Preethi Raghavan


Jetsons at the FinNLP-2022 ERAI Task: BERT-Chinese for mining high MPP posts
Alolika Gon | Sihan Zha | Sai Krishna Rallabandi | Parag Pravin Dakle | Preethi Raghavan
Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)

In this paper, we discuss the various approaches by the Jetsons team for the “Pairwise Comparison” sub-task of the ERAI shared task to compare financial opinions for profitability and loss. Our BERT-Chinese model considers a pair of opinions and predicts the one with a higher maximum potential profit (MPP) with 62.07% accuracy. We analyze the performance of our approaches on both the MPP and maximal loss (ML) problems and deeply dive into why BERT-Chinese outperforms other models.

Using Transformer-based Models for Taxonomy Enrichment and Sentence Classification
Parag Pravin Dakle | Shrikumar Patil | Sai Krishna Rallabandi | Chaitra Hegde | Preethi Raghavan
Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)

In this paper, we present a system that addresses the taxonomy enrichment problem for Environment, Social and Governance issues in the financial domain, as well as classifying sentences as sustainable or unsustainable, for FinSim4-ESG, a shared task for the FinNLP workshop at IJCAI-2022. We first created a derived dataset for taxonomy enrichment by using a sentence-BERT-based paraphrase detector (Reimers and Gurevych, 2019) (on the train set) to create positive and negative term-concept pairs. We then model the problem by fine-tuning the sentence-BERT-based paraphrase detector on this derived dataset, and use it as the encoder, and use a Logistic Regression classifier as the decoder, resulting in test Accuracy: 0.6 and Avg. Rank: 1.97. In case of the sentence classification task, the best-performing classifier (Accuracy: 0.92) consists of a pre-trained RoBERTa model (Liu et al., 2019a) as the encoder and a Feed Forward Neural Network classifier as the decoder.

Towards Generalizable Methods for Automating Risk Score Calculation
Jennifer J Liang | Eric Lehman | Ananya Iyengar | Diwakar Mahajan | Preethi Raghavan | Cindy Y. Chang | Peter Szolovits
Proceedings of the 21st Workshop on Biomedical Language Processing

Clinical risk scores enable clinicians to tabulate a set of patient data into simple scores to stratify patients into risk categories. Although risk scores are widely used to inform decision-making at the point-of-care, collecting the information necessary to calculate such scores requires considerable time and effort. Previous studies have focused on specific risk scores and involved manual curation of relevant terms or codes and heuristics for each data element of a risk score. To support more generalizable methods for risk score calculation, we annotate 100 patients in MIMIC-III with elements of CHA2DS2-VASc and PERC scores, and explore using question answering (QA) and off-the-shelf tools. We show that QA models can achieve comparable or better performance for certain risk score elements as compared to heuristic-based methods, and demonstrate the potential for more scalable risk score automation without the need for expert-curated heuristics. Our annotated dataset will be released to the community to encourage efforts in generalizable methods for automating risk scores.

Learning to Ask Like a Physician
Eric Lehman | Vladislav Lialin | Katelyn Edelwina Legaspi | Anne Janelle Sy | Patricia Therese Pile | Nicole Rose Alberto | Richard Raymund Ragasa | Corinna Victoria Puyat | Marianne Katharina Taliño | Isabelle Rose Alberto | Pia Gabrielle Alfonso | Dana Moukheiber | Byron Wallace | Anna Rumshisky | Jennifer Liang | Preethi Raghavan | Leo Anthony Celi | Peter Szolovits
Proceedings of the 4th Clinical Natural Language Processing Workshop

