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Traditionally, parsers are evaluated against gold standard test data. This can cause problems if there is a mismatch between the data structures and representations used by the parser and the gold standard. A particular case in point is German, for which two treebanks (TiGer and TüBa-D/Z) are available with highly different annotation schemes for the acquisition of (e.g.) PCFG parsers. The differences between the TiGer and TüBa-D/Z annotation schemes make fair and unbiased parser evaluation difficult [7, 9, 12]. The resource (TEPACOC) presented in this paper takes a different approach to parser evaluation: instead of providing evaluation data in a single annotation scheme, TEPACOC uses comparable sentences and their annotations for 5 selected key grammatical phenomena (with 20 sentences each per phenomena) from both TiGer and TüBa-D/Z resources. This provides a 2 times 100 sentence comparable testsuite which allows us to evaluate TiGer-trained parsers against the TiGer part of TEPACOC, and TüBa-D/Z-trained parsers against the TüBa-D/Z part of TEPACOC for key phenomena, instead of comparing them against a single (and potentially biased) gold standard. To overcome the problem of inconsistency in human evaluation and to bridge the gap between the two different annotation schemes, we provide an extensive error classification, which enables us to compare parser output across the two different treebanks. In the remaining part of the paper we present the testsuite and describe the grammatical phenomena covered in the data. We discuss the different annotation strategies used in the two treebanks to encode these phenomena and present our error classification of potential parser errors.
Parsing coordinations
(2009)
The present paper is concerned with statistical parsing of constituent structures in German. The paper presents four experiments that aim at improving parsing performance of coordinate structure: 1) reranking the n-best parses of a PCFG parser, 2) enriching the input to a PCFG parser by gold scopes for any conjunct, 3) reranking the parser output for all possible scopes for conjuncts that are permissible with regard to clause structure. Experiment 4 reranks a combination of parses from experiments 1 and 3. The experiments presented show that n- best parsing combined with reranking improves results by a large margin. Providing the parser with different scope possibilities and reranking the resulting parses results in an increase in F-score from 69.76 for the baseline to 74.69. While the F-score is similar to the one of the first experiment (n-best parsing and reranking), the first experiment results in higher recall (75.48% vs. 73.69%) and the third one in higher precision (75.43% vs. 73.26%). Combining the two methods results in the best result with an F-score of 76.69.
Pseudomonas aeruginosa is a human pathogen that causes health-care associated blood stream infections (BSI). Although P. aeruginosa BSI are associated with high mortality rates, the clinical relevance of pathogen-derived prognostic biomarker to identify patients at risk for unfavorable outcome remains largely unexplored. We found novel pathogen-derived prognostic biomarker candidates by applying a multi-omics approach on a multicenter sepsis patient cohort. Multi-level Cox regression was used to investigate the relation between patient characteristics and pathogen features (2298 accessory genes, 1078 core protein levels, 107 parsimony-informative variations in reported virulence factors) with 30-day mortality. Our analysis revealed that presence of the helP gene encoding a putative DEAD-box helicase was independently associated with a fatal outcome (hazard ratio 2.01, p = 0.05). helP is located within a region related to the pathogenicity island PAPI-1 in close proximity to a pil gene cluster, which has been associated with horizontal gene transfer. Besides helP, elevated protein levels of the bacterial flagellum protein FliL (hazard ratio 3.44, p < 0.001) and of a bacterioferritin-like protein (hazard ratio 1.74, p = 0.003) increased the risk of death, while high protein levels of a putative aminotransferase were associated with an improved outcome (hazard ratio 0.12, p < 0.001). The prognostic potential of biomarker candidates and clinical factors was confirmed with different machine learning approaches using training and hold-out datasets. The helP genotype appeared the most attractive biomarker for clinical risk stratification due to its relevant predictive power and ease of detection.