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Ed. by Ian Kershaw and Moshe Lewin. Cambridge University Press, Cambridge [etc.] 1997. xii, 369 pp. £45.00. (Paper: £15.95.) KALB, DON. ... Moshe Lewin has contributed both a psychological portrait of Stalin and a study of the role of the bureaucracy. ...doi:10.1017/s002085909967050x fatcat:zs5ofmuidrdmzmykeysmmvaocm
Canadian Journal of History/Annales canadiennes d'histoire XXXIV, April/avril 121 Stalinism and Nazism: Dictatorships in Comparison, edited by Ian Kershaw and Moshe Lewin. ... Theoretically, the book should provide new comparative insights into social, political, and cultural aspects of the Nazi and Stalinist states, especially since the editors, Ian Kershaw and Moshe Lewin, ...doi:10.3138/cjh.34.1.121 fatcat:jc3xlllhhzflrbjwgojz6uqtjy
A CRITIQUE OF THE BARKER, DEMBO, AND LEWIN EXPERIMENT BY IRVIN L. CHILD AND IAN K. ... CHILD AND IAN K. ...doi:10.1037/h0053541 fatcat:vcxxeulbxrge5jp2z5hpep3cty
Applying Natural Language Processing techniques to biomedical text as a potential aid to curation has become the focus of intensive research. However, developing integrated systems which address the curators' real-world needs has been studied less rigorously. This paper addresses this question and presents generic tools developed to assist FlyBase curators. We discuss how they have been integrated into the curation workflow and present initial evidence about their effectiveness.doi:10.1142/9789812772435_0024 fatcat:zanxpnhvibc4zlj5pl4v4a4a5u
Named entity recognition (NER) is an essential step in automatic text processing pipelines. A number of solutions have been presented and evaluated against gold standard corpora (GSC). The benchmarking against GSCs is crucial, but left to the individual researcher. Herewith we present a League Table web site, which benchmarks NER solutions against selected public GSCs, maintains a ranked list and archives the annotated corpus for future comparisons. Results: The web site enables access to thedoi:10.1186/2041-1480-4-19 pmid:24034148 pmcid:PMC4015903 fatcat:blv6w5lrfba7ti5vzcuw67g55a
more »... fferent GSCs in a standardized format (IeXML). Upon submission of the annotated corpus the user has to describe the specification of the used solution and then uploads the annotated corpus for evaluation. The performance of the system is measured against one or more GSCs and the results are then added to the web site ("League Table" ). It displays currently the results from publicly available NER solutions from the Whatizit infrastructure for future comparisons. Conclusion: The League Table enables the evaluation of NER solutions in a standardized infrastructure and monitors the results long-term. For access please go to http://wwwdev.ebi.ac.uk/Rebholz-srv/calbc/assessmentGSC/. Contact: firstname.lastname@example.org.
This paper demonstrates how Drosophila gene name recognition and anaphoric linking of gene names and their products can be achieved using existing information in FlyBase and the Sequence Ontology. Extending an extant approach to gene name recognition we achieved a F-score of 0.8559, and we report a preliminary experiment using a baseline anaphora resolution algorithm. We also present guidelines for annotation of gene mentions in texts and outline how the resulting system is used to aid FlyBase curation.doi:10.1142/9789812701626_0010 fatcat:zopw5gnzobanllledtov36usum
At this stage, the different sections of the document as well as their headings and subheadings are recognised as explained in Lewin  . ...doi:10.1186/1471-2105-9-193 pmid:18410678 pmcid:PMC2375127 fatcat:mkbkdp2ld5ge7ia4s4274odta4
Gene Ontology (GO) terms are often used to assess the results of microarray experiments. The most common way to do this is to perform Fisher's exact tests to find GO terms which are over-represented amongst the genes declared to be differentially expressed in the analysis of the microarray experiment. However, due to the high degree of dependence between GO terms, statistical testing is conservative, and interpretation is difficult. We propose testing groups of GO terms rather than individualdoi:10.1186/1471-2105-7-426 pmid:17018143 pmcid:PMC1622761 fatcat:yzjv7snifjeyhliwqbjoeiv3ay
more »... rms, to increase statistical power, reduce dependence between tests and improve the interpretation of results. We use the publicly available package POSOC to group the terms. Our method finds groups of GO terms significantly over-represented amongst differentially expressed genes which are not found by Fisher's tests on individual GO terms. Grouping Gene Ontology terms improves the interpretation of gene set enrichment for microarray data.
