Search Results for author: Franck Dernoncourt

Found 98 papers, 43 papers with code

IGA: An Intent-Guided Authoring Assistant

no code implementations EMNLP 2021 Simeng Sun, Wenlong Zhao, Varun Manjunatha, Rajiv Jain, Vlad Morariu, Franck Dernoncourt, Balaji Vasan Srinivasan, Mohit Iyyer

While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants have yet to be explored.

Language Modelling Pretrained Language Models

Learning Prototype Representations Across Few-Shot Tasks for Event Detection

1 code implementation EMNLP 2021 Viet Lai, Franck Dernoncourt, Thien Huu Nguyen

We address the sampling bias and outlier issues in few-shot learning for event detection, a subtask of information extraction.

Event Detection Few-Shot Learning

Document-Level Event Argument Extraction via Optimal Transport

no code implementations Findings (ACL) 2022 Amir Pouran Ben Veyseh, Minh Van Nguyen, Franck Dernoncourt, Bonan Min, Thien Nguyen

Event Argument Extraction (EAE) is one of the sub-tasks of event extraction, aiming to recognize the role of each entity mention toward a specific event trigger.

Event Argument Extraction Event Extraction

Joint Summarization-Entailment Optimization for Consumer Health Question Understanding

1 code implementation NAACL (NLPMC) 2021 Khalil Mrini, Franck Dernoncourt, Walter Chang, Emilia Farcas, Ndapa Nakashole

Understanding the intent of medical questions asked by patients, or Consumer Health Questions, is an essential skill for medical Conversational AI systems.

Data Augmentation

Improving Visual Grounding by Encouraging Consistent Gradient-based Explanations

no code implementations30 Jun 2022 Ziyan Yang, Kushal Kafle, Franck Dernoncourt, Vicente Ordóñez Román

We propose a margin-based loss for vision-language model pretraining that encourages gradient-based explanations that are consistent with region-level annotations.

Fine-grained Image Captioning with CLIP Reward

1 code implementation26 May 2022 Jaemin Cho, Seunghyun Yoon, Ajinkya Kale, Franck Dernoncourt, Trung Bui, Mohit Bansal

Toward more descriptive and distinctive caption generation, we propose using CLIP, a multimodal encoder trained on huge image-text pairs from web, to calculate multimodal similarity and use it as a reward function.

Image Captioning Image Retrieval +2

Symlink: A New Dataset for Scientific Symbol-Description Linking

no code implementations26 Apr 2022 Viet Dac Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen

Mathematical symbols and descriptions appear in various forms across document section boundaries without explicit markup.

Survey of Aspect-based Sentiment Analysis Datasets

no code implementations11 Apr 2022 Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka, Thamar Solorio

Aspect-based sentiment analysis (ABSA) is a natural language processing problem that requires analyzing user-generated reviews in order to determine: a) The target entity being reviewed, b) The high-level aspect to which it belongs, and c) The sentiment expressed toward the targets and the aspects.

Aspect-Based Sentiment Analysis Natural Language Processing

MHMS: Multimodal Hierarchical Multimedia Summarization

no code implementations7 Apr 2022 JieLin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin

Multimedia summarization with multimodal output can play an essential role in real-world applications, i. e., automatically generating cover images and titles for news articles or providing introductions to online videos.

Enriching Unsupervised User Embedding via Medical Concepts

1 code implementation20 Mar 2022 Xiaolei Huang, Franck Dernoncourt, Mark Dredze

Clinical notes in Electronic Health Records (EHR) present rich documented information of patients to inference phenotype for disease diagnosis and study patient characteristics for cohort selection.

Mortality Prediction

CAISE: Conversational Agent for Image Search and Editing

1 code implementation24 Feb 2022 Hyounghun Kim, Doo Soon Kim, Seunghyun Yoon, Franck Dernoncourt, Trung Bui, Mohit Bansal

To our knowledge, this is the first dataset that provides conversational image search and editing annotations, where the agent holds a grounded conversation with users and helps them to search and edit images according to their requests.

