ClioQuery: Interactive Query-Oriented Text Analytics for Comprehensive Investigation of Historical News Archives (Runner-up, Best Paper Award of ACM TiiS 2022)
Handler, Abram,
Mahyar, Narges,
and O’Connor, Brendan
@phdthesis{handlerphdthesis,author = {Handler, Abram},title = {Natural Language Processing for Lexical Corpus Analysis},school = {University of Massachusetts, Amherst},year = {2021},abbr = {Dissertation},pdf = {https://www.abehandler.com/assets/pdf/dissertation.pdf}}
2019
EMNLP
Query-focused Sentence Compression in Linear Time
Handler, Abram,
and O’Connor, Brendan
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)
2019
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.
@inproceedings{handler-oconnor-2019-query,title = {Query-focused Sentence Compression in Linear Time},author = {Handler, Abram and O{'}Connor, Brendan},booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},month = nov,year = {2019},abbr = {EMNLP},address = {Hong Kong, China},publisher = {Association for Computational Linguistics},url = {https://www.aclweb.org/anthology/D19-1612},doi = {10.18653/v1/D19-1612},pages = {5969--5975},code = {https://github.com/slanglab/qsb}}
EMNLP
Investigating Sports Commentator Bias within a Large Corpus of American Football Broadcasts
Merullo, Jack,
Yeh, Luke,
Handler, Abram,
Grissom II, Alvin,
O’Connor, Brendan,
and Iyyer, Mohit
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)
2019
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.
@inproceedings{merullo-etal-2019-investigating,title = {Investigating Sports Commentator Bias within a Large Corpus of {A}merican Football Broadcasts},author = {Merullo, Jack and Yeh, Luke and Handler, Abram and Grissom II, Alvin and O{'}Connor, Brendan and Iyyer, Mohit},booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},month = nov,year = {2019},supp = {https://github.com/jmerullo/football},abbr = {EMNLP},pdf = {https://www.aclweb.org/anthology/D19-1666.pdf},address = {Hong Kong, China},publisher = {Association for Computational Linguistics},url = {https://www.aclweb.org/anthology/D19-1666},doi = {10.18653/v1/D19-1666},pages = {6355--6361}}
EMNLP WS
Summarizing Relationships for Interactive Concept Map Browsers
Handler, Abram,
Ganeshkumar, Premkumar,
O’Connor, Brendan,
and AlTantawy, Mohamed
In Proceedings of the 2nd Workshop on New Frontiers in Summarization
2019
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.
@inproceedings{handler-etal-2019-summarizing,title = {Summarizing Relationships for Interactive Concept Map Browsers},author = {Handler, Abram and Ganeshkumar, Premkumar and O{'}Connor, Brendan and AlTantawy, Mohamed},booktitle = {Proceedings of the 2nd Workshop on New Frontiers in Summarization},month = nov,year = {2019},abbr = {EMNLP WS},pdf = {https://www.aclweb.org/anthology/D19-5414.pdf},address = {Hong Kong, China},publisher = {Association for Computational Linguistics},url = {https://www.aclweb.org/anthology/D19-5414},doi = {10.18653/v1/D19-5414},pages = {111--115},code = {https://github.com/slanglab/concept_maps_newsum19}}
2018
NAACL
Relational Summarization for Corpus Analysis
Handler, Abram,
and O’Connor, Brendan
In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
2018
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.
@inproceedings{handler-oconnor-2018-relational,title = {Relational Summarization for Corpus Analysis},author = {Handler, Abram and O{'}Connor, Brendan},booktitle = {Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},month = jun,year = {2018},address = {New Orleans, Louisiana},publisher = {Association for Computational Linguistics},url = {https://www.aclweb.org/anthology/N18-1159},doi = {10.18653/v1/N18-1159},pages = {1760--1769},abbr = {NAACL},pdf = {example_pdf.pdf}}
2017
KDD WS
Rookie: A unique approach for exploring news archives
Handler, Abram,
and O’Connor, Brendan
In Workshop on Data Science + Journalism at KDD 2017
2017
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.
@inproceedings{keith-etal-2017-identifying,title = {Identifying civilians killed by police with distantly supervised entity-event extraction},author = {Keith, Katherine and Handler, Abram and Pinkham, Michael and Magliozzi, Cara and McDuffie, Joshua and O{'}Connor, Brendan},booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},month = sep,year = {2017},abbr = {EMNLP},pdf = {https://www.aclweb.org/anthology/D17-1163.pdf},address = {Copenhagen, Denmark},publisher = {Association for Computational Linguistics},url = {https://www.aclweb.org/anthology/D17-1163},doi = {10.18653/v1/D17-1163},pages = {1547--1557},code = {http://slanglab.cs.umass.edu/PoliceKillingsExtraction/}}
2016
EMNLP WS
Bag of What? Simple Noun Phrase Extraction for Text Analysis
Handler, Abram,
Denny, Matthew,
Wallach, Hanna,
and O’Connor, Brendan
In Proceedings of the First Workshop on NLP and Computational Social Science
2016
@inproceedings{handler-etal-2016-bag,title = {Bag of What? Simple Noun Phrase Extraction for Text Analysis},author = {Handler, Abram and Denny, Matthew and Wallach, Hanna and O{'}Connor, Brendan},booktitle = {Proceedings of the First Workshop on {NLP} and Computational Social Science},abbr = {EMNLP WS},month = nov,pdf = {https://www.aclweb.org/anthology/W16-5615.pdf},year = {2016},address = {Austin, Texas},publisher = {Association for Computational Linguistics},url = {https://www.aclweb.org/anthology/W16-5615},doi = {10.18653/v1/W16-5615},pages = {114--124}}
ICML WS
Visualizing textual models with in-text and word-as-pixel highlighting
Handler, Abram,
Blodgett, Su Lin,
and O’Connor, Brendan T.