Natasha Jacques, principal pharmacist in diabetes, Heart of England NHS Foundation Trust, Birmingham; Yvonne Johns, patient/carer member; Ian Lewin, consultant diabetologist, North Devon District Hospital ...doi:10.1136/bmj.i1575 pmid:27052837 fatcat:26cvwnqmajg2tk3adrxuytfqxu
DELVAI, BET TADAPURA ASHIM, IAN M. ANDERSON, NICOLA A. ...doi:10.1192/bjp.bp.106.028019 pmid:17541116 fatcat:hu6hql463rd2tc3w6sjwjgrgfu
Motivation: The recognition of named entities (NER) is an elementary task in biomedical text mining. A number of NER solutions have been proposed in recent years, taking advantage of available annotated corpora, terminological resources and machine-learning techniques. Currently, the best performing solutions combine the outputs from selected annotation solutions measured against a single corpus. However, little effort has been spent on a systematic analysis of methods harmonizing thedoi:10.1093/bioinformatics/bts125 pmid:22419783 fatcat:ihh7bn22bvfppkxm7nlyqrfiai
more »... results and measuring against a combination of Gold Standard Corpora (GSCs). Results: We present Totum, a machine learning solution that harmonizes gene/protein annotations provided by heterogeneous NER solutions. It has been optimized and measured against a combination of manually curated GSCs. The performed experiments show that our approach improves the F-measure of state-of-the-art solutions by up to 10% (achieving ≈70%) in exact alignment and 22% (achieving ≈82%) in nested alignment. We demonstrate that our solution delivers reliable annotation results across the GSCs and it is an important contribution towards a homogeneous annotation of MEDLINE abstracts. Availability and implementation: Totum is implemented in Java and its resources are available at
Motivation: Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge.doi:10.1371/journal.pone.0075185 pmid:24124474 pmcid:PMC3790750 fatcat:bz44prbpgbe2bmjbinn4nqcqpi
2020 IEEE Electrical Insulation Conference (EIC)
In order to understand the aging process of paper used in high voltage transformers, transformer grade Kraft paper samples were aged in a fan oven in order to access the full range of DP values from 1100 (new) to ~200 (end of life). The absence of significant oxidation was verified by infrared spectroscopy and the mechanical and dielectric properties were assessed as a function of DP. Whilst the dielectric properties (in the absence of water) were unaffected by aging, the tensile strength wasdoi:10.1109/eic47619.2020.9158661 fatcat:k6ebpnvtvjdr5flaj6fdc7kv6m
more »... duced. This confirms studies in the literature which show that most transformer breakdowns occur through mechanical failure of the paper and crucially, provides a mechanism of providing paper samples of known DP for subsequent exposure to oil flow.
Motivation: The identification of protein and gene names (PGNs) from the scientific literature requires semantic resources: Terminological and lexical resources deliver the term candidates into PGN tagging solutions and the gold standard corpora (GSC) train them to identify term parameters and contextual features. Ideally all three resources, i.e. corpora, lexica and taggers, cover the same domain knowledge, and thus support identification of the same types of PGNs and cover all of them.doi:10.1186/2041-1480-4-28 pmid:24112383 pmcid:PMC4021975 fatcat:r3pwqrgypzcphnbgscrxyeyeam
more »... nately, none of the three serves as a predominant standard and for this reason it is worth exploring, how these three resources comply with each other. We systematically compare different PGN taggers against publicly available corpora and analyze the impact of the included lexical resource in their performance. In particular, we determine the performance gains through false positive filtering, which contributes to the disambiguation of identified PGNs. Results: In general, machine learning approaches (ML-Tag) for PGN tagging show higher F1-measure performance against the BioCreative-II and Jnlpba GSCs (exact matching), whereas the lexicon based approaches (LexTag) in combination with disambiguation methods show better results on FsuPrge and PennBio. The ML-Tag solutions balance precision and recall, whereas the LexTag solutions have different precision and recall profiles at the same F1-measure across all corpora. Higher recall is achieved with larger lexical resources, which also introduce more noise (false positive results). The ML-Tag solutions certainly perform best, if the test corpus is from the same GSC as the training corpus. As expected, the false negative errors characterize the test corpora and -on the other hand -the profiles of the false positive mistakes characterize the tagging solutions. Lex-Tag solutions that are based on a large terminological resource in combination with false positive filtering produce better results, which, in addition, provide concept identifiers from a knowledge source in contrast to ML-Tag solutions. Conclusion: The standard ML-Tag solutions achieve high performance, but not across all corpora, and thus should be trained using several different corpora to reduce possible biases. The LexTag solutions have different profiles for their precision and recall performance, but with similar F1-measure. This result is surprising and suggests that they cover a portion of the most common naming standards, but cope differently with the term variability across the corpora. The false positive filtering applied to LexTag solutions does improve the results by increasing their precision without compromising significantly their recall. The harmonisation of the annotation schemes in combination with standardized lexical resources in the tagging solutions will enable their comparability and will pave the way for a shared standard.
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