Image Retrieval

SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions

no code implementations19 Feb 2022 Viet Dac Lai, Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen

Given the increasing number of livestreaming videos, automatic speech recognition and post-processing for livestreaming video transcripts are crucial for efficient data management as well as knowledge mining.

Automatic Speech Recognition Natural Language Processing +1

MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction

no code implementations19 Feb 2022 Amir Pouran Ben Veyseh, Nicole Meister, Seunghyun Yoon, Rajiv Jain, Franck Dernoncourt, Thien Huu Nguyen

Acronym extraction is the task of identifying acronyms and their expanded forms in texts that is necessary for various NLP applications.

Exploring Conditional Text Generation for Aspect-Based Sentiment Analysis

no code implementations5 Oct 2021 Siva Uday Sampreeth Chebolu, Franck Dernoncourt, Nedim Lipka, Thamar Solorio

Aspect-based sentiment analysis (ABSA) is an NLP task that entails processing user-generated reviews to determine (i) the target being evaluated, (ii) the aspect category to which it belongs, and (iii) the sentiment expressed towards the target and aspect pair.

Aspect-Based Sentiment Analysis Conditional Text Generation

TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts

1 code implementation EMNLP (newsum) 2021 Sajad Sotudeh, Hanieh Deilamsalehy, Franck Dernoncourt, Nazli Goharian

Recent models in developing summarization systems consist of millions of parameters and the model performance is highly dependent on the abundance of training data.

Extreme Summarization

User-Entity Differential Privacy in Learning Natural Language Models

no code implementations29 Sep 2021 Phung Lai, Tong Sun, Rajiv Jain, Franck Dernoncourt, Jiuxiang Gu, Nikolaos Barmpalios, Han Hu, Hai Phan

In this paper, we introduce a novel concept of user-entity differential privacy (UeDP) to provide formal privacy protection simultaneously to both sensitive entities in textual data and data owners in learning natural language models.

Bit-aware Randomized Response for Local Differential Privacy in Federated Learning

no code implementations29 Sep 2021 Phung Lai, Hai Phan, Li Xiong, Khang Phuc Tran, My Thai, Tong Sun, Franck Dernoncourt, Jiuxiang Gu, Nikolaos Barmpalios, Rajiv Jain

In this paper, we develop BitRand, a bit-aware randomized response algorithm, to preserve local differential privacy (LDP) in federated learning (FL).

Federated Learning Image Classification

StreamHover: Livestream Transcript Summarization and Annotation

1 code implementation EMNLP 2021 Sangwoo Cho, Franck Dernoncourt, Tim Ganter, Trung Bui, Nedim Lipka, Walter Chang, Hailin Jin, Jonathan Brandt, Hassan Foroosh, Fei Liu

With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge.

Extractive Summarization

TIMERS: Document-level Temporal Relation Extraction

no code implementations ACL 2021 Puneet Mathur, Rajiv Jain, Franck Dernoncourt, Vlad Morariu, Quan Hung Tran, Dinesh Manocha

We present TIMERS - a TIME, Rhetorical and Syntactic-aware model for document-level temporal relation classification in the English language.

Relation Classification

DPR at SemEval-2021 Task 8: Dynamic Path Reasoning for Measurement Relation Extraction

no code implementations SEMEVAL 2021 Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen

To this end, in this paper, we propose a novel model for the task of measurement relation extraction (MRE) whose goal is to recognize the relation between measured entities, quantities, and conditions mentioned in a document.

Relation Extraction Translation

Unleash GPT-2 Power for Event Detection

no code implementations ACL 2021 Amir Pouran Ben Veyseh, Viet Lai, Franck Dernoncourt, Thien Huu Nguyen

To prevent the noises inevitable in automatically generated data from hampering training process, we propose to exploit a teacher-student architecture in which the teacher is supposed to learn anchor knowledge from the original data.

