Search Results for author: Ani Nenkova

Found 46 papers, 5 papers with code

Unified Pretraining Framework for Document Understanding

no code implementations22 Apr 2022 Jiuxiang Gu, Jason Kuen, Vlad I. Morariu, Handong Zhao, Nikolaos Barmpalios, Rajiv Jain, Ani Nenkova, Tong Sun

Document intelligence automates the extraction of information from documents and supports many business applications.

Self-Supervised Learning

Temporal Effects on Pre-trained Models for Language Processing Tasks

no code implementations24 Nov 2021 Oshin Agarwal, Ani Nenkova

We present a set of experiments with systems powered by large neural pretrained representations for English to demonstrate that {\em temporal model deterioration} is not as big a concern, with some models in fact improving when tested on data drawn from a later time period.

Domain Adaptation Experimental Design +1

From Toxicity in Online Comments to Incivility in American News: Proceed with Caution

no code implementations EACL 2021 Anushree Hede, Oshin Agarwal, Linda Lu, Diana C. Mutz, Ani Nenkova

The ability to quantify incivility online, in news and in congressional debates, is of great interest to political scientists.

Understanding Clinical Trial Reports: Extracting Medical Entities and Their Relations

no code implementations7 Oct 2020 Benjamin E. Nye, Jay DeYoung, Eric Lehman, Ani Nenkova, Iain J. Marshall, Byron C. Wallace

Here we consider the end-to-end task of both (a) extracting treatments and outcomes from full-text articles describing clinical trials (entity identification) and, (b) inferring the reported results for the former with respect to the latter (relation extraction).

Decision Making Relation Extraction

Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time

1 code implementation ACL 2020 Benjamin E. Nye, Ani Nenkova, Iain J. Marshall, Byron C. Wallace

We apply the system at scale to all reports of randomized controlled trials indexed in MEDLINE, powering the automatic generation of evidence maps, which provide a global view of the efficacy of different interventions combining data from all relevant clinical trials on a topic.

Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve

no code implementations CL (ACL) 2021 Oshin Agarwal, Yinfei Yang, Byron C. Wallace, Ani Nenkova

We examine these questions by contrasting the performance of several variants of LSTM-CRF architectures for named entity recognition, with some provided only representations of the context as features.

Named Entity Recognition

Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models

1 code implementation8 Apr 2020 Oshin Agarwal, Yinfei Yang, Byron C. Wallace, Ani Nenkova

We propose a method for auditing the in-domain robustness of systems, focusing specifically on differences in performance due to the national origin of entities.

Fairness Named Entity Recognition

Emotion Impacts Speech Recognition Performance

no code implementations NAACL 2019 Rushab Munot, Ani Nenkova

It has been established that the performance of speech recognition systems depends on multiple factors including the lexical content, speaker identity and dialect.

Speech Recognition

Evaluation of named entity coreference

no code implementations WS 2019 Oshin Agarwal, Sanjay Subramanian, Ani Nenkova, Dan Roth

It is therefore important that coreference resolution systems are able to link these different types of mentions to the correct entity name.

Coreference Resolution

Browsing Health: Information Extraction to Support New Interfaces for Accessing Medical Evidence

no code implementations WS 2019 Soham Parikh, Elizabeth Conrad, Oshin Agarwal, Iain Marshall, Byron Wallace, Ani Nenkova

Typical information needs, such as retrieving a full list of medical interventions for a given condition, or finding the reported efficacy of a particular treatment with respect to a specific outcome of interest cannot be straightforwardly posed in typical text-box search.

How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature

no code implementations WS 2019 Simeng Sun, Ori Shapira, Ido Dagan, Ani Nenkova

We show that plain ROUGE F1 scores are not ideal for comparing current neural systems which on average produce different lengths.

Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction

no code implementations NAACL 2019 Yinfei Yang, Oshin Agarwal, Chris Tar, Byron C. Wallace, Ani Nenkova

Experiments on a complex biomedical information extraction task using expert and lay annotators show that: (i) simply excluding from the training data instances predicted to be difficult yields a small boost in performance; (ii) using difficulty scores to weight instances during training provides further, consistent gains; (iii) assigning instances predicted to be difficult to domain experts is an effective strategy for task routing.

Named Person Coreference in English News

no code implementations26 Oct 2018 Oshin Agarwal, Sanjay Subramanian, Ani Nenkova, Dan Roth

Here, we evaluate two state of the art coreference resolution systems on the subtask of Named Person Coreference, in which we are interested in identifying a person mentioned by name, along with all other mentions of the person, by pronoun or generic noun phrase.

Coreference Resolution Named Entity Recognition

Syntactic Patterns Improve Information Extraction for Medical Search

no code implementations NAACL 2018 Roma Patel, Yinfei Yang, Iain Marshall, Ani Nenkova, Byron Wallace

Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s) of interest.

Aggregating and Predicting Sequence Labels from Crowd Annotations

1 code implementation ACL 2017 An Thanh Nguyen, Byron Wallace, Junyi Jessy Li, Ani Nenkova, Matthew Lease

Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text.

Named Entity Recognition Part-Of-Speech Tagging

Combining Lexical and Syntactic Features for Detecting Content-dense Texts in News

no code implementations3 Apr 2017 Yinfei Yang, Ani Nenkova

On manually annotated data, we compare the performance of domain-specific classifiers, trained on data only from a given news domain and a general classifier in which data from all four domains is pooled together.

Question Answering

Detecting (Un)Important Content for Single-Document News Summarization

no code implementations EACL 2017 Yinfei Yang, Forrest Sheng Bao, Ani Nenkova

We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs.

Document Summarization News Summarization

Improving the Annotation of Sentence Specificity

no code implementations LREC 2016 Junyi Jessy Li, Bridget O{'}Daniel, Yi Wu, Wenli Zhao, Ani Nenkova

We found that the lack of specificity distributes evenly among immediate prior context, long distance prior context and no prior context.

What Makes Writing Great? First Experiments on Article Quality Prediction in the Science Journalism Domain

no code implementations TACL 2013 Annie Louis, Ani Nenkova

We show that the distinction between great and typical articles can be detected fairly accurately, and that the entire spectrum of our features contribute to the distinction.

Information Retrieval Recommendation Systems

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