Search Results for author:
Found 104 papers, 39 papers with code
Explaining Models of Mental Health via Clinically Grounded Auxiliary Tasks
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.
Civil Unrest on Twitter (CUT): A Dataset of Tweets to Support Research on Civil Unrest
We present baseline systems trained on this data for the identification of tweets related to civil unrest.
Towards Understanding the Role of Gender in Deploying Social Media-Based Mental Health Surveillance Models
Spurred by advances in machine learning and natural language processing, developing social media-based mental health surveillance models has received substantial recent attention.
Study of Manifestation of Civil Unrest on Twitter
Twitter is commonly used for civil unrest detection and forecasting tasks, but there is a lack of work in evaluating how civil unrest manifests on Twitter across countries and events.
Changes in Tweet Geolocation over Time: A Study with Carmen 2.0
Despite the importance of these tools for data curation, the impact of tweet language, country of origin, and creation date on tool performance remains largely unknown.
Updated Headline Generation: Creating Updated Summaries for Evolving News Stories
We propose the task of updated headline generation, in which a system generates a headline for an updated article, considering both the previous article and headline.
Model Distillation for Faithful Explanations of Medical Code Predictions
We propose to use knowledge distillation, or training a student model that mimics the behavior of a trained teacher model, as a technique to generate faithful and plausible explanations.
Qualitative Analysis of Depression Models by Demographics
Models for identifying depression using social media text exhibit biases towards different gender and racial/ethnic groups.
A Closer Look at Claim Decomposition
We investigate how various methods of claim decomposition -- especially LLM-based methods -- affect the result of an evaluation approach such as the recently proposed FActScore, finding that it is sensitive to the decomposition method used.
Evaluating Biases in Context-Dependent Health Questions
We study how large language model biases are exhibited through these contextual questions in the healthcare domain.
Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions
To address these challenges, we construct two new datasets: JAMA Clinical Challenge and Medbullets.
An Eye on Clinical BERT: Investigating Language Model Generalization for Diabetic Eye Disease Phenotyping
Diabetic eye disease is a major cause of blindness worldwide.
Selecting Shots for Demographic Fairness in Few-Shot Learning with Large Language Models
In this work, we explore the effect of shots, which directly affect the performance of models, on the fairness of LLMs as NLP classification systems.
MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies
Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P -- that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE).
Transferring Fairness using Multi-Task Learning with Limited Demographic Information
Training supervised machine learning systems with a fairness loss can improve prediction fairness across different demographic groups.
BloombergGPT: A Large Language Model for Finance
The use of NLP in the realm of financial technology is broad and complex, with applications ranging from sentiment analysis and named entity recognition to question answering.
Do Text-to-Text Multi-Task Learners Suffer from Task Conflict?
Learning these models often requires specialized training algorithms that address task-conflict in the shared parameter updates, which otherwise can lead to negative transfer.
Using Open-Ended Stressor Responses to Predict Depressive Symptoms across Demographics
Stressors are related to depression, but this relationship is complex.
Zero-shot Cross-lingual Transfer is Under-specified Optimization
Pretrained multilingual encoders enable zero-shot cross-lingual transfer, but often produce unreliable models that exhibit high performance variance on the target language.
The Problem of Semantic Shift in Longitudinal Monitoring of Social Media: A Case Study on Mental Health During the COVID-19 Pandemic
Social media allows researchers to track societal and cultural changes over time based on language analysis tools.
Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses
Self-disclosed mental health diagnoses, which serve as ground truth annotations of mental health status in the absence of clinical measures, underpin the conclusions behind most computational studies of mental health language from the last decade.
What Makes Data-to-Text Generation Hard for Pretrained Language Models?
Previous work shows that pre-trained language models(PLMs) perform remarkably well on this task after fine-tuning on a significant amount of task-specific training data.
Enriching Unsupervised User Embedding via Medical Concepts
Clinical notes in Electronic Health Records (EHR) present rich documented information of patients to inference phenotype for disease diagnosis and study patient characteristics for cohort selection.