Event Detection Language Modelling

Syntopical Graphs for Computational Argumentation Tasks

no code implementations ACL 2021 Joe Barrow, Rajiv Jain, Nedim Lipka, Franck Dernoncourt, Vlad Morariu, Varun Manjunatha, Douglas Oard, Philip Resnik, Henning Wachsmuth

Approaches to computational argumentation tasks such as stance detection and aspect detection have largely focused on the text of independent claims, losing out on potentially valuable context provided by the rest of the collection.

Stance Detection

UMIC: An Unreferenced Metric for Image Captioning via Contrastive Learning

1 code implementation ACL 2021 Hwanhee Lee, Seunghyun Yoon, Franck Dernoncourt, Trung Bui, Kyomin Jung

Also, we observe critical problems of the previous benchmark dataset (i. e., human annotations) on image captioning metric, and introduce a new collection of human annotations on the generated captions.

Contrastive Learning Image Captioning +1

Learning by Planning: Language-Guided Global Image Editing

1 code implementation CVPR 2021 Jing Shi, Ning Xu, Yihang Xu, Trung Bui, Franck Dernoncourt, Chenliang Xu

Recently, language-guided global image editing draws increasing attention with growing application potentials.

IGA : An Intent-Guided Authoring Assistant

1 code implementation14 Apr 2021 Simeng Sun, Wenlong Zhao, Varun Manjunatha, Rajiv Jain, Vlad Morariu, Franck Dernoncourt, Balaji Vasan Srinivasan, Mohit Iyyer

While large-scale pretrained language models have significantly improved writing assistance functionalities such as autocomplete, more complex and controllable writing assistants have yet to be explored.

Language Modelling Pretrained Language Models

Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU Models

no code implementations NAACL 2021 Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, Xia Hu

These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample.

User Factor Adaptation for User Embedding via Multitask Learning

1 code implementation EACL (AdaptNLP) 2021 Xiaolei Huang, Michael J. Paul, Robin Burke, Franck Dernoncourt, Mark Dredze

In this study, we treat the user interest as domains and empirically examine how the user language can vary across the user factor in three English social media datasets.

Text Classification

MadDog: A Web-based System for Acronym Identification and Disambiguation

1 code implementation EACL 2021 Amir Pouran Ben Veyseh, Franck Dernoncourt, Walter Chang, Thien Huu Nguyen

However, none of the existing works provide a unified solution capable of processing acronyms in various domains and to be publicly available.

Learning to Emphasize: Dataset and Shared Task Models for Selecting Emphasis in Presentation Slides

no code implementations2 Jan 2021 Amirreza Shirani, Giai Tran, Hieu Trinh, Franck Dernoncourt, Nedim Lipka, Paul Asente, Jose Echevarria, Thamar Solorio

We evaluate a range of state-of-the-art models on this novel dataset by organizing a shared task and inviting multiple researchers to model emphasis in this new domain.

Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding

no code implementations22 Dec 2020 Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen, Walter Chang, Leo Anthony Celi

To push forward research in this direction, we have organized two shared task for acronym identification and acronym disambiguation in scientific documents, named AI@SDU and AD@SDU, respectively.

Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering

no code implementations COLING 2020 Quan Tran, Nhan Dam, Tuan Lai, Franck Dernoncourt, Trung Le, Nham Le, Dinh Phung

Interpretability and explainability of deep neural networks are challenging due to their scale, complexity, and the agreeable notions on which the explaining process rests.

Question Answering

What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation

2 code implementations COLING 2020 Amir Pouran Ben Veyseh, Franck Dernoncourt, Quan Hung Tran, Thien Huu Nguyen

The proposed model outperforms the state-of-the-art models on the new AD dataset, providing a strong baseline for future research on this dataset.

