#Final draft tagger error parsing xml manual#

We test the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Machine learning-based classifications show that the distributional differences can be reproduced by computational linguistic = , Results show that participants from different domains indeed inquire different sets of online sources for the same task.

#Final draft tagger error parsing xml manual#

This scheme was used both to the manual tagging of the corpus and to develop the parser. In this way, we develop a computational linguistic model that correlates GEN-COR abilities with properties of documents consulted for solving the GEN-COR tasks. Previous to the completion of the grammar for the dependency parsing, the design of the Dependency Structure-based Scheme had to be accomplished we concentrated on issues that must be resolved by any practic al system that uses such models. That is, we work with competing classifications to support our findings. A major contribution of our analyses is a multi-part text classification system that contrasts human annotation and rating of the documents used with a semi-automatic classification to predict the document type of web pages. We use machine learning to predict domain-specific group membership based on documents consulted during task solving. We present a computational model for educationally relevant texts that combines features at multiple levels (lexical, syntactic, semantic). Here, we focus on GEN-COR to distinguish between different groups of graduates from the three disciplines in the context of generic COR tasks.

For this reason, we collected data by requiring participants to solve domain-specific (DOM-COR) and generic (GEN-COR) tasks in an authentic Internet-based COR performance assessment (CORA), allowing us to disentangle the assumed components of COR abilities. To investigate this assumption, we focus on commonalities and differences in textual preferences in solving COR-related tasks between graduates/young professionals from three domains. 2016), studies have found evidence supporting their domain-specificity (Toplak et al. Although Critical Online Reasoning (COR) is often viewed as a general competency (e.g.