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English.
Learning to Look Inside: Augmenting Token-Based Encoders with Character-Level Information
Commonly-used transformer language models depend on a tokenization schema which sets an unchangeable subword vocabulary prior to pre-training, destined to be applied to all downstream tasks regardless of domain shift, novel word formations, or other sources of vocabulary mismatch.
Faithful and Plausible Explanations of Medical Code Predictions
Machine learning models that offer excellent predictive performance often lack the interpretability necessary to support integrated human machine decision-making.
Improving Zero-Shot Multi-Lingual Entity Linking
Entity linking -- the task of identifying references in free text to relevant knowledge base representations -- often focuses on single languages.
Fine-tuning Encoders for Improved Monolingual and Zero-shot Polylingual Neural Topic Modeling
Neural topic models can augment or replace bag-of-words inputs with the learned representations of deep pre-trained transformer-based word prediction models.
Gender and Racial Fairness in Depression Research using Social Media
While previous research has raised concerns about possible biases in models produced from this data, no study has quantified how these biases actually manifest themselves with respect to different demographic groups, such as gender and racial/ethnic groups.
User Factor Adaptation for User Embedding via Multitask Learning
In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets.
Generating Synthetic Text Data to Evaluate Causal Inference Methods
High-dimensional and unstructured data such as natural language complicates the evaluation of causal inference methods; such evaluations rely on synthetic datasets with known causal effects.
On the State of Social Media Data for Mental Health Research
Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade.
Do Models of Mental Health Based on Social Media Data Generalize?
Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples.
Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking
We find that the multilingual ability of BERT leads to robust performance in monolingual and multilingual settings.
Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement
The #MeToo movement on Twitter has drawn attention to the pervasive nature of sexual harassment and violence.
Do Explicit Alignments Robustly Improve Multilingual Encoders?
Multilingual BERT (mBERT), XLM-RoBERTa (XLMR) and other unsupervised multilingual encoders can effectively learn cross-lingual representation.
Clinical Concept Linking with Contextualized Neural Representations
We propose an approach to concept linking that leverages recent work in contextualized neural models, such as ELMo (Peters et al. 2018), which create a token representation that integrates the surrounding context of the mention and concept name.
Are All Languages Created Equal in Multilingual BERT?
Multilingual BERT (mBERT) trained on 104 languages has shown surprisingly good cross-lingual performance on several NLP tasks, even without explicit cross-lingual signals.
Sources of Transfer in Multilingual Named Entity Recognition
However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data.
Multilingual Named Entity Recognition
named-entity-recognition
+2
Using Noisy Self-Reports to Predict Twitter User Demographics
We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions.
Phenotyping of Clinical Notes with Improved Document Classification Models Using Contextualized Neural Language Models
Clinical notes contain an extensive record of a patient's health status, such as smoking status or the presence of heart conditions.
Ranked #1 on
Clinical Note Phenotyping
on I2B2 2006: Smoking
Mental Health Surveillance over Social Media with Digital Cohorts
The ability to track mental health conditions via social media opened the doors for large-scale, automated, mental health surveillance.
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks.
Ranked #8 on
Cross-Lingual NER
on CoNLL Spanish
Discriminative Candidate Generation for Medical Concept Linking
Linking mentions of medical concepts in a clinical note to a concept in an ontology enables a variety of tasks that rely on understanding the content of a medical record, such as identifying patient populations and decision support.
Using Author Embeddings to Improve Tweet Stance Classification
Many social media classification tasks analyze the content of a message, but do not consider the context of the message.
Convolutions Are All You Need (For Classifying Character Sequences)
While recurrent neural networks (RNNs) are widely used for text classification, they demonstrate poor performance and slow convergence when trained on long sequences.
Challenges of Using Text Classifiers for Causal Inference
Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets.
