Ayah Zirikly


2024

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Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
Andrew Yates | Bart Desmet | Emily Prud’hommeaux | Ayah Zirikly | Steven Bedrick | Sean MacAvaney | Kfir Bar | Molly Ireland | Yaakov Ophir
Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)

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Overview of the CLPsych 2024 Shared Task: Leveraging Large Language Models to Identify Evidence of Suicidality Risk in Online Posts
Jenny Chim | Adam Tsakalidis | Dimitris Gkoumas | Dana Atzil-Slonim | Yaakov Ophir | Ayah Zirikly | Philip Resnik | Maria Liakata
Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)

We present the overview of the CLPsych 2024 Shared Task, focusing on leveraging open source Large Language Models (LLMs) for identifying textual evidence that supports the suicidal risk level of individuals on Reddit. In particular, given a Reddit user, their pre- determined suicide risk level (‘Low’, ‘Mod- erate’ or ‘High’) and all of their posts in the r/SuicideWatch subreddit, we frame the task of identifying relevant pieces of text in their posts supporting their suicidal classification in two ways: (a) on the basis of evidence highlighting (extracting sub-phrases of the posts) and (b) on the basis of generating a summary of such evidence. We annotate a sample of 125 users and introduce evaluation metrics based on (a) BERTScore and (b) natural language inference for the two sub-tasks, respectively. Finally, we provide an overview of the system submissions and summarise the key findings.

2023

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Characterization of Stigmatizing Language in Medical Records
Keith Harrigian | Ayah Zirikly | Brant Chee | Alya Ahmad | Anne Links | Somnath Saha | Mary Catherine Beach | Mark Dredze
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.

2022

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Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology
Ayah Zirikly | Dana Atzil-Slonim | Maria Liakata | Steven Bedrick | Bart Desmet | Molly Ireland | Andrew Lee | Sean MacAvaney | Matthew Purver | Rebecca Resnik | Andrew Yates
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

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Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks
Ayah Zirikly | Mark Dredze
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

Models of mental health based on natural language processing can uncover latent signals of mental health from language. Models that indicate whether an individual is depressed, or has other mental health conditions, can aid in diagnosis and treatment. A critical aspect of integration of these models into the clinical setting relies on explaining their behavior to domain experts. In the case of mental health diagnosis, clinicians already rely on an assessment framework to make these decisions; that framework can help a model generate meaningful explanations. In this work we propose to use PHQ-9 categories as an auxiliary task to explaining a social media based model of depression. We develop a multi-task learning framework that predicts both depression and PHQ-9 categories as auxiliary tasks. We compare the quality of explanations generated based on the depression task only, versus those that use the predicted PHQ-9 categories. We find that by relying on clinically meaningful auxiliary tasks, we produce more meaningful explanations.

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Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts
Adam Tsakalidis | Jenny Chim | Iman Munire Bilal | Ayah Zirikly | Dana Atzil-Slonim | Federico Nanni | Philip Resnik | Manas Gaur | Kaushik Roy | Becky Inkster | Jeff Leintz | Maria Liakata
Proceedings of the Eighth Workshop on Computational Linguistics and Clinical Psychology

We provide an overview of the CLPsych 2022 Shared Task, which focusses on the automatic identification of ‘Moments of Change’ in lon- gitudinal posts by individuals on social media and its connection with information regarding mental health . This year’s task introduced the notion of longitudinal modelling of the text generated by an individual online over time, along with appropriate temporally sen- sitive evaluation metrics. The Shared Task con- sisted of two subtasks: (a) the main task of cap- turing changes in an individual’s mood (dras- tic changes-‘Switches’- and gradual changes -‘Escalations’- on the basis of textual content shared online; and subsequently (b) the sub- task of identifying the suicide risk level of an individual – a continuation of the CLPsych 2019 Shared Task– where participants were encouraged to explore how the identification of changes in mood in task (a) can help with assessing suicidality risk in task (b).

