Eben Holderness


2023

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The Coreference under Transformation Labeling Dataset: Entity Tracking in Procedural Texts Using Event Models
Kyeongmin Rim | Jingxuan Tu | Bingyang Ye | Marc Verhagen | Eben Holderness | James Pustejovsky
Findings of the Association for Computational Linguistics: ACL 2023

We demonstrate that coreference resolution in procedural texts is significantly improved when performing transformation-based entity linking prior to coreference relation identification. When events in the text introduce changes to the state of participating entities, it is often impossible to accurately link entities in anaphoric and coreference relations without an understanding of the transformations those entities undergo. We show how adding event semantics helps to better model entity coreference. We argue that all transformation predicates, not just creation verbs, introduce a new entity into the discourse, as a kind of generalized Result Role, which is typically not textually mentioned. This allows us to model procedural texts as process graphs and to compute the coreference type for any two entities in the recipe. We present our annotation methodology and the corpus generated as well as describe experiments on coreference resolution of entity mentions under a process-oriented model of events.

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Dense Paraphrasing for Textual Enrichment
Jingxuan Tu | Kyeongmin Rim | Eben Holderness | Bingyang Ye | James Pustejovsky
Proceedings of the 15th International Conference on Computational Semantics

Understanding inferences from text requires more than merely recovering surface arguments, adjuncts, or strings associated with the query terms. As humans, we interpret sentences as contextualized components of a narrative or discourse, by both filling in missing information, and reasoning about event consequences. In this paper, we define the process of rewriting a textual expression (lexeme or phrase) such that it reduces ambiguity while also making explicit the underlying semantics that is not (necessarily) expressed in the economy of sentence structure as Dense Paraphrasing (DP). We apply the DP techniques on the English procedural texts from the cooking recipe domain, and provide the scope and design of the application that involves creating a graph representation of events and generating hidden arguments through paraphrasing. We provide insights on how this DP process can enrich a source text by showing that the dense-paraphrased event graph is a good resource to large LLMs such as GPT-3 to generate reliable paraphrases; and by experimenting baselines for automaticDP generation. Finally, we demonstrate the utility of the dataset and event graph structure by providing a case study on the out-of-domain modeling and different DP prompts and GPT models for paraphrasing.

2022

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Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)
Alberto Lavelli | Eben Holderness | Antonio Jimeno Yepes | Anne-Lyse Minard | James Pustejovsky | Fabio Rinaldi
Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)

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SemEval-2022 Task 9: R2VQ – Competence-based Multimodal Question Answering
Jingxuan Tu | Eben Holderness | Marco Maru | Simone Conia | Kyeongmin Rim | Kelley Lynch | Richard Brutti | Roberto Navigli | James Pustejovsky
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

In this task, we identify a challenge that is reflective of linguistic and cognitive competencies that humans have when speaking and reasoning. Particularly, given the intuition that textual and visual information mutually inform each other for semantic reasoning, we formulate a Competence-based Question Answering challenge, designed to involve rich semantic annotation and aligned text-video objects. The task is to answer questions from a collection of cooking recipes and videos, where each question belongs to a “question family” reflecting a specific reasoning competence. The data and task result is publicly available.

2021

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Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis
Eben Holderness | Antonio Jimeno Yepes | Alberto Lavelli | Anne-Lyse Minard | James Pustejovsky | Fabio Rinaldi
Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis

2020

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Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis
Eben Holderness | Antonio Jimeno Yepes | Alberto Lavelli | Anne-Lyse Minard | James Pustejovsky | Fabio Rinaldi
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

2019

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Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)
Eben Holderness | Antonio Jimeno Yepes | Alberto Lavelli | Anne-Lyse Minard | James Pustejovsky | Fabio Rinaldi
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

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Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction
Elena Alvarez-Mellado | Eben Holderness | Nicholas Miller | Fyonn Dhang | Philip Cawkwell | Kirsten Bolton | James Pustejovsky | Mei-Hua Hall
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

Predicting which patients are more likely to be readmitted to a hospital within 30 days after discharge is a valuable piece of information in clinical decision-making. Building a successful readmission risk classifier based on the content of Electronic Health Records (EHRs) has proved, however, to be a challenging task. Previously explored features include mainly structured information, such as sociodemographic data, comorbidity codes and physiological variables. In this paper we assess incorporating additional clinically interpretable NLP-based features such as topic extraction and clinical sentiment analysis to predict early readmission risk in psychiatry patients.

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Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records
Eben Holderness | Philip Cawkwell | Kirsten Bolton | James Pustejovsky | Mei-Hua Hall
Proceedings of the 2nd Clinical Natural Language Processing Workshop

Recently natural language processing (NLP) tools have been developed to identify and extract salient risk indicators in electronic health records (EHRs). Sentiment analysis, although widely used in non-medical areas for improving decision making, has been studied minimally in the clinical setting. In this study, we undertook, to our knowledge, the first domain adaptation of sentiment analysis to psychiatric EHRs by defining psychiatric clinical sentiment, performing an annotation project, and evaluating multiple sentence-level sentiment machine learning (ML) models. Results indicate that off-the-shelf sentiment analysis tools fail in identifying clinically positive or negative polarity, and that the definition of clinical sentiment that we provide is learnable with relatively small amounts of training data. This project is an initial step towards further refining sentiment analysis methods for clinical use. Our long-term objective is to incorporate the results of this project as part of a machine learning model that predicts inpatient readmission risk. We hope that this work will initiate a discussion concerning domain adaptation of sentiment analysis to the clinical setting.

2018

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Analysis of Risk Factor Domains in Psychosis Patient Health Records
Eben Holderness | Nicholas Miller | Kirsten Bolton | Philip Cawkwell | Marie Meteer | James Pustejovsky | Mei Hua-Hall
Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health records (EHRs) span a wide range of topics and vocabulary; therefore, a psychiatric readmission prediction model must begin with a robust and interpretable topic extraction component. We created a data pipeline for using document vector similarity metrics to perform topic extraction on psychiatric EHR data in service of our long-term goal of creating a readmission risk classifier. We show initial results for our topic extraction model and identify additional features we will be incorporating in the future.