Developing features has been shown crucial to advancing the state-of-the-art in Semantic Role Labeling (SRL). To improve Chinese SRL, we propose a set of additional features, some of which are designed to better capture structural information. Our system achieves 93.49 Fmeasure, a significant improvement over the best reported performance 92.0. We are further concerned with the effect of parsin...

Recently there has been a lot of interest in using Statistical Relational Learning (SRL) models for Information Extraction (IE). One of the important IE tasks is extraction of temporal relations between events and time expressions (timex). SRL methods that use hand-written rules have been proposed for various IE tasks. In contrast, we propose an approach that employs structure learning in SRL t...

Background:  Therapeutic drug monitoring of immunosuppressant drugs are used to monitor drug efficacy and toxicity and to prevent organ transplantation rejection. This study evaluates the analytical performance of semi-automated electrochemiluminescence immunoassays (ECLIA) for cyclosporine (CSA), tacrolimus (TAC) and sirolimus (SRL) on the Roche cobas e 411 analyzer at a major transplant hospi...

Chronic allograft nephropathy (CAN) is a major indication for initiation of sirolimus (SRL) in renal transplantation (TX) to prevent deterioration of renal function. We evaluated whether the CAN score at time of sirolimus rescue (SRL-R) predicts renal allograft function. CAN score is the sum of the following 4 categories: glomerulopathy (cg, 0-3), interstitial fibrosis (ci, 0-3), tubular atroph...

Semantic Role Labeling (SRL) as a Shallow Semantic Parsing causes more and more attention recently. The shortage of manually tagged data is one of main obstacles to supervised learning, which is even serious in SRL. Transductive SVM (TSVM) is a novel semi-supervised learning method special to small mount of tagged data. In this paper, we introduce an application of TSVM in Chinese SRL. To impro...

This paper proposes the use of Lexicalized Tree-Adjoining Grammar (LTAG) formalism as an important additional source of features for the Semantic Role Labeling (SRL) task. Using a set of one-vs-all Support Vector Machines (SVMs), we evaluate these LTAG-based features. Our experiments show that LTAG-based features can improve SRL accuracy significantly. When compared with the best known set of f...

The FLaIT task held within the EvalIta 2011 challenge is here described. Systems were asked to label semantic frames and their arguments as evoked by input predicate words over plain text sentences. Proposed systems are based on a variety of learning techniques and achieve very good results, over 80% of accuracy, in most subtasks. 1 The Frame Labeling Task In the “Frame Labeling over Italian Te...

Metacognition, motivation, and affect are components of self-regulated learning (SRL) that interact. The “metacognitive and affective model of self-regulated learning” (theMASRLmodel) distinguishes two levels of functioning in SRL, namely, the Person level and the Task× Person level. At the Person level interactions between trait-like characteristics such as cognitive ability, metacognitive kno...

The vast majority of work in Machine Learning has focused on propositional data which is assumed to be identically and independently distributed, however, many real world datasets are relational and most real world applications are characterized by the presence of uncertainty and complex relational structure where the data distribution is neither identical nor independent. An emerging research ...

In this paper, we describe strategies we have used to foster selfregulated learning (SRL) in computer programming course in order to enhance the learning process. Strategies include direct instructions, guided practice and feedback, they were implemented using different tools to train student on SRL skills. Students in this course were enrolled in two sections randomly, one section was consider...