Semi-automatic conversion of BioProp semantic annotation to PASBio annotation

<p>Abstract</p> <p>Background</p> <p>Semantic role labeling (SRL) is an important text analysis technique. In SRL, sentences are represented by one or more predicate-argument structures (PAS). Each PAS is composed of a predicate (verb) and several arguments (noun phrases, adverbial phrases, etc.) with different semantic roles, including main arguments (agent or patient) as well as adjunct arguments (time, manner, or location). PropBank is the most widely used PAS corpus and annotation format in the newswire domain. In the biomedical field, however, more detailed and restrictive PAS annotation formats such as PASBio are popular. Unfortunately, due to the lack of an annotated PASBio corpus, no publicly available machine-learning (ML) based SRL systems based on PASBio have been developed. In previous work, we constructed a biomedical corpus based on the PropBank standard called BioProp, on which we developed an ML-based SRL system, BIOSMILE. In this paper, we aim to build a system to convert BIOSMILE's BioProp annotation output to PASBio annotation. Our system consists of BIOSMILE in combination with a BioProp-PASBio rule-based converter, and an additional semi-automatic rule generator.</p> <p>Results</p> <p>Our first experiment evaluated our rule-based converter's performance independently from BIOSMILE performance. The converter achieved an F-score of 85.29%. The second experiment evaluated combined system (BIOSMILE + rule-based converter). The system achieved an F-score of 69.08% for PASBio's 29 verbs.</p> <p>Conclusion</p> <p>Our approach allows PAS conversion between BioProp and PASBio annotation using BIOSMILE alongside our newly developed semi-automatic rule generator and rule-based converter. Our system can match the performance of other state-of-the-art domain-specific ML-based SRL systems and can be easily customized for PASBio application development.</p

Estimating the Permeability of Naturally Structured Soil From Percolation Theory and Pore Space Characteristics Imaged by X-Ray

The saturated hydraulic conductivity of soil, Ks, is a critical parameter in hydrological models that remains notoriously difficult to predict. In this study, we test the capability of a model based on percolation theory and critical path analysis to estimate Ks measured on 95 undisturbed soil cores collected from contrasting soil types. One parameter (the pore geometry factor) was derived by model fitting, while the remaining two parameters (the critical pore diameter, dc, and the effective porosity) were derived from X‐ray computed tomography measurements. The model gave a highly significant fit to the Ks measurements (p < 0.0001) although only ~47% of the variation was explained and the fitted pore geometry factor was approximately 1 to 2 orders of magnitude larger than various theoretical values obtained for idealized porous media and pore network models. Apart from assumptions in the model that might not hold in reality, this could also be attributed to experimental error induced by, for example, air entrapment and changes in the soil pore structure occurring during sample presaturation and the measurement of Ks. Variation in the critical pore diameter, dc, was the dominant source of variation in Ks, which suggests that dc is a suitable length scale for predicting soil permeability. Thus, from the point of view of pedotransfer functions, it could be worthwhile to direct future research toward exploring the correlations of dc with basic soil properties and site attributes.publishedVersio