@article{zhang_lin_chi_2020, title={Going deeper: Automatic short-answer grading by combining student and question models}, volume={30}, ISSN={["1573-1391"]}, DOI={10.1007/s11257-019-09251-6}, number={1}, journal={USER MODELING AND USER-ADAPTED INTERACTION}, author={Zhang, Yuan and Lin, Chen and Chi, Min}, year={2020}, month={Mar}, pages={51–80} }
 @article{lin_kalia_xiao_vukovic_anerousis_2018, title={NL2API: A Framework for Bootstrapping Service Recommendation using Natural Language Queries}, DOI={10.1109/ICWS.2018.00037}, abstractNote={Existing approaches to recommend services using natural language queries are supervised or unsupervised. Supervised approaches rely on a dataset with natural language queries annotated with categorizing labels. As the annotation process is manual and requires deep domain knowledge, these approaches are not readily applicable on new datasets. On the other hand, unsupervised approaches overcome the limitation. To date, unsupervised approaches are primarily based on matching keywords, entity relationships, topics and clusters. Keywords and entity relationships ignore the semantic similarity between a query and services. Topics and clusters capture the semantic similarity, but rely on mashups that explicitly capture relationships between services. Again, for new services, the information are not readily available. We propose NL2API, a framework that relies solely on service descriptions for recommending services. NL2API has the benefit of being immediately applicable as a bootstrap recommender for new datasets. To capture relationships among services, NL2API provides different approaches to construct communities where a community represents an abstraction over a group of services. Based on the communities and users' queries, NL2API applies a query matching approach to recommend top-k services. We evaluate NL2API on datasets collected from Programmable Web and API Harmony. Our evaluation shows that for sizable datasets such as Programmable Web NL2API outperforms baseline approaches.}, journal={2018 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (IEEE ICWS 2018)}, author={Lin, Chen and Kalia, Anup K. and Xiao, Jin and Vukovic, Maja and Anerousis, Nikos}, year={2018}, pages={235–242} }
 @inproceedings{zhang_lin_chi_ivy_capan_huddleston_2017, title={LSTM for septic shock: Adding unreliable labels to reliable predictions}, DOI={10.1109/bigdata.2017.8258049}, abstractNote={Sepsis is a leading cause of death over the world and septic shock, the most severe complication of sepsis, reaches a mortality rate as high as 50%. Early diagnosis and treatment can prevent most morbidity and mortality. Nowadays, the increasing availability of the electronic health records (EHRs) has generated great interests in developing models to predict acute medical conditions such as septic shock. However, septic shock prediction faces two major challenges : 1) how to capture the informative progression of septic shock in a long visit to hospital of a patient; and 2) how to obtain reliable predictions without well-established moment-by-moment ground-truth labels for septic shock. In this work, we proposed 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. The framework integrates two levels of imperfect yet informative labels to jointly learn the discriminative patterns of septic shock: ICD-9 code as the visit-level label and the clinical criteria designed by domain experts as the moment-by-moment event-level label. We evaluate our method on a real-world data extracted from an EHR system constituted by 12,954 visits and 1,348,625 events, and compare it against multiple baselines. The robustness of the method is validated using three sets of clinician-proposed adjusted ground-truth labels. Also, we explore whether the framework is effective for the early prediction of the patients developing septic shock. The experimental results demonstrate the superiority of our proposed method in the task of septic shock prediction.}, booktitle={2017 IEEE International Conference on Big Data (Big Data)}, author={Zhang, Y. and Lin, C. and Chi, M. and Ivy, J. and Capan, M. and Huddleston, J. M.}, year={2017}, pages={1233–1242} }
 @inproceedings{shen_lin_mostafavi_barnes_chi_2016, title={An analysis of feature selection and reward function for model-based reinforcement learning}, volume={0684}, booktitle={Intelligent tutoring systems, its 2016}, author={Shen, S. T. and Lin, C. and Mostafavi, B. and Barnes, T. and Chi, M.}, year={2016}, pages={504–505} }
 @inproceedings{lin_chi_2016, title={Intervention-BKT: Incorporating instructional interventions into Bayesian knowledge tracing}, volume={0684}, booktitle={Intelligent tutoring systems, its 2016}, author={Lin, C. and Chi, M.}, year={2016}, pages={208–218} }