina; Hanafy, Ayman. - : Zenodo, 2016 BASE Show details 7 A Relationship Extraction Method From Literary Fiction Considering Korean Linguistic Features ... Ahn, Hee-Jeong; Kee-Won Kim; Kim, Seung-Hoon. - : Zenodo, 2016 BASE Show details 8 A Relationship Extraction Method From Literary Fiction Considering Korean Linguistic Features ... Ahn, Hee-Jeong; Kee-Won Kim; Kim, Seung-Hoon. - : Zenodo, 2016 BASE Show details 9 Loay & Safa Dataset ... Sami, Safa. - : Mendeley, 2016 BASE Show details 10 Investigating Techniques for Low Resource Conversational Speech Recognition Laurent, Antoine; Fraga-Silva,Thiago; Lamel,Lori. - 2016 BASE Show details 11 Study of Large Data Resources for Multilingual Training and System Porting (Pub Version, Open Access) Grezl, Frantisek; Egorova,Ekaterina; Karafiat,Martin. - 2016 BASE Show details 12 Microbial Phenomics Information Extractor (MicroPIE): A natural language processing tool for the automated acquisition of prokaryotic phenotypic characters from text sources Mao, Jin; Moore, Lisa R., Ph.D.; Blank, Carrine E.; Wu, Elvis Hsin-Hui; Ackerman, Marcia; Ranade, Sonali; Cui, Hong In: Faculty Publications (2016) Abstract: Background The large-scale analysis of phenomic data (i.e., full phenotypic traits of an organism, such as shape, metabolic substrates, and growth conditions) in microbial bioinformatics has been hampered by the lack of tools to rapidly and accurately extract phenotypic data from existing legacy text in the field of microbiology. To quickly obtain knowledge on the distribution and evolution of microbial traits, an information extraction system needed to be developed to extract phenotypic characters from large numbers of taxonomic descriptions so they can be used as input to existing phylogenetic analysis software packages. Results We report the development and evaluation of Microbial Phenomics Information Extractor (MicroPIE, version 0.1.0). MicroPIE is a natural language processing application that uses a robust supervised classification algorithm (Support Vector Machine) to identify characters from sentences in prokaryotic taxonomic descriptions, followed by a combination of algorithms applying linguistic rules with groups of known terms to extract characters as well as character states. The input to MicroPIE is a set of taxonomic descriptions (clean text). The output is a taxon-by-character matrix—with taxa in the rows and a set of 42 pre-defined characters (e.g., optimum growth temperature) in the columns. The performance of MicroPIE was evaluated against a gold standard matrix and another student-made matrix. Results show that, compared to the gold standard, MicroPIE extracted 21 characters (50%) with a Relaxed F1 score > 0.80 and 16 characters (38%) with Relaxed F1 scores ranging between 0.50 and 0.80. Inclusion of a character prediction component (SVM) improved the overall performance of MicroPIE, notably the precision. Evaluated against the same gold standard, MicroPIE performed significantly better than the undergraduate students. Conclusion MicroPIE is a promising new tool for the rapid and efficient extraction of phenotypic character information from prokaryotic taxonomic descriptions. However, further development, including incorporation of ontologies, will be necessary to improve the performance of the extraction for some character types. Keyword: Algorithm evaluation; Biology; Character matrices; Information extraction; Life Sciences; Machine learning; Microbial phenotypes; Natural language processing; Phenotypic data extraction; Prokaryotic taxonomic descriptions; Support vector machine; Text mining URL: https://digitalcommons.usm.maine.edu/bio-faculty/32 https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1396-8#Abs1 BASE Hide details 13 Coronary artery analysis: Computerâ assisted selection of bestâ quality segments in multipleâ phase coronary CT angiography Zhou, Chuan; Chan, Heang‐ping; Hadjiiski, Lubomir M.. - : American Association of Physicists in Medicine, 2016. : Wiley Periodicals, Inc., 2016 BASE Show details 14 Distantly Supervised Web Relation Extraction for Knowledge Base Population Augenstein, I; Maynard, D; Ciravegna, F In: Semantic Web Journal , 7 (4) pp. 335-349. (2016) (2016) BASE Show details 15 Extraction d'associations lexicales fortes dans les commentaires Nouasria, Abderraouf. - 2016 BASE Show details

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