The Gun Violence Database: A new task and data set for NLP

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1018–1024, Austin, Texas, November 1-5, 2016. c⃝2016 Association for Computational Linguistics The Gun Violence Database: A new task and data set for NLP Ellie Pavlick1 Heng Ji2 Xiaoman Pan2 Chris Callison-Burch1 1Computer and Information Science Department, University of Pennsylvania 2Computer Science Department, Rensselaer Polytechnic Institute Abstract We argue that NLP researchers are especially well-positioned to contribute to the national discussion about gun violence. Reasoning about the causes and outcomes of gun violence is typically dominated by politics and emo- tion, and data-driven research on the topic is stymied by a shortage of data and a lack of federal funding. However, data abounds in the form of unstructured text from news articles across the country. This is an ideal application of NLP technologies, such as relation extrac- tion, coreference resolution, and event detec- tion. We introduce a new and growing dataset, the Gun Violence Database, in order to facil- itate the adaptation of current NLP technolo- gies to the domain of gun violence, thus en- abling better social science research on this important and under-resourced problem. 1 Introduction The field of natural language processing often touts its mission as harnessing the information contained in human language: taking unstructured data in the form of speech and text, and transforming it into information that can be searched, categorized, and reasoned about. This is an ambitious goal, and the current state-of-the-art of language technology has made impressive strides towards understanding “who did what to whom, when, where, how, and why” (Kao and Poteet, 2007). Advances in NLP have enabled us to read news in real time (Petrovi´c et al., 2010), identify the key players (Ruppenhofer et al., 2009), recognize the relationships between them (Riedel et al., 2013), summarize the new informa- tion (Wang et al., 2016), update central databases (Singhal, 2012), and use those databases to answer questions about the world (Berant et al., 2013). Although these technological achievements are profound, often times we as researchers apply them to somewhat trivial settings like learning about the latest Hollywood divorces (Wijaya et al., 2015) or learning silly facts about the world, like that ⟨white suites, will never go out of, style⟩ (Fader et al., 2011). In this paper, we call the attention of the NLP community to one particularly good use case of our current technology, which could have profound pol- icy implications: gun violence research. Gun violence is an undeniable problem in the United States, but its causes are poorly understood, and attempts to reason about solutions are often marred by emotions and political bias. Research into the factors that cause and prevent gun violence is limited by the fact that data collection is expensive, and political agendas have all but eliminated fund- ing on the topic. However, in the form of unstruc- tured natural language published daily by newspa- pers across the country, data abounds. We argue that this is the exact type of information that NLP is de- signed …

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