Search Results for author: Joseph Chee Chang

Found 9 papers, 0 papers with code

PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers

no code implementations5 Mar 2024 Yoonjoo Lee, Hyeonsu B. Kang, Matt Latzke, Juho Kim, Jonathan Bragg, Joseph Chee Chang, Pao Siangliulue

With the rapid growth of scholarly archives, researchers subscribe to "paper alert" systems that periodically provide them with recommendations of recently published papers that are similar to previously collected papers.

Personalized Jargon Identification for Enhanced Interdisciplinary Communication

no code implementations16 Nov 2023 Yue Guo, Joseph Chee Chang, Maria Antoniak, Erin Bransom, Trevor Cohen, Lucy Lu Wang, Tal August

We collect a dataset of over 10K term familiarity annotations from 11 computer science researchers for terms drawn from 100 paper abstracts.

Beyond Summarization: Designing AI Support for Real-World Expository Writing Tasks

no code implementations5 Apr 2023 Zejiang Shen, Tal August, Pao Siangliulue, Kyle Lo, Jonathan Bragg, Jeff Hammerbacher, Doug Downey, Joseph Chee Chang, David Sontag

In this position paper, we argue that developing AI supports for expository writing has unique and exciting research challenges and can lead to high real-world impacts.

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

Fuse: In-Situ Sensemaking Support in the Browser

no code implementations31 Aug 2022 Andrew Kuznetsov, Joseph Chee Chang, Nathan Hahn, Napol Rachatasumrit, Bradley Breneisen, Julina Coupland, Aniket Kittur

People spend a significant amount of time trying to make sense of the internet, collecting content from a variety of sources and organizing it to make decisions and achieve their goals.


Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time

no code implementations8 Jan 2018 Ting-Hao 'Kenneth' Huang, Joseph Chee Chang, Jeffrey P. Bigham

Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs.

Open-Domain Dialog

Recurrent-Neural-Network for Language Detection on Twitter Code-Switching Corpus

no code implementations14 Dec 2014 Joseph Chee Chang, Chu-Cheng Lin

However, people who are capable of using more than one language often communicate using multiple languages at the same time.

Machine Translation Part-Of-Speech Tagging +2

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