Historians and archivists often find and analyze the occurrences of query words in newspaper archives to help answer fundamental questions about society. But much work in text analytics focuses on helping people investigate other textual units, such as events, clusters, ranked documents, entity relationships, or thematic hierarchies. Informed by a study into the needs of historians and archivists, we thus propose ClioQuery , a text analytics system uniquely organized around the analysis of query words in context. ClioQuery applies text simplification techniques from natural language processing to help historians quickly and comprehensively gather and analyze all occurrences of a query word across an archive. It also pairs these new NLP methods with more traditional features like linked views and in-text highlighting to help engender trust in summarization techniques. We evaluate ClioQuery with two separate user studies, in which historians explain how ClioQuery ’s novel text simplification features can help facilitate historical research. We also evaluate with a separate quantitative comparison study, which shows that ClioQuery helps crowdworkers find and remember historical information. Such results suggest possible new directions for text analytics in other query-oriented settings.

Abram Handler, Brendan O’Connor. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Jack Merullo, Luke Yeh, Abram Handler, Alvin Grissom II, Brendan O’Connor, Mohit Iyyer. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Abram Handler, Brendan O’Connor. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

We propose a new, socially-impactful task for natural language processing: from a news corpus, extract names of persons who have been killed by police.We present a newly collected police fatality corpus, which we release publicly, and present a model to solve this problem that uses EM-based distant supervision with logistic regression and convolutional neural network classifiers.Our model outperforms two off-the-shelf event extractor systems, and it can suggest candidate victim names in some cases faster than one of the major manually-collected police fatality databases.