Search Results for author: Aakanksha Naik

Found 25 papers, 13 papers with code

FanfictionNLP: A Text Processing Pipeline for Fanfiction

1 code implementation NAACL (NUSE) 2021 Michael Yoder, Sopan Khosla, Qinlan Shen, Aakanksha Naik, Huiming Jin, Hariharan Muralidharan, Carolyn Rosé

The pipeline includes modules for character identification and coreference, as well as the attribution of quotes and narration to those characters.

SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature

no code implementations10 Jun 2024 David Wadden, Kejian Shi, Jacob Morrison, Aakanksha Naik, Shruti Singh, Nitzan Barzilay, Kyle Lo, Tom Hope, Luca Soldaini, Shannon Zejiang Shen, Doug Downey, Hannaneh Hajishirzi, Arman Cohan

We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following demonstrations for 54 tasks covering five essential scientific literature understanding capabilities: information extraction, summarization, question answering, claim verification, and classification.

Claim Verification Instruction Following +3

On-the-fly Definition Augmentation of LLMs for Biomedical NER

1 code implementation29 Mar 2024 Monica Munnangi, Sergey Feldman, Byron C Wallace, Silvio Amir, Tom Hope, Aakanksha Naik

In this work we set out to improve LLM performance on biomedical NER in limited data settings via a new knowledge augmentation approach which incorporates definitions of relevant concepts on-the-fly.


NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMs

1 code implementation19 Dec 2023 Maria Antoniak, Aakanksha Naik, Carla S. Alvarado, Lucy Lu Wang, Irene Y. Chen

Ethical frameworks for the use of natural language processing (NLP) are urgently needed to shape how large language models (LLMs) and similar tools are used for healthcare applications.


CARE: Extracting Experimental Findings From Clinical Literature

no code implementations16 Nov 2023 Aakanksha Naik, Bailey Kuehl, Erin Bransom, Doug Downey, Tom Hope

Focusing on biomedicine, this work presents CARE -- a new IE dataset for the task of extracting clinical findings.

Relation Extraction

LongBoX: Evaluating Transformers on Long-Sequence Clinical Tasks

1 code implementation16 Nov 2023 Mihir Parmar, Aakanksha Naik, Himanshu Gupta, Disha Agrawal, Chitta Baral

Assessing these models on long sequences is crucial since prior work in the general domain has demonstrated performance degradation of LLMs on longer texts.


SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Design

no code implementations19 Jun 2023 Carl Edwards, Aakanksha Naik, Tushar Khot, Martin Burke, Heng Ji, Tom Hope

We are given a small "personalized dataset" of 10-20 drug synergy relationships in the context of specific cancer cell targets.

In-Context Learning Language Modelling

S2abEL: A Dataset for Entity Linking from Scientific Tables

1 code implementation30 Apr 2023 Yuze Lou, Bailey Kuehl, Erin Bransom, Sergey Feldman, Aakanksha Naik, Doug Downey

Entity linking (EL) is the task of linking a textual mention to its corresponding entry in a knowledge base, and is critical for many knowledge-intensive NLP applications.

Entity Linking Question Answering

Relatedly: Scaffolding Literature Reviews with Existing Related Work Sections

no code implementations13 Feb 2023 Srishti Palani, Aakanksha Naik, Doug Downey, Amy X. Zhang, Jonathan Bragg, Joseph Chee Chang

Scholars who want to research a scientific topic must take time to read, extract meaning, and identify connections across many papers.

Descriptive Navigate +1

Literature-Augmented Clinical Outcome Prediction

1 code implementation Findings (NAACL) 2022 Aakanksha Naik, Sravanthi Parasa, Sergey Feldman, Lucy Lu Wang, Tom Hope

We present BEEP (Biomedical Evidence-Enhanced Predictions), a novel approach for clinical outcome prediction that retrieves patient-specific medical literature and incorporates it into predictive models.

Decision Making

Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks

1 code implementation2 Nov 2021 Aakanksha Naik, Jill Lehman, Carolyn Rose

We reflect on the question: have transfer learning methods sufficiently addressed the poor performance of benchmark-trained models on the long tail?

Natural Language Understanding Transfer Learning

STAGE: Tool for Automated Extraction of Semantic Time Cues to Enrich Neural Temporal Ordering Models

no code implementations15 May 2021 Luke Breitfeller, Aakanksha Naik, Carolyn Rose

We demonstrate the utility of extracted cues by integrating them with an event ordering model using a joint BiLSTM and ILP constraint architecture.

Adapting Event Extractors to Medical Data: Bridging the Covariate Shift

no code implementations EACL 2021 Aakanksha Naik, Jill Lehman, Carolyn Rose

Our best-performing models reach F1 scores of 70. 0 and 72. 9 on notes and conversations respectively, using no labeled data from target domains.

Domain Adaptation Event Extraction +1

Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation

1 code implementation ACL 2020 Aakanksha Naik, Carolyn Rosé

We tackle the task of building supervised event trigger identification models which can generalize better across domains.

Domain Adaptation

TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events

no code implementations WS 2019 Aakanksha Naik, Luke Breitfeller, Carolyn Rose

Prior work on temporal relation classification has focused extensively on event pairs in the same or adjacent sentences (local), paying scant attention to discourse-level (global) pairs.

Relation Classification Sentence +1

Using Functional Schemas to Understand Social Media Narratives

1 code implementation WS 2019 Xinru Yan, Aakanksha Naik, Yohan Jo, Carolyn Rose

We propose a novel take on understanding narratives in social media, focusing on learning {''}functional story schemas{''}, which consist of sets of stereotypical functional structures.

General Classification text-classification +1

Exploring Numeracy in Word Embeddings

no code implementations ACL 2019 Aakanksha Naik, Ravich, Abhilasha er, Carolyn Rose, Eduard Hovy

In this work, we show that existing embedding models are inadequate at constructing representations that capture salient aspects of mathematical meaning for numbers, which is important for language understanding.

Word Embeddings

Stress Test Evaluation for Natural Language Inference

1 code implementation COLING 2018 Aakanksha Naik, Abhilasha Ravichander, Norman Sadeh, Carolyn Rose, Graham Neubig

Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner.

Natural Language Inference Natural Language Understanding +1

Extracting Personal Medical Events for User Timeline Construction using Minimal Supervision

no code implementations WS 2017 Aakanksha Naik, Chris Bogart, Carolyn Rose

In this paper, we describe a system for automatic construction of user disease progression timelines from their posts in online support groups using minimal supervision.

Event Detection

Tackling Biomedical Text Summarization: OAQA at BioASQ 5B

no code implementations WS 2017 Khyathi u, Aakanksha Naik, Ch, Aditya rasekar, Zi Yang, Niloy Gupta, Eric Nyberg

In this paper, we describe our participation in phase B of task 5b of the fifth edition of the annual BioASQ challenge, which includes answering factoid, list, yes-no and summary questions from biomedical data.

Answer Generation Clustering +4

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