2022
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Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction
Mihai Surdeanu
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John Hungerford
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Yee Seng Chan
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Jessica MacBride
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Benjamin Gyori
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Andrew Zupon
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Zheng Tang
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Haoling Qiu
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Bonan Min
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Yan Zverev
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Caitlin Hilverman
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Max Thomas
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Walter Andrews
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Keith Alcock
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Zeyu Zhang
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Michael Reynolds
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Steven Bethard
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Rebecca Sharp
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Egoitz Laparra
Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing
An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none.When one does exist, as the information needs shift, it must be continually extended. This is a slow and tedious task, and one which does not scale well.Here we propose an interactive tool that allows a taxonomy to be built or extended rapidly and with a human in the loop to control precision. We apply insights from text summarization and information extraction to reduce the search space dramatically, then leverage modern pretrained language models to perform contextualized clustering of the remaining concepts to yield candidate nodes for the user to review. We show this allows a user to consider as many as 200 taxonomy concept candidates an hour, to quickly build or extend a taxonomy to better fit information needs.
2021
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A Dashboard for Mitigating the COVID-19 Misinfodemic
Zhengyuan Zhu
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Kevin Meng
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Josue Caraballo
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Israa Jaradat
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Xiao Shi
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Zeyu Zhang
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Farahnaz Akrami
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Haojin Liao
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Fatma Arslan
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Damian Jimenez
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Mohanmmed Samiul Saeef
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Paras Pathak
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Chengkai Li
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations
This paper describes the current milestones achieved in our ongoing project that aims to understand the surveillance of, impact of and intervention on COVID-19 misinfodemic on Twitter. Specifically, it introduces a public dashboard which, in addition to displaying case counts in an interactive map and a navigational panel, also provides some unique features not found in other places. Particularly, the dashboard uses a curated catalog of COVID-19 related facts and debunks of misinformation, and it displays the most prevalent information from the catalog among Twitter users in user-selected U.S. geographic regions. The paper explains how to use BERT models to match tweets with the facts and misinformation and to detect their stance towards such information. The paper also discusses the results of preliminary experiments on analyzing the spatio-temporal spread of misinformation.
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Joint Models for Answer Verification in Question Answering Systems
Zeyu Zhang
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Thuy Vu
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Alessandro Moschitti
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
This paper studies joint models for selecting correct answer sentences among the top k provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems. Our work shows that a critical step to effectively exploiting an answer set regards modeling the interrelated information between pair of answers. For this purpose, we build a three-way multi-classifier, which decides if an answer supports, refutes, or is neutral with respect to another one. More specifically, our neural architecture integrates a state-of-the-art AS2 module with the multi-classifier, and a joint layer connecting all components. We tested our models on WikiQA, TREC-QA, and a real-world dataset. The results show that our models obtain the new state of the art in AS2.
2020
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A Generate-and-Rank Framework with Semantic Type Regularization for Biomedical Concept Normalization
Dongfang Xu
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Zeyu Zhang
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Steven Bethard
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Concept normalization, the task of linking textual mentions of concepts to concepts in an ontology, is challenging because ontologies are large. In most cases, annotated datasets cover only a small sample of the concepts, yet concept normalizers are expected to predict all concepts in the ontology. In this paper, we propose an architecture consisting of a candidate generator and a list-wise ranker based on BERT. The ranker considers pairings of concept mentions and candidate concepts, allowing it to make predictions for any concept, not just those seen during training. We further enhance this list-wise approach with a semantic type regularizer that allows the model to incorporate semantic type information from the ontology during training. Our proposed concept normalization framework achieves state-of-the-art performance on multiple datasets.
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ScienceExamCER: A High-Density Fine-Grained Science-Domain Corpus for Common Entity Recognition
Hannah Smith
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Zeyu Zhang
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John Culnan
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Peter Jansen
Proceedings of the Twelfth Language Resources and Evaluation Conference
Named entity recognition identifies common classes of entities in text, but these entity labels are generally sparse, limiting utility to downstream tasks. In this work we present ScienceExamCER, a densely-labeled semantic classification corpus of 133k mentions in the science exam domain where nearly all (96%) of content words have been annotated with one or more fine-grained semantic class labels including taxonomic groups, meronym groups, verb/action groups, properties and values, and synonyms. Semantic class labels are drawn from a manually-constructed fine-grained typology of 601 classes generated through a data-driven analysis of 4,239 science exam questions. We show an off-the-shelf BERT-based named entity recognition model modified for multi-label classification achieves an accuracy of 0.85 F1 on this task, suggesting strong utility for downstream tasks in science domain question answering requiring densely-labeled semantic classification.