Introducing Syntactic Structures into Target Opinion Word Extraction with Deep Learning

no code implementations EMNLP 2020 Amir Pouran Ben Veyseh, Nasim Nouri, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen

In this work, we propose to incorporate the syntactic structures of the sentences into the deep learning models for TOWE, leveraging the syntax-based opinion possibility scores and the syntactic connections between the words.

Aspect-oriented Opinion Extraction

Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation

no code implementations Findings of the Association for Computational Linguistics 2020 Amir Pouran Ben Veyseh, Nasim Nour, Franck Dernoncourt, Quan Hung Tran, Dejing Dou, Thien Huu Nguyen

In addition, we propose a mechanism to obtain the importance scores for each word in the sentences based on the dependency trees that are then injected into the model to improve the representation vectors for ABSA.

Aspect-Based Sentiment Analysis

Learning to Fuse Sentences with Transformers for Summarization

1 code implementation EMNLP 2020 Logan Lebanoff, Franck Dernoncourt, Doo Soon Kim, Lidan Wang, Walter Chang, Fei Liu

The ability to fuse sentences is highly attractive for summarization systems because it is an essential step to produce succinct abstracts.

Sentence Fusion

A Benchmark and Baseline for Language-Driven Image Editing

no code implementations5 Oct 2020 Jing Shi, Ning Xu, Trung Bui, Franck Dernoncourt, Zheng Wen, Chenliang Xu

To solve this new task, we first present a new language-driven image editing dataset that supports both local and global editing with editing operation and mask annotations.

SemEval-2020 Task 6: Definition extraction from free text with the DEFT corpus

no code implementations SEMEVAL 2020 Sasha Spala, Nicholas A. Miller, Franck Dernoncourt, Carl Dockhorn

Research on definition extraction has been conducted for well over a decade, largely with significant constraints on the type of definitions considered.

Definition Extraction Relation Extraction +1

SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media

no code implementations SEMEVAL 2020 Amirreza Shirani, Franck Dernoncourt, Nedim Lipka, Paul Asente, Jose Echevarria, Thamar Solorio

In this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media.

POS TAG

Bayesian Optimization for Selecting Efficient Machine Learning Models

no code implementations2 Aug 2020 Lidan Wang, Franck Dernoncourt, Trung Bui

The performance of many machine learning models depends on their hyper-parameter settings.

Model Selection

Exploiting the Syntax-Model Consistency for Neural Relation Extraction

no code implementations ACL 2020 Amir Pouran Ben Veyseh, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen

In order to overcome these issues, we propose a novel deep learning model for RE that uses the dependency trees to extract the syntax-based importance scores for the words, serving as a tree representation to introduce syntactic information into the models with greater generalization.

Multi-Task Learning Relation Extraction

Extensively Matching for Few-shot Learning Event Detection

1 code implementation WS 2020 Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen

In this work, weformulate event detection as a few-shot learn-ing problem to enable to extend event detec-tion to new event types.

Event Detection Few-Shot Learning

Open-Domain Question Answering with Pre-Constructed Question Spaces

no code implementations NAACL 2021 Jinfeng Xiao, Lidan Wang, Franck Dernoncourt, Trung Bui, Tong Sun, Jiawei Han

Our reader-retriever first uses an offline reader to read the corpus and generate collections of all answerable questions associated with their answers, and then uses an online retriever to respond to user queries by searching the pre-constructed question spaces for answers that are most likely to be asked in the given way.

Information Retrieval Knowledge Graphs +1

Interaction Matching for Long-Tail Multi-Label Classification

no code implementations18 May 2020 Sean MacAvaney, Franck Dernoncourt, Walter Chang, Nazli Goharian, Ophir Frieder

We present an elegant and effective approach for addressing limitations in existing multi-label classification models by incorporating interaction matching, a concept shown to be useful for ad-hoc search result ranking.

Classification General Classification +1

Let Me Choose: From Verbal Context to Font Selection

2 code implementations ACL 2020 Amirreza Shirani, Franck Dernoncourt, Jose Echevarria, Paul Asente, Nedim Lipka, Thamar Solorio

In this paper, we aim to learn associations between visual attributes of fonts and the verbal context of the texts they are typically applied to.

