This paper describes our construction of named-entity recognition (NER) systems in two Western Iranian languages, Sorani Kurdish and Tajik, as a part of a pilot study of “Linguistic Rapid Response” to potential emergency humanitarian relief situations. In the absence of large annotated corpora, parallel corpora, treebanks, bilingual lexica, etc., we found the following to be effective: exploiting distributional regularities in monolingual data, projecting information across closely related languages, and utilizing human linguist judgments. We show promising results on both a four-month exercise in Sorani and a two-day exercise in Tajik, achieved with minimal annotation costs.

This paper introduces EasyTree, a dynamic graphical tool for dependency tree annotation. Built in JavaScript using the popular D3 data visualization library, EasyTree allows annotators to construct and label trees entirely by manipulating graphics, and then export the corresponding data in JSON format. Human users are thus able to annotate in an intuitive way without compromising the machine-compatibility of the output. EasyTree has a number of features to assist annotators, including color-coded part-of-speech indicators and optional translation displays. It can also be customized to suit a wide range of projects; part-of-speech categories, edge labels, and many other settings can be edited from within the GUI. The system also utilizes UTF-8 encoding and properly handles both left-to-right and right-to-left scripts. By providing a user-friendly annotation tool, we aim to reduce time spent transforming data or learning to use the software, to improve the user experience for annotators, and to make annotation approachable even for inexperienced users. Unlike existing solutions, EasyTree is built entirely with standard web technologies–JavaScript, HTML, and CSS–making it ideal for web-based annotation efforts, including crowdsourcing efforts.