68 citations · 100 across the 20 of their papers we have counts for
6 papers · 1 filter
Quantifying the Task-Specific Information in Text-Based Classifications
Zining Zhu, Aparna Balagopalan, Marzyeh Ghassemi +1
Recently, neural natural language models have attained state-of-the-art performance on a wide variety of tasks, but the high performance can result from superficial, surface-level…
Comparing Acoustic-based Approaches for Alzheimer's Disease Detection
Aparna Balagopalan, Jekaterina Novikova
Robust strategies for Alzheimer's disease (AD) detection are important, given the high prevalence of AD. In this paper, we study the performance and generalizability of three appro…
Augmenting BERT Carefully with Underrepresented Linguistic Features
Aparna Balagopalan, Jekaterina Novikova
Fine-tuned Bidirectional Encoder Representations from Transformers (BERT)-based sequence classification models have proven to be effective for detecting Alzheimer's Disease (AD) fr…
To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection
Aparna Balagopalan, Benjamin Eyre, Frank Rudzicz +1
Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly…
Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power
Jekaterina Novikova, Aparna Balagopalan, Ksenia Shkaruta +1
Understanding the vulnerability of linguistic features extracted from noisy text is important for both developing better health text classification models and for interpreting vuln…
Impact of ASR on Alzheimer's Disease Detection: All Errors are Equal, but Deletions are More Equal than Others
Aparna Balagopalan, Ksenia Shkaruta, Jekaterina Novikova
Automatic Speech Recognition (ASR) is a critical component of any fully-automated speech-based dementia detection model. However, despite years of speech recognition research, litt…