Publications

2025

1. MISQ

   AI-Augmented Content Validation in Behavioral Research: Development and Evaluation of the RATER System

   Jean-Charles Pillet, Kai Larsen, David Dobolyi, and 4 more authors

   MIS Quarterly, 2025

   Forthcoming

2024

1. IJIM

   Large language models present new questions for decision support

   Abram Handler, Kai R. Larsen, and Richard Hackathorn

   International Journal of Information Management, 2024

   DOI

2022

1. TiiS

   ClioQuery: Interactive Query-Oriented Text Analytics for Comprehensive Investigation of Historical News Archives (Runner-up, Best Paper Award of ACM TiiS 2022)

   Abram Handler, Narges Mahyar, and Brendan O’Connor

   ACM Trans. Interact. Intell. Syst., 2022

   PDF Code

2019

1. EMNLP

   Query-focused Sentence Compression in Linear Time

   Abram Handler and Brendan O’Connor

   In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Nov 2019

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   Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface. This work introduces a new transition-based sentence compression technique developed for such settings. Our query-focused method constructs length and lexically constrained compressions in linear time, by growing a subgraph in the dependency parse of a sentence. This theoretically efficient approach achieves an 11x empirical speedup over baseline ILP methods, while better reconstructing gold constrained shortenings. Such speedups help query-focused applications, because users are measurably hindered by interface lags. Additionally, our technique does not require an ILP solver or a GPU.

2. EMNLP

   Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts

   Jack Merullo, Luke Yeh, Abram Handler, and 3 more authors

   In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Nov 2019

   Abs DOI PDF Supp

   Sports broadcasters inject drama into play-by-play commentary by building team and player narratives through subjective analyses and anecdotes. Prior studies based on small datasets and manual coding show that such theatrics evince commentator bias in sports broadcasts. To examine this phenomenon, we assemble FOOTBALL, which contains 1,455 broadcast transcripts from American football games across six decades that are automatically annotated with 250K player mentions and linked with racial metadata. We identify major confounding factors for researchers examining racial bias in FOOTBALL, and perform a computational analysis that supports conclusions from prior social science studies.

3. EMNLP WS

   Summarizing Relationships for Interactive Concept Map Browsers

   Abram Handler, Premkumar Ganeshkumar, Brendan O’Connor, and 1 more author

   In Proceedings of the 2nd Workshop on New Frontiers in Summarization, Nov 2019

   Abs DOI PDF Code

   Concept maps are visual summaries, structured as directed graphs: important concepts from a dataset are displayed as vertexes, and edges between vertexes show natural language descriptions of the relationships between the concepts on the map. Thus far, preliminary attempts at automatically creating concept maps have focused on building static summaries. However, in interactive settings, users will need to dynamically investigate particular relationships between pairs of concepts. For instance, a historian using a concept map browser might decide to investigate the relationship between two politicians in a news archive. We present a model which responds to such queries by returning one or more short, importance-ranked, natural language descriptions of the relationship between two requested concepts, for display in a visual interface. Our model is trained on a new public dataset, collected for this task.

2018

1. NAACL

   Relational Summarization for Corpus Analysis

   Abram Handler and Brendan O’Connor

   In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), Jun 2018

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   This work introduces a new problem, relational summarization, in which the goal is to generate a natural language summary of the relationship between two lexical items in a corpus, without reference to a knowledge base. Motivated by the needs of novel user interfaces, we define the task and give examples of its application. We also present a new query-focused method for finding natural language sentences which express relationships. Our method allows for summarization of more than two times more query pairs than baseline relation extractors, while returning measurably more readable output. Finally, to help guide future work, we analyze the challenges of relational summarization using both a news and a social media corpus.

2017

1. KDD WS

   Rookie: A unique approach for exploring news archives

   Abram Handler and Brendan O’Connor

   In Workshop on Data Science + Journalism at KDD 2017, Jun 2017

   Supp
2. EMNLP

   Identifying civilians killed by police with distantly supervised entity-event extraction

   Katherine Keith, Abram Handler, Michael Pinkham, and 3 more authors

   In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Sep 2017

   Abs DOI PDF Code

   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.

2016

1. EMNLP WS

   Bag of What? Simple Noun Phrase Extraction for Text Analysis

   Abram Handler, Matthew Denny, Hanna Wallach, and 1 more author

   In Proceedings of the First Workshop on NLP and Computational Social Science, Nov 2016

   DOI PDF
2. ICML WS

   Visualizing textual models with in-text and word-as-pixel highlighting

   Abram Handler, Su Lin Blodgett, and Brendan T. O’Connor

   ArXiv, Nov 2016

   PDF