DSTC8-AVSD: Multimodal Semantic Transformer Network with Retrieval Style Word Generator

no code implementations1 Apr 2020 Hwanhee Lee, Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Kyomin Jung

Audio Visual Scene-aware Dialog (AVSD) is the task of generating a response for a question with a given scene, video, audio, and the history of previous turns in the dialog.

Word Embeddings

A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients

no code implementations LREC 2020 Edward T. Moseley, Joy T. Wu, Jonathan Welt, John Foote, Patrick D. Tyler, David W. Grant, Eric T. Carlson, Sebastian Gehrmann, Franck Dernoncourt, Leo Anthony Celi

In this paper, we introduce a dataset for patient phenotyping, a task that is defined as the identification of whether a patient has a given medical condition (also referred to as clinical indication or phenotype) based on their patient note.

Natural Language Processing Patient Phenotyping

Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition

2 code implementations LREC 2020 Xiaolei Huang, Linzi Xing, Franck Dernoncourt, Michael J. Paul

Existing research on fairness evaluation of document classification models mainly uses synthetic monolingual data without ground truth for author demographic attributes.

Document Classification Fairness +2

Exploiting the Matching Information in the Support Set for Few Shot Event Classification

no code implementations13 Feb 2020 Viet Dac Lai, Franck Dernoncourt, Thien Huu Nguyen

The existing event classification (EC) work primarily focuseson the traditional supervised learning setting in which models are unableto extract event mentions of new/unseen event types.

Classification Few-Shot Learning +2

Variational Hierarchical Dialog Autoencoder for Dialog State Tracking Data Augmentation

1 code implementation EMNLP 2020 Kang Min Yoo, Hanbit Lee, Franck Dernoncourt, Trung Bui, Walter Chang, Sang-goo Lee

Recent works have shown that generative data augmentation, where synthetic samples generated from deep generative models complement the training dataset, benefit NLP tasks.

Data Augmentation Dialogue State Tracking +2

A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency

1 code implementation5 Nov 2019 Amir Pouran Ben Veyseh, Franck Dernoncourt, Dejing Dou, Thien Huu Nguyen

In this work, we propose a novel model for DE that simultaneously performs the two tasks in a single framework to benefit from their inter-dependencies.

Definition Extraction Multi-Task Learning +1

Improving Slot Filling by Utilizing Contextual Information

no code implementations WS 2020 Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen

To address this issue, in this paper, we propose a novel method to incorporate the contextual information in two different levels, i. e., representation level and task-specific (i. e., label) level.

Intent Detection Slot Filling +1

Analyzing Sentence Fusion in Abstractive Summarization

no code implementations WS 2019 Logan Lebanoff, John Muchovej, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang, Fei Liu

While recent work in abstractive summarization has resulted in higher scores in automatic metrics, there is little understanding on how these systems combine information taken from multiple document sentences.

Abstractive Text Summarization Sentence Fusion

Margin Call: an Accessible Web-based Text Viewer with Generated Paragraph Summaries in the Margin

no code implementations WS 2019 Nabah Rizvi, Sebastian Gehrmann, Franck Dernoncourt

We present Margin Call, a web-based text viewer that automatically generates short summaries for each paragraph of the text and displays the summaries in the margin of the text next to the corresponding paragraph.

Propagate-Selector: Detecting Supporting Sentences for Question Answering via Graph Neural Networks

1 code implementation LREC 2020 Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Kyomin Jung

In this study, we propose a novel graph neural network called propagate-selector (PS), which propagates information over sentences to understand information that cannot be inferred when considering sentences in isolation.

Answer Selection

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition

no code implementations8 Aug 2019 Subhadeep Dey, Petr Motlicek, Trung Bui, Franck Dernoncourt

In this paper, we explore various approaches for semi supervised learning in an end to end automatic speech recognition (ASR) framework.

