Nguyen Vo

Google (United States)

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Kyumin Lee

Worcester Polytechnic Institute

Chitta Baral

Arizona State University

Sandeep Tata

Google (United States)

Yonggwan Won

Chonnam National University

Minh-Tuan T. Hoang

Chonnam National University

Ying Sheng

Google (United States)

Hieu Trung Huynh

Industrial University of Ho Chi Minh City

Thanh Tran

Worcester Polytechnic Institute

James B. Wendt

Google (United States)

Yichao Zhou

Nanjing University of Science and Technology

Cooperative Institutions

Arizona State University

Google (United States)

Decision Systems (United States)

La Roche College

University of California, Berkeley

University of California, San Diego

Chonnam National University

The University of Texas at Austin

Nanjing University of Science and Technology

University of California, Los Angeles

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FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents

Bill Yuchen Lin Ying Sheng Nguyen Vo Sandeep Tata

Extracting structured data from HTML documents is a long-studied problem with a broad range of applications like augmenting knowledge bases, supporting faceted search, and providing domain-specific experiences for key verticals like shopping and movies. Previous approaches have either required a small number of examples for each target site or relied on carefully handcrafted heuristics built over visual renderings of websites. In this paper, we present a novel two-stage neural approach, named FreeDOM, which overcomes both these limitations. The first stage learns a representation for each DOM node in the page by combining both the text and markup information. The second stage captures longer range distance and semantic relatedness using a relational neural network. By combining these stages, FreeDOM is able to generalize to unseen sites after training on a small number of seed sites from that vertical without requiring expensive hand-crafted features over visual renderings of the page. Through experiments on a public dataset with 8 different verticals, we show that FreeDOM beats the previous state of the art by nearly 3.7 F1 points on average without requiring features over rendered pages or expensive hand-crafted features.

Heuristics

Representation

HTML

10.1145/3394486.3403153

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Citations (36)

Event-Object Reasoning with Curated Knowledge Bases: Deriving Missing Information

Lecture notes in computer science (2013)

Chitta Baral Nguyen Vo

Event calculus

10.1007/978-3-642-40564-8_16

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Citations (1)

Learning from Fact-checkers: Analysis and Generation of Fact-checking Language

arXiv (Cornell University) (2019)

Nguyen Vo Kyumin Lee

In fighting against fake news, many fact-checking systems comprised of human-based fact-checking sites (e.g., this http URL and this http URL) and automatic detection systems have been developed in recent years. However, online users still keep sharing fake news even when it has been debunked. It means that early fake news detection may be insufficient and we need another complementary approach to mitigate the spread of misinformation. In this paper, we introduce a novel application of text generation for combating fake news. In particular, we (1) leverage online users named \emph{fact-checkers}, who cite fact-checking sites as credible evidences to fact-check information in public discourse; (2) analyze linguistic characteristics of fact-checking tweets; and (3) propose and build a deep learning framework to generate responses with fact-checking intention to increase the fact-checkers' engagement in fact-checking activities. Our analysis reveals that the fact-checkers tend to refute misinformation and use formal language (e.g. few swear words and Internet slangs). Our framework successfully generates relevant responses, and outperforms competing models by achieving up to 30\% improvements. Our qualitative study also confirms that the superiority of our generated responses compared with responses generated from the existing models.

Misinformation

Leverage (statistics)

Fake News

Source

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Citations (4)

Two-Step Iterative Registration for 2D-Gel Electrophoresis Images

Minh-Tuan T. Hoang Hieu Trung Huynh Nguyen Vo Yonggwan Won Jung‐Ja Kim

2D-gel electrophoresis (2D-GE) is a central technique for separation of protein samples in proteomic research. A 2D-GE image usually contains hundreds of protein spots that should be detected by an image processing technique. Identifying the missing proteins by comparison of two samples such as from healthy person and patient can be helpful in developing medicine or disease control. This matching process is a laborious work that needs to be automatically done by computers with less user intervention as much as possible. In this paper, we propose a two-step iterative registration algorithm for protein spots in 2D-GE images. The algorithm estimates the geometrical differences between the corresponding landmarks and iteratively moves the spot points in the matched gel in order to be closer to its correspondence in the reference gel. The amount of movement of each spot is affected by the Euclidian distances from the spot to all landmarks. The single-step matching is defined with its pros and cons; then the motivation to a two-step iterative registration algorithm and its results are discussed.

