Search Results for author: Philip Resnik

Found 58 papers, 10 papers with code

Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task

no code implementations NAACL (CLPsych) 2021 Sean MacAvaney, Anjali Mittu, Glen Coppersmith, Jeff Leintz, Philip Resnik

Progress on NLP for mental health — indeed, for healthcare in general — is hampered by obstacles to shared, community-level access to relevant data.

Overview of the CLPsych 2022 Shared Task: Capturing Moments of Change in Longitudinal User Posts

no code implementations NAACL (CLPsych) 2022 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

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 .

Large Language Models are Biased Because They Are Large Language Models

no code implementations19 Jun 2024 Philip Resnik

This paper's primary goal is to provoke thoughtful discussion about the relationship between bias and fundamental properties of large language models.

Language Modelling Large Language Model

A Multimodal Framework for the Assessment of the Schizophrenia Spectrum

no code implementations14 Jun 2024 Gowtham Premananth, Yashish M. Siriwardena, Philip Resnik, Sonia Bansal, Deanna L. Kelly, Carol Espy-Wilson

This paper presents a novel multimodal framework to distinguish between different symptom classes of subjects in the schizophrenia spectrum and healthy controls using audio, video, and text modalities.

Words, Subwords, and Morphemes: What Really Matters in the Surprisal-Reading Time Relationship?

no code implementations26 Oct 2023 Sathvik Nair, Philip Resnik

An important assumption that comes with using LLMs on psycholinguistic data has gone unverified.

A multi-modal approach for identifying schizophrenia using cross-modal attention

no code implementations26 Sep 2023 Gowtham Premananth, Yashish M. Siriwardena, Philip Resnik, Carol Espy-Wilson

This study focuses on how different modalities of human communication can be used to distinguish between healthy controls and subjects with schizophrenia who exhibit strong positive symptoms.

Are Neural Topic Models Broken?

1 code implementation28 Oct 2022 Alexander Hoyle, Pranav Goel, Rupak Sarkar, Philip Resnik

Recently, the relationship between automated and human evaluation of topic models has been called into question.

Topic Models

Syntopical Graphs for Computational Argumentation Tasks

no code implementations ACL 2021 Joe Barrow, Rajiv Jain, Nedim Lipka, Franck Dernoncourt, Vlad Morariu, Varun Manjunatha, Douglas Oard, Philip Resnik, Henning Wachsmuth

Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection.

Stance Detection

Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence

2 code implementations NeurIPS 2021 Alexander Hoyle, Pranav Goel, Denis Peskov, Andrew Hian-Cheong, Jordan Boyd-Graber, Philip Resnik

To address the standardization gap, we systematically evaluate a dominant classical model and two state-of-the-art neural models on two commonly used datasets.

Topic Models

Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence

1 code implementation NeurIPS 2021 Alexander Hoyle, Pranav Goel, Andrew Hian-Cheong, Denis Peskov, Jordan Lee Boyd-Graber, Philip Resnik

To address the standardization gap, we systematically evaluate a dominant classical model and two state-of-the-art neural models on two commonly used datasets.

Topic Models

Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes

no code implementations27 Apr 2021 Han-Chin Shing, Chaitanya Shivade, Nima Pourdamghani, Feng Nan, Philip Resnik, Douglas Oard, Parminder Bhatia

The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information.

Hallucination Informativeness +2

Improving Neural Topic Models using Knowledge Distillation

1 code implementation EMNLP 2020 Alexander Hoyle, Pranav Goel, Philip Resnik

Topic models are often used to identify human-interpretable topics to help make sense of large document collections.

Knowledge Distillation Topic Models

A Prioritization Model for Suicidality Risk Assessment

no code implementations ACL 2020 Han-Chin Shing, Philip Resnik, Douglas Oard

We reframe suicide risk assessment from social media as a ranking problem whose goal is maximizing detection of severely at-risk individuals given the time available.

Retrieval

Assigning Medical Codes at the Encounter Level by Paying Attention to Documents

no code implementations15 Nov 2019 Han-Chin Shing, Guoli Wang, Philip Resnik

The vast majority of research in computer assisted medical coding focuses on coding at the document level, but a substantial proportion of medical coding in the real world involves coding at the level of clinical encounters, each of which is typically represented by a potentially large set of documents.

CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts

no code implementations WS 2019 Ayah Zirikly, Philip Resnik, {\"O}zlem Uzuner, Kristy Hollingshead

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.

Assessing Composition in Sentence Vector Representations

1 code implementation COLING 2018 Allyson Ettinger, Ahmed Elgohary, Colin Phillips, Philip Resnik

We describe the details of the method and generation system, and then present results of experiments applying our method to probe for compositional information in embeddings from a number of existing sentence composition models.

Sentence

CLPsych 2018 Shared Task: Predicting Current and Future Psychological Health from Childhood Essays

no code implementations WS 2018 Veronica Lynn, Alissa Goodman, Kate Niederhoffer, Kate Loveys, Philip Resnik, H. Andrew Schwartz

We describe the shared task for the CLPsych 2018 workshop, which focused on predicting current and future psychological health from an essay authored in childhood.

Parser for Abstract Meaning Representation using Learning to Search

no code implementations26 Oct 2015 Sudha Rao, Yogarshi Vyas, Hal Daume III, Philip Resnik

We develop a novel technique to parse English sentences into Abstract Meaning Representation (AMR) using SEARN, a Learning to Search approach, by modeling the concept and the relation learning in a unified framework.

Learning a Concept Hierarchy from Multi-labeled Documents

no code implementations NeurIPS 2014 Viet-An Nguyen, Jordan L. Ying, Philip Resnik, Jonathan Chang

While topic models can discover patterns of word usage in large corpora, it is difficult to meld this unsupervised structure with noisy, human-provided labels, especially when the label space is large.

Missing Labels Topic Models

Lexical and Hierarchical Topic Regression

no code implementations NeurIPS 2013 Viet-An Nguyen, Jordan L. Ying, Philip Resnik

Inspired by a two-level theory that unifies agenda setting and ideological framing, we propose supervised hierarchical latent Dirichlet allocation (SHLDA) which jointly captures documents' multi-level topic structure and their polar response variables.

regression Sentiment Analysis

Using Information Content to Evaluate Semantic Similarity in a Taxonomy

2 code implementations29 Nov 1995 Philip Resnik

This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content.

Semantic Similarity Semantic Textual Similarity

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