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A Whole-Person Function Dictionary for the Mobility, Self-Care and Domestic Life Domains: a Seedset Expansion Approach
Ayah Zirikly | Bart Desmet | Julia Porcino | Jonathan Camacho Maldonado | Pei-Shu Ho | Rafael Jimenez Silva | Maryanne Sacco
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Whole-person functional limitations in the areas of mobility, self-care and domestic life affect a majority of individuals with disabilities. Detecting, recording and monitoring such limitations would benefit those individuals, as well as research on whole-person functioning and general public health. Dictionaries of terms related to whole-person function would enable automated identification and extraction of relevant information. However, no such terminologies currently exist, due in part to a lack of standardized coding and their availability mainly in free text clinical notes. In this paper, we introduce terminologies of whole-person function in the domains of mobility, self-care and domestic life, built and evaluated using a small set of manually annotated clinical notes, which provided a seedset that was expanded using a mix of lexical and deep learning approaches.

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Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
Thong Nguyen | Andrew Yates | Ayah Zirikly | Bart Desmet | Arman Cohan
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9’s symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect.

2020

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Development of Natural Language Processing Tools to Support Determination of Federal Disability Benefits in the U.S.
Bart Desmet | Julia Porcino | Ayah Zirikly | Denis Newman-Griffis | Guy Divita | Elizabeth Rasch
Proceedings of the 1st Workshop on Language Technologies for Government and Public Administration (LT4Gov)

The disability benefits programs administered by the US Social Security Administration (SSA) receive between 2 and 3 million new applications each year. Adjudicators manually review hundreds of evidence pages per case to determine eligibility based on financial, medical, and functional criteria. Natural Language Processing (NLP) technology is uniquely suited to support this adjudication work and is a critical component of an ongoing inter-agency collaboration between SSA and the National Institutes of Health. This NLP work provides resources and models for document ranking, named entity recognition, and terminology extraction in order to automatically identify documents and reports pertinent to a case, and to allow adjudicators to search for and locate desired information quickly. In this paper, we describe our vision for how NLP can impact SSA’s adjudication process, present the resources and models that have been developed, and discuss some of the benefits and challenges in working with large-scale government data, and its specific properties in the functional domain.

2019

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CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts
Ayah Zirikly | Philip Resnik | Özlem Uzuner | Kristy Hollingshead
Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology

The shared task for the 2019 Workshop on Computational Linguistics and Clinical Psychology (CLPsych’19) introduced an assessment of suicide risk based on social media postings, using data from Reddit to identify users at no, low, moderate, or severe risk. Two variations of the task focused on users whose posts to the r/SuicideWatch subreddit indicated they might be at risk; a third task looked at screening users based only on their more everyday (non-SuicideWatch) posts. We received submissions from 15 different teams, and the results provide progress and insight into the value of language signal in helping to predict risk level.

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Classifying the reported ability in clinical mobility descriptions
Denis Newman-Griffis | Ayah Zirikly | Guy Divita | Bart Desmet
Proceedings of the 18th BioNLP Workshop and Shared Task

Assessing how individuals perform different activities is key information for modeling health states of individuals and populations. Descriptions of activity performance in clinical free text are complex, including syntactic negation and similarities to textual entailment tasks. We explore a variety of methods for the novel task of classifying four types of assertions about activity performance: Able, Unable, Unclear, and None (no information). We find that ensembling an SVM trained with lexical features and a CNN achieves 77.9% macro F1 score on our task, and yields nearly 80% recall on the rare Unclear and Unable samples. Finally, we highlight several challenges in classifying performance assertions, including capturing information about sources of assistance, incorporating syntactic structure and negation scope, and handling new modalities at test time. Our findings establish a strong baseline for this novel task, and identify intriguing areas for further research.

2018

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Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings
Han-Chin Shing | Suraj Nair | Ayah Zirikly | Meir Friedenberg | Hal Daumé III | Philip Resnik
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

We report on the creation of a dataset for studying assessment of suicide risk via online postings in Reddit. Evaluation of risk-level annotations by experts yields what is, to our knowledge, the first demonstration of reliability in risk assessment by clinicians based on social media postings. We also introduce and demonstrate the value of a new, detailed rubric for assessing suicide risk, compare crowdsourced with expert performance, and present baseline predictive modeling experiments using the new dataset, which will be made available to researchers through the American Association of Suicidology.