Existing question answering (QA) datasets derived from electronic health records (EHR) are artificially generated and consequently fail to capture realistic physician information needs. We present Discharge Summary Clinical Questions (DiSCQ), a newly curated question dataset composed of 2,000+ questions paired with the snippets of text (triggers) that prompted each question. The questions are generated by medical experts from 100+ MIMIC-III discharge summaries. We analyze this dataset to characterize the types of information sought by medical experts. We also train baseline models for trigger detection and question generation (QG), paired with unsupervised answer retrieval over EHRs. Our baseline model is able to generate high quality questions in over 62% of cases when prompted with human selected triggers. We release this dataset (and all code to reproduce baseline model results) to facilitate further research into realistic clinical QA and QG:


emrKBQA: A Clinical Knowledge-Base Question Answering Dataset
Preethi Raghavan | Jennifer J Liang | Diwakar Mahajan | Rachita Chandra | Peter Szolovits
Proceedings of the 20th Workshop on Biomedical Language Processing

We present emrKBQA, a dataset for answering physician questions from a structured patient record. It consists of questions, logical forms and answers. The questions and logical forms are generated based on real-world physician questions and are slot-filled and answered from patients in the MIMIC-III KB through a semi-automated process. This community-shared release consists of over 940000 question, logical form and answer triplets with 389 types of questions and ~7.5 paraphrases per question type. We perform experiments to validate the quality of the dataset and set benchmarks for question to logical form learning that helps answer questions on this dataset.


Advancing Seq2seq with Joint Paraphrase Learning
So Yeon Min | Preethi Raghavan | Peter Szolovits
Proceedings of the 3rd Clinical Natural Language Processing Workshop

We address the problem of model generalization for sequence to sequence (seq2seq) architectures. We propose going beyond data augmentation via paraphrase-optimized multi-task learning and observe that it is useful in correctly handling unseen sentential paraphrases as inputs. Our models greatly outperform SOTA seq2seq models for semantic parsing on diverse domains (Overnight - up to 3.2% and emrQA - 7%) and Nematus, the winning solution for WMT 2017, for Czech to English translation (CzENG 1.6 - 1.5 BLEU).

Entity-Enriched Neural Models for Clinical Question Answering
Bhanu Pratap Singh Rawat | Wei-Hung Weng | So Yeon Min | Preethi Raghavan | Peter Szolovits
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing

We explore state-of-the-art neural models for question answering on electronic medical records and improve their ability to generalize better on previously unseen (paraphrased) questions at test time. We enable this by learning to predict logical forms as an auxiliary task along with the main task of answer span detection. The predicted logical forms also serve as a rationale for the answer. Further, we also incorporate medical entity information in these models via the ERNIE architecture. We train our models on the large-scale emrQA dataset and observe that our multi-task entity-enriched models generalize to paraphrased questions ~5% better than the baseline BERT model.


emrQA: A Large Corpus for Question Answering on Electronic Medical Records
Anusri Pampari | Preethi Raghavan | Jennifer Liang | Jian Peng
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We propose a novel methodology to generate domain-specific large-scale question answering (QA) datasets by re-purposing existing annotations for other NLP tasks. We demonstrate an instance of this methodology in generating a large-scale QA dataset for electronic medical records by leveraging existing expert annotations on clinical notes for various NLP tasks from the community shared i2b2 datasets. The resulting corpus (emrQA) has 1 million questions-logical form and 400,000+ question-answer evidence pairs. We characterize the dataset and explore its learning potential by training baseline models for question to logical form and question to answer mapping.


Addressing Limited Data for Textual Entailment Across Domains
Chaitanya Shivade | Preethi Raghavan | Siddharth Patwardhan
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


Cross-narrative Temporal Ordering of Medical Events
Preethi Raghavan | Eric Fosler-Lussier | Noémie Elhadad | Albert M. Lai
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


Learning to Temporally Order Medical Events in Clinical Text
Preethi Raghavan | Albert Lai | Eric Fosler-Lussier
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Exploring Semi-Supervised Coreference Resolution of Medical Concepts using Semantic and Temporal Features
Preethi Raghavan | Eric Fosler-Lussier | Albert Lai
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Temporal Classification of Medical Events
Preethi Raghavan | Eric Fosler-Lussier | Albert Lai
BioNLP: Proceedings of the 2012 Workshop on Biomedical Natural Language Processing