Measure for Measure: A Critical Consumers' Guide to Reading Comprehension Assessments for Adolescents

A companion report to Carnegie's Time to Act, analyzes and rates commonly used reading comprehension tests for various elements and purposes. Outlines trends in types of questions, stress on critical thinking, and screening or diagnostic functions

Classification Methods in Context at Theological Libraries: A Case Study

This case study explores issues of interoperability and shared collection management between two libraries – one community and one academic – located within the American Jewish University (AJU). AJU’s choice to use two separate classification systems, Library of Congress and Elazar, respectively, provides a necessary separation of academic and religious context, but limits record access between the two collections. Specifically, this study aims to answer the following core research question: is consolidation into one classification scheme both a realistic and helpful solution for increased interoperability? Examining the history, patron needs, and principles of arrangement in both systems provided further insights regarding shared or coexisting collections between libraries that fulfill more than one role. Suggestions for further research are considered, as they relate to theological collections as well as other context-dependent classification systems

Data Innovation for International Development: An overview of natural language processing for qualitative data analysis

Availability, collection and access to quantitative data, as well as its limitations, often make qualitative data the resource upon which development programs heavily rely. Both traditional interview data and social media analysis can provide rich contextual information and are essential for research, appraisal, monitoring and evaluation. These data may be difficult to process and analyze both systematically and at scale. This, in turn, limits the ability of timely data driven decision-making which is essential in fast evolving complex social systems. In this paper, we discuss the potential of using natural language processing to systematize analysis of qualitative data, and to inform quick decision-making in the development context. We illustrate this with interview data generated in a format of micro-narratives for the UNDP Fragments of Impact project

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17