Image registration

Feature (linguistics)

Iterative closest point

10.1109/rivf.2007.369168

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Citations (0)

Event-Object Reasoning with Curated Knowledge Bases: Deriving Missing Information

arXiv (Cornell University) (2013)

Chitta Baral Nguyen Vo

The broader goal of our research is to formulate answers to why and how questions with respect to knowledge bases, such as AURA. One issue we face when reasoning with many available knowledge bases is that at times needed information is missing. Examples of this include partially missing information about next sub-event, first sub-event, last sub-event, result of an event, input to an event, destination of an event, and raw material involved in an event. In many cases one can recover part of the missing knowledge through reasoning. In this paper we give a formal definition about how such missing information can be recovered and then give an ASP implementation of it. We then discuss the implication of this with respect to answering why and how questions.

Source

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WiiKey: An Innovative Smartphone Based Wi-Fi Application

International Multi-Symposiums on Computer and Computational Sciences (2008)

Nguyen Vo Hao Shi Jakub Szajman

Many mobile applications such as email, calendar and web browser make use of smartphones technology. WiiKey, a proposed smart application, aims to extend the current smartphone capabilities to allow users to control the next generation Wi-Fi enabled devices found in some cars or home appliances. In this paper, Wi-Fi standards and their applications are first reviewed, and then the design and development of WiiKey prototype are described. Details of implementation of WiiKey using Java are presented. WiiKey has a potential to control smart objects in next generation of mobile devices. WiiKey brings benefits to users in their daily life and may one day become a true 'personal trust device'.

10.1109/imsccs.2008.37

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Citations (11)

Classification of Unbalanced Medical Data with Weighted Regularized Least Squares

Frontiers in Convergence of Bioscience and Information Technologies (2007)

Nguyen Vo Yonggwan Won

In medical diagnosis classification, we often face the unbalanced number of data samples between the classes in which there are not enough samples in rare classes. Conventional competitive learning methods are not suitable in this situation, because they usually tend to be biased to the classes that have the larger number of data samples. In this paper, we proposed a cost-sensitive extension of regularized least square(RLS) algorithm that penalizes errors of different samples with different weights and some rules of thumb to determine those weights. The significantly better classification accuracy of weighted RLS classifiers showed that it is promising substitution of other previous cost-sensitive classification methods for unbalanced data set.

Statistical classification

Rule of thumb

Data set

10.1109/fbit.2007.20

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Citations (27)

The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News

arXiv (Cornell University) (2018)

Nguyen Vo Kyumin Lee

A large body of research work and efforts have been focused on detecting fake news and building online fact-check systems in order to debunk fake news as soon as possible. Despite the existence of these systems, fake news is still wildly shared by online users. It indicates that these systems may not be fully utilized. After detecting fake news, what is the next step to stop people from sharing it? How can we improve the utilization of these fact-check systems? To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities. We found that the guardians usually took less than one day to reply to claims in online conversations and took another day to spread verified information to hundreds of millions of followers. Our proposed recommendation model outperformed four state-of-the-art models by 11%~33%. Our source code and dataset are available at https://github.com/nguyenvo09/CombatingFakeNews.

Misinformation

Code (set theory)

Fake News

10.48550/arxiv.1806.07516

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Citations (31)

Performance Enhancement of RBF Networks in Classification by Removing Outliers in the Training Phase

Lecture notes in computer science (2007)

Hieu Trung Huynh Nguyen Vo Minh-Tuan T. Hoang Yonggwan Won

Training set

Hierarchical RBF

10.1007/978-3-540-73729-2_32

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Citations (0)