Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

© Springer Nature Switzerland AG 2018. Text documents often contain information relevant for a particular domain in short “snippets”. The social science field of peace and conflict studies is such a domain, where identifying, classifying and tracking drivers of conflict from text sources is important, and snippets are typically classified by human analysts using an ontology. One issue in automating this process is that snippets tend to contain infrequent “rare” terms which lack class-conditional evidence. In this work we develop a method to enrich a bag-of-words model by complementing rare terms in the text to be classified with related terms from a Word Vector model. This method is then combined with standard linear text classification algorithms. By reducing sparseness in the bag-of-words, these enriched models perform better than the baseline classifiers. A second issue is to improve performance on “small” classes having only a few examples, and here we show that Paragraph Vectors outperform the enriched models

Nowcasting for hunger relief: a study of promise and perils

Pitched as an aid to better development decision-making, the website HungerMap LIVE presents composite data on, and machine-learning-derived predictions of, food insecurity in 90 countries. Of its current version, this article asks the following questions: What work is HungerMap LIVE called upon to do in ICT for development (ICT4D) practice? How well is it set up to do that work? Combining technical (both computer science and statistical) and social analysis, this article employs a close reading method drawn from humanities and legal research not usually directed at digital platforms or websites in combination with interview-based techniques. By this means, it scrutinizes HungerMap LIVE’s potential to guide or mislead users and canvasses some elaborations that could enhance its usability. It argues that interdisciplinary research of this kind can counter both the historical and technological determinism troubling the ICT4D field and better position decision-makers to employ machine learning in history- and context-attentive ways