The Story Workbench: An Extensible Semi-Automatic Text Annotation Tool

Text annotations are of great use to researchers in the language sciences, and much effort has been invested in creating annotated corpora for an wide variety of purposes. Unfortunately, software support for these corpora tends to be quite limited: it is usually ad-hoc, poorly designed and documented, or not released for public use. I describe an annotation tool, the Story Workbench, which provides a generic platform for text annotation. It is free, open-source, cross-platform, and user friendly. It provides a number of common text annotation operations, including representations (e.g., tokens, sentences, parts of speech), functions (e.g., generation of initial annotations by algorithm, checking annotation validity by rule, fully manual manipulation of annotations) and tools (e.g., distributing texts to annotators via version control, merging doubly-annotated texts into a single file). The tool is extensible at many different levels, admitting new representations, algorithm, and tools. I enumerate ten important features and illustrate how they support the annotation process at three levels: (1) annotation of individual texts by a single annotator, (2) double-annotation of texts by two annotators and an adjudicator, and (3) annotation scheme development. The Story Workbench is scheduled for public release in March 2012. Text annotations are of great use to researchers in the language sciences: a large fraction of that work relies on annotated data to build, train, or test their systems. Good examples are the Penn Treebank, which catalyzed work in developing statistical syntactic parsers, and PropBank, which did the same for semantic role labeling. It is not an exaggeration to say that annotated corpora are a central resource for these fields, and are only growing in importance. Work on narrative shares many of the same problems, and as a consequence has much to gain from advances in language annotation tools and techniques. Despite the importance of annotated data, there remains a missing link: software support is not given nearly the same amount of attention as the annotations themselves. Researchers usually release only the data; if they release any tools at all, they are usually ad-hoc, poorly designed and  Tools do not build on one another. The language sciences need to move to a standard where, if annotated data is released, software for accessing and creating the data are released as a matter of course. Researchers should prepare for it, reviewers should demand it, and readers should expect it. One way of facilitating this is to lower the barrier for creating tools. Many of the phases of the annotation cycle are the same no matter what sort of annotation you are doing a freely available tool, or suite of tools, to support these phases would go a long way. I describe the Story Workbench (Finlayson 2008), a major step toward just such a tool suite. The Story Workbench is free, open-source, extensible, cross-platform, and user friendly. It is a working piece of software, having been in beta testing for over three years, with a public release scheduled for March 2012. It has been used by more than 12 annotators to annotate over 100k words across 17 representations. Two corpora have been created so far with it: the UMIREC corpus (Hervas and Finlayson 2010) comprising 25k words of news and folktales annotated for referring expression structure, and 18k words of Russian folktales annotated in all 17 different representations. The Story Workbench is especially interesting to researchers working on narrative. Understanding a narrative requires not just one representation, not just two, but a dozen or more. The Story Workbench was created specifically to overcome that problem, but is now finding application beyond the realm of narrative research. In particular, in the next section I describe three phases of the annotation process; many, if not most, annotation studies move through these phases. In the next section I enumerate some of the more important features of the Story Workbench, and show how these support the phases. Three Loops of the Annotation Process Conceptually, the process of producing a gold-standard annotated corpus can be split into at least three nested loops. In the widest, top-most loop the researchers design and vet the annotation scheme and annotation tool; embedded therein is the middle loop, where annotation teams produce goldannotated texts; embedded within that is the loop of the individual annotator working on individual texts. These nested loops are illustrated in Figure 1. 21 AAAI Technical Report WS-11-18