Johns Hopkins or johnny-hopkins: Classifying Individuals versus Organizations on Twitter
Previous work (McCorriston et al., 2015) presented a method for determining if an account was an individual or organization based on account profile and a collection of tweets.
Predicting Twitter User Demographics from Names Alone
Social media analysis frequently requires tools that can automatically infer demographics to contextualize trends.
Deep Dirichlet Multinomial Regression
We present deep Dirichlet Multinomial Regression (dDMR), a generative topic model that simultaneously learns document feature representations and topics.
CADET: Computer Assisted Discovery Extraction and Translation
It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users.
Constructing an Alias List for Named Entities during an Event
EntitySpike uses a temporal heuristic to identify named entities with similar context that occur in the same time period (within minutes) during an event.
How Does Twitter User Behavior Vary Across Demographic Groups?
Demographically-tagged social media messages are a common source of data for computational social science.
Pocket Knowledge Base Population
Existing Knowledge Base Population methods extract relations from a closed relational schema with limited coverage leading to sparse KBs.
Bayesian Modeling of Lexical Resources for Low-Resource Settings
Practically, this means that we may treat the lexical resources as observations under the proposed generative model.
Low Resource Named Entity Recognition
named-entity-recognition
+2
Ethical Research Protocols for Social Media Health Research
Social media have transformed data-driven research in political science, the social sciences, health, and medicine.
Feature Generation for Robust Semantic Role Labeling
Hand-engineered feature sets are a well understood method for creating robust NLP models, but they require a lot of expertise and effort to create.
Harmonic Grammar, Optimality Theory, and Syntax Learnability: An Empirical Exploration of Czech Word Order
This work presents a systematic theoretical and empirical comparison of the major algorithms that have been proposed for learning Harmonic and Optimality Theory grammars (HG and OT, respectively).
Name Variation in Community Question Answering Systems
In this paper, we propose an algorithm to reduce the number of unanswered questions in Yahoo!
Multi-task Domain Adaptation for Sequence Tagging
Many domain adaptation approaches rely on learning cross domain shared representations to transfer the knowledge learned in one domain to other domains.
Twitter as a Source of Global Mobility Patterns for Social Good
Data on human spatial distribution and movement is essential for understanding and analyzing social systems.
Embedding Lexical Features via Low-Rank Tensors
Modern NLP models rely heavily on engineered features, which often combine word and contextual information into complex lexical features.
Improving Named Entity Recognition for Chinese Social Media with Word Segmentation Representation Learning
Named entity recognition, and other information extraction tasks, frequently use linguistic features such as part of speech tags or chunkings.
Approximation-Aware Dependency Parsing by Belief Propagation
We show how to adjust the model parameters to compensate for the errors introduced by this approximation, by following the gradient of the actual loss on training data.
Interactive Knowledge Base Population
Most work on building knowledge bases has focused on collecting entities and facts from as large a collection of documents as possible.
Improved Relation Extraction with Feature-Rich Compositional Embedding Models
We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement.
Ranked #1 on
Relation Extraction
on ACE 2005
(Cross Sentence metric)
Sprite: Generalizing Topic Models with Structured Priors
We introduce Sprite, a family of topic models that incorporates structure into model priors as a function of underlying components.
Learning Composition Models for Phrase Embeddings
We propose efficient unsupervised and task-specific learning objectives that scale our model to large datasets.
Factorial LDA: Sparse Multi-Dimensional Text Models
Multi-dimensional latent variable models can capture the many latent factors in a text corpus, such as topic, author perspective and sentiment.
Adaptive Regularization of Weight Vectors
We present AROW, a new online learning algorithm that combines several properties of successful : large margin training, confidence weighting, and the capacity to handle non-separable data.
Exact Convex Confidence-Weighted Learning
Confidence-weighted (CW) learning [6], an online learning method for linear classifiers, maintains a Gaussian distributions over weight vectors, with a covariance matrix that represents uncertainty about weights and correlations.
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
Automatic sentiment classification has been extensively studied and applied in recent years.
