Demo Data from SIGIR 2026 / reviewers in the wild / expert
Ming-Lung Chen
dblp:70/7868
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Language models and text generation · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Learning and educational technologies · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › text generation › sentence planning
lexical choice |
0.2 | 1 | 2016 | A Computer-Assistance Learning System for Emotional Wording · IEEE Trans. Knowl. Data Eng. 2016 |
Learning and educational technologies › language learning
language learning support |
0.1 | 1 | 2016 | A Computer-Assistance Learning System for Emotional Wording · IEEE Trans. Knowl. Data Eng. 2016 |
Methods — techniques the papers use, named apart from their topics
ranking scoring function · 0.5pattern learning · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | A Computer-Assistance Learning System for Emotional WordingabstractLanguage learners' limited lexical knowledge leads to imprecise wording. This is especially true when they attempt to express their emotions. Many learners rely heavily on the traditional thesaurus. Unfortunately, this fails to provide appropriate suggestions for lexical choices. To better aid English-as-a-second-language learners with word choices, we propose RESOLVE, which provides ranked synonyms of emotion words based on contextual information. RESOLVE suggests precise emotion words regarding the events in the relevant context. Patterns are learned to capture emotion events, and various factors are considered in the scoring function for ranking emotion words. We also describe an online writing system developed using RESOLVE and evaluate its effectiveness for learning assistance with a writing task. Experimental results showed that RESOLVE yielded a superior performance on NDCG@5 which significantly outperformed both PMI and SVM approaches, and offered better suggestions than Roget's Thesaurus and PIGAI (an online automated essay scoring system). Moreover, when applying it to the writing task, students' appropriateness with emotion words was 30 percent improved. Less-proficient learners benefited more from RESOLVE than highly-proficient learners. Post-tests also showed that after using RESOLVE, less-proficient learners' ability to use emotion words approached that of highly-proficient learners. RESOLVE thus enables learners to use precise emotion words. Wei-Fan Chen 0001, Mei-Hua Chen, Ming-Lung Chen, Lun-Wei Ku |
IEEE Trans. Knowl. Data Eng. | 3 |
| 2012 | Performance evaluation of enhancement of the layered self-scheduling approach for heterogeneous multicore cluster systems
Chao-Chin Wu, Lien Fu Lai, Liang-Tsung Huang, Ming-Lung Chen |
J. Supercomput. | 4 |