Automatic Speech Recognition

DEFT: A corpus for definition extraction in free- and semi-structured text

no code implementations WS 2019 Sasha Spala, Nicholas A. Miller, Yiming Yang, Franck Dernoncourt, Carl Dockhorn

Definition extraction has been a popular topic in NLP research for well more than a decade, but has been historically limited to well-defined, structured, and narrow conditions.

Definition Extraction

Expressing Visual Relationships via Language

1 code implementation ACL 2019 Hao Tan, Franck Dernoncourt, Zhe Lin, Trung Bui, Mohit Bansal

To push forward the research in this direction, we first introduce a new language-guided image editing dataset that contains a large number of real image pairs with corresponding editing instructions.

Image Captioning

A Compare-Aggregate Model with Latent Clustering for Answer Selection

no code implementations30 May 2019 Seunghyun Yoon, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Kyomin Jung

In this paper, we propose a novel method for a sentence-level answer-selection task that is a fundamental problem in natural language processing.

Answer Selection Language Modelling +2

Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation

no code implementations NAACL 2019 Sebastian Gehrmann, Steven Layne, Franck Dernoncourt

Titles of short sections within long documents support readers by guiding their focus towards relevant passages and by providing anchor-points that help to understand the progression of the document.

Reading Comprehension

PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts

8 code implementations IJCNLP 2017 Franck Dernoncourt, Ji Young Lee

First, the majority of datasets for sequential short-text classification (i. e., classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task.

Classification General Classification +1

Transfer Learning for Named-Entity Recognition with Neural Networks

no code implementations LREC 2018 Ji Young Lee, Franck Dernoncourt, Peter Szolovits

In particular, we demonstrate that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification.

De-identification named-entity-recognition +2

Neural Networks for Joint Sentence Classification in Medical Paper Abstracts

5 code implementations EACL 2017 Franck Dernoncourt, Ji Young Lee, Peter Szolovits

Existing models based on artificial neural networks (ANNs) for sentence classification often do not incorporate the context in which sentences appear, and classify sentences individually.

Classification General Classification +2

Feature-Augmented Neural Networks for Patient Note De-identification

no code implementations WS 2016 Ji Young Lee, Franck Dernoncourt, Ozlem Uzuner, Peter Szolovits

In this work, we explore a method to incorporate human-engineered features as well as features derived from EHRs to a neural-network-based de-identification system.

De-identification

Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification

1 code implementation27 Sep 2016 Franck Dernoncourt, Ji Young Lee

Therefore it is a useful technique for tuning ANN models to yield the best performances for natural language processing tasks.

Dialog Act Classification Gaussian Processes +2

Mapping distributional to model-theoretic semantic spaces: a baseline

1 code implementation11 Jul 2016 Franck Dernoncourt

Word embeddings have been shown to be useful across state-of-the-art systems in many natural language processing tasks, ranging from question answering systems to dependency parsing.

Dependency Parsing Natural Language Processing +2

De-identification of Patient Notes with Recurrent Neural Networks

1 code implementation10 Jun 2016 Franck Dernoncourt, Ji Young Lee, Ozlem Uzuner, Peter Szolovits

It yields an F1-score of 97. 85 on the i2b2 2014 dataset, with a recall 97. 38 and a precision of 97. 32, and an F1-score of 99. 23 on the MIMIC de-identification dataset, with a recall 99. 25 and a precision of 99. 06.

De-identification Feature Engineering

Robust Dialog State Tracking for Large Ontologies

no code implementations7 May 2016 Franck Dernoncourt, Ji Young Lee, Trung H. Bui, Hung H. Bui

The Dialog State Tracking Challenge 4 (DSTC 4) differentiates itself from the previous three editions as follows: the number of slot-value pairs present in the ontology is much larger, no spoken language understanding output is given, and utterances are labeled at the subdialog level.

Coreference Resolution Spoken Language Understanding

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