Text Annotation Graphs: Annotating Complex Natural Language Phenomena

Text Annotation Graphs: Annotating Complex Natural Language Phenomena Angus G. Forbes∗, Kristine Lee†, Gus Hahn-Powell‡, Marco A. Valenzuela-Esc´arcega‡, Mihai Surdeanu‡ ∗University of California, Santa Cruz, angus@ucsc.edu †University of Illinois at Chicago, khlee2@uic.edu ‡University of Arizona, {hahnpowell,marcov,msurdeanu}@email.arizona.edu Abstract This paper introduces a new web-based software tool for annotating text, Text Annotation Graphs, or TAG. It provides functionality for representing complex relationships between words and word phrases that are not available in other software tools, including the ability to define and visualize relationships between the relationships themselves (semantic hypergraphs). Additionally, we include a visualization mode in which annotation subgraphs, or semantic summaries, are used to show relationships outside of the sequential context of the text itself. These subgraphs can be used to quickly find similar structures within the current document or external annotated documents. TAG was initially developed to support information extraction tasks on a large database of biomedical articles. However, our software is flexible enough to support a wide range of annotation tasks for many domains. Examples are provided that showcase TAG’s capabilities on morphological parsing and event extraction tasks. Keywords: annotation, event extraction, online NLP tools, text visualization Figure 1: A screen capture of the annotation panel in the TAG software. Here a single sentence of biomedical text is shown, with both semantic (top) and syntactic (bottom) relationships toggled on. 1. Introduction According to systems biologist Arthur Lander, the domi- nant icon of our current era is the “hairball” (Lander, 2010), a visual depiction of a network of such complexity that the edges overlap so densely as to obscure the meaning of the very relationships it is meant to explain. Part of the rea- son for this shift from a more straightforward symbol (such as the double helix, which, while still complex, indicates a more orderly, and potentially predictable machinery) to the hairball, Lander writes, is the rise of the computational tools that make it possible to generate vast amounts of “big data,” as well as the realization that thorny problems gen- erally involve the intersections between data drawn from multiple, interacting systems. Contemporary information extraction approaches seek to capture complex natural language phenomena, such as those found in biomedical literature (Bj¨orne et al., 2010; Valenzuela-Esc´arcega et al., 2015). These biomedical arti- cles present new knowledge regarding biological processes which can be usefully described via a semantic graph that represents the event structure of biochemical reactions. Moreover, these approaches can be used to detect predi- cates (triggers), relations, and events within individual ar- ticles in order to find connections to other corpora and to draw inferences about relationships between texts. We introduce a novel web-based annotation software appli- cation, Text Annotation Graphs, or TAG, that provides a rich set of tools for viewing and editing annotations that cap- ture complex natural language phenomena, such as deeply nested event structures (Figs. 1 and 6), syntactic dependen- cies (bottom portion of Fig. 1), undirected relations, coref- erence resolution, and morphological parses (Fig. 5). TAG provides easily readable output for complex events and …

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