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RSDD-Time: Temporal Annotation of Self-Reported Mental Health Diagnoses
Sean MacAvaney | Bart Desmet | Arman Cohan | Luca Soldaini | Andrew Yates | Ayah Zirikly | Nazli Goharian
Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic

Self-reported diagnosis statements have been widely employed in studying language related to mental health in social media. However, existing research has largely ignored the temporality of mental health diagnoses. In this work, we introduce RSDD-Time: a new dataset of 598 manually annotated self-reported depression diagnosis posts from Reddit that include temporal information about the diagnosis. Annotations include whether a mental health condition is present and how recently the diagnosis happened. Furthermore, we include exact temporal spans that relate to the date of diagnosis. This information is valuable for various computational methods to examine mental health through social media because one’s mental health state is not static. We also test several baseline classification and extraction approaches, which suggest that extracting temporal information from self-reported diagnosis statements is challenging.

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Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility
Denis Newman-Griffis | Ayah Zirikly
Proceedings of the BioNLP 2018 workshop

Functioning is gaining recognition as an important indicator of global health, but remains under-studied in medical natural language processing research. We present the first analysis of automatically extracting descriptions of patient mobility, using a recently-developed dataset of free text electronic health records. We frame the task as a named entity recognition (NER) problem, and investigate the applicability of NER techniques to mobility extraction. As text corpora focused on patient functioning are scarce, we explore domain adaptation of word embeddings for use in a recurrent neural network NER system. We find that embeddings trained on a small in-domain corpus perform nearly as well as those learned from large out-of-domain corpora, and that domain adaptation techniques yield additional improvements in both precision and recall. Our analysis identifies several significant challenges in extracting descriptions of patient mobility, including the length and complexity of annotated entities and high linguistic variability in mobility descriptions.

2016

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The GW/UMD CLPsych 2016 Shared Task System
Ayah Zirikly | Varun Kumar | Philip Resnik
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology

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The GW/LT3 VarDial 2016 Shared Task System for Dialects and Similar Languages Detection
Ayah Zirikly | Bart Desmet | Mona Diab
Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3)

This paper describes the GW/LT3 contribution to the 2016 VarDial shared task on the identification of similar languages (task 1) and Arabic dialects (task 2). For both tasks, we experimented with Logistic Regression and Neural Network classifiers in isolation. Additionally, we implemented a cascaded classifier that consists of coarse and fine-grained classifiers (task 1) and a classifier ensemble with majority voting for task 2. The submitted systems obtained state-of-the art performance and ranked first for the evaluation on social media data (test sets B1 and B2 for task 1), with a maximum weighted F1 score of 91.94%.

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The Power of Language Music: Arabic Lemmatization through Patterns
Mohammed Attia | Ayah Zirikly | Mona Diab
Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)

The interaction between roots and patterns in Arabic has intrigued lexicographers and morphologists for centuries. While roots provide the consonantal building blocks, patterns provide the syllabic vocalic moulds. While roots provide abstract semantic classes, patterns realize these classes in specific instances. In this way both roots and patterns are indispensable for understanding the derivational, morphological and, to some extent, the cognitive aspects of the Arabic language. In this paper we perform lemmatization (a high-level lexical processing) without relying on a lookup dictionary. We use a hybrid approach that consists of a machine learning classifier to predict the lemma pattern for a given stem, and mapping rules to convert stems to their respective lemmas with the vocalization defined by the pattern.

2015

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Named Entity Recognition for Arabic Social Media
Ayah Zirikly | Mona Diab
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

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Cross-lingual Transfer of Named Entity Recognizers without Parallel Corpora
Ayah Zirikly | Masato Hagiwara
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

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Named Entity Recognition System for Dialectal Arabic
Ayah Zirikly | Mona Diab
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP)