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Found 52 papers, 7 papers with code
Developing a Curated Topic Model for COVID-19 Medical Research Literature
Topic models can facilitate search, navigation, and knowledge discovery in large document collections.
Community-level Research on Suicidality Prediction in a Secure Environment: Overview of the CLPsych 2021 Shared Task
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
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 .
Making the Implicit Explicit: Implicit Content as a First Class Citizen in NLP
In this work, we represent language with language, and direct an LLM to decompose utterances into logical and plausible inferences.
Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics
Stressors are related to depression, but this relationship is complex.
Are Neural Topic Models Broken?
Recently, the relationship between automated and human evaluation of topic models has been called into question.
Syntopical Graphs for Computational Argumentation Tasks
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.
Is Automated Topic Model Evaluation Broken?: The Incoherence of Coherence
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.
Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence
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.
Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes
The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information.
Improving Neural Topic Models using Knowledge Distillation
Topic models are often used to identify human-interpretable topics to help make sense of large document collections.
A Prioritization Model for Suicidality Risk Assessment
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.
A Joint Model for Document Segmentation and Segment Labeling
Text segmentation aims to uncover latent structure by dividing text from a document into coherent sections.
Assigning Medical Codes at the Encounter Level by Paying Attention to Documents
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.
A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability
Multilingual topic models (MTMs) learn topics on documents in multiple languages.
CLPsych 2019 Shared Task: Predicting the Degree of Suicide Risk in Reddit Posts
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
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.
Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings
We report on the creation of a dataset for studying assessment of suicide risk via online postings in Reddit.
CLPsych 2018 Shared Task: Predicting Current and Future Psychological Health from Childhood Essays
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.
Adapting Topic Models using Lexical Associations with Tree Priors
Models work best when they are optimized taking into account the evaluation criteria that people care about.
Parser for Abstract Meaning Representation using Learning to Search
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
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.
Lexical and Hierarchical Topic Regression
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.
Using Information Content to Evaluate Semantic Similarity in a Taxonomy
This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content.
