Chen Lin

Works (5)

Updated: July 5th, 2023 15:10

2020 journal article

Going deeper: Automatic short-answer grading by combining student and question models

USER MODELING AND USER-ADAPTED INTERACTION, 30(1), 51–80.

By: Y. Zhang n, C. Lin n & M. Chi n

author keywords: Automatic short-answer grading; Machine learning; Deep belief network
TL;DR: Overall, the results on a real-world corpus demonstrate that 1) leveraging student and question models to the conventional answer-based approach can greatly enhance the performance of ASAG, and 2) deep learning models such as DBN can be productively applied to the task of ASAGs. (via Semantic Scholar)
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4. Quality Education (OpenAlex)
Source: Web Of Science
Added: March 30, 2020

2018 article

NL2API: A Framework for Bootstrapping Service Recommendation using Natural Language Queries

2018 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (IEEE ICWS 2018), pp. 235–242.

By: C. Lin n, A. Kalia *, J. Xiao *, M. Vukovic* & N. Anerousis *

author keywords: service discovery; topic modeling; deep learning; community detection; clustering; web services
TL;DR: NL2API, a framework that relies solely on service descriptions for recommending services, is proposed and evaluated, showing that for sizable datasets such as Programmable Web NL2API outperforms baseline approaches. (via Semantic Scholar)
Source: Web Of Science
Added: September 16, 2019

2017 conference paper

LSTM for septic shock: Adding unreliable labels to reliable predictions

2017 IEEE International Conference on Big Data (Big Data), 1233–1242.

By: Y. Zhang n, C. Lin n, M. Chi n, J. Ivy n, M. Capan * & J. Huddleston *

TL;DR: A generic framework to predict septic shock based on Long-Short Term Memory (LSTM) model, which is capable of memorizing temporal dependencies over a long period, is proposed and the robustness of the method is validated using three sets of clinician-proposed adjusted ground-truth labels. (via Semantic Scholar)
Source: NC State University Libraries
Added: August 6, 2018

2016 conference paper

An analysis of feature selection and reward function for model-based reinforcement learning

Intelligent tutoring systems, its 2016, 0684, 504–505.

By: S. Shen, C. Lin, B. Mostafavi, T. Barnes & M. Chi

Source: NC State University Libraries
Added: August 6, 2018

2016 conference paper

Intervention-BKT: Incorporating instructional interventions into Bayesian knowledge tracing

Intelligent tutoring systems, its 2016, 0684, 208–218.

By: C. Lin & M. Chi

Source: NC State University Libraries
Added: August 6, 2018

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