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Found 22 papers, 4 papers with code
Mitigating Data Shift of Biomedical Research Articles for Information Retrieval and Semantic Indexing
Our results show that the model effectively leverages task similarity to improve the robustness to dataset shift.
Team UTSA-NLP at SemEval 2024 Task 5: Prompt Ensembling for Argument Reasoning in Civil Procedures with GPT4
Overall, our system results in a Macro F1 of . 8095 on the validation dataset and . 7315 (5th out of 21 teams) on the final test set.
Extracting Biomedical Entities from Noisy Audio Transcripts
Our dataset offers a comprehensive collection of almost 2, 000 clean and noisy recordings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text
We first study the effectiveness of state-of-the-art approaches and find that they are severely limited against text produced by diverse generators and domains in the real world.
BabyStories: Can Reinforcement Learning Teach Baby Language Models to Write Better Stories?
These insights highlight the potential benefits of RLHF fine-tuning for language models within limited data, enhancing their ability to maintain narrative focus and coherence while adhering better to initial instructions in storytelling tasks.
Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasets
To show this, we create new splits of the existing medical text-to-SQL dataset MIMICSQL that better measure the generalizability of the resulting models.
Bike Frames: Understanding the Implicit Portrayal of Cyclists in the News
In this paper, we explore the perceived perception of cyclists within news headlines.
Linguistic Elements of Engaging Customer Service Discourse on Social Media
Thus, through this study, we aim to bridge this critical gap in the existing literature by analyzing language's content and stylistic aspects such as expressed empathy, psycho-linguistic features, dialogue tags, and metrics for quantifying personalization of the utterances that can influence the engagement of an interaction.
A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition Models
Our findings emphasize the biases in chemical NER models, urging practitioners to account for these biases in downstream applications.
A Marker-based Neural Network System for Extracting Social Determinants of Health
The major finding of this study is that the multi-stage pipeline effectively extracts SDoH information from clinical notes.
Measuring Geographic Performance Disparities of Offensive Language Classifiers
Likewise, we show that while offensive language models produce false positives on African American English, model performance is not correlated with each city's minority population proportions.
UTSA NLP at SemEval-2022 Task 4: An Exploration of Simple Ensembles of Transformers, Convolutional, and Recurrent Neural Networks
The act of appearing kind or helpful via the use of but having a feeling of superiority condescending and patronizing language can have have serious mental health implications to those that experience it.
Turning Stocks into Memes: A Dataset for Understanding How Social Communities Can Drive Wall Street
Likewise, we curate a dataset to better understand how intent interacts with a user's general support towards the coordinated actions of the community for GameStop.
Case Study on Detecting COVID-19 Health-Related Misinformation in Social Media
This paper presents a mechanism to detect COVID-19 health-related misinformation in social media following an interdisciplinary approach.
Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions
Language generation models' democratization benefits many domains, from answering health-related questions to enhancing education by providing AI-driven tutoring services.
An Empirical Study of the Downstream Reliability of Pre-Trained Word Embeddings
While pre-trained word embeddings have been shown to improve the performance of downstream tasks, many questions remain regarding their reliability: Do the same pre-trained word embeddings result in the best performance with slight changes to the training data?
Quantifying 60 Years of Gender Bias in Biomedical Research with Word Embeddings
Specifically, we address multiple questions, including, How has gender bias changed over time in biomedical research, and what health-related concepts are the most biased?
How Many Users Are Enough? Exploring Semi-Supervision and Stylometric Features to Uncover a Russian Troll Farm
In this paper, we study the impact the number of labeled troll accounts has on detection performance.
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces
Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III.
Predicting Psychological Health from Childhood Essays with Convolutional Neural Networks for the CLPsych 2018 Shared Task (Team UKNLP)
The second task (task B) asks participants to predict future psychological distress at ages 23, 33, 42, and 50 using the age 11 essays.
EMR Coding with Semi-Parametric Multi-Head Matching Networks
Coding EMRs with diagnosis and procedure codes is an indispensable task for billing, secondary data analyses, and monitoring health trends.
Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models
Text mining the relations between chemicals and proteins is an increasingly important task.
