3 citations · 9 across the 5 of their papers we have counts for
9 papers
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…
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…
Fantastic Features and Where to Find Them: Detecting Cognitive Impairment with a Subsequence Classification Guided Approach
Benjamin Eyre, Aparna Balagopalan, Jekaterina Novikova
Despite the widely reported success of embedding-based machine learning methods on natural language processing tasks, the use of more easily interpreted engineered features remains…
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…
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation
Aparna Balagopalan, Jekaterina Novikova, Matthew B. A. McDermott +3
Multi-language speech datasets are scarce and often have small sample sizes in the medical domain. Robust transfer of linguistic features across languages could improve rates of ea…
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…