Yangfeng Ji

4.0k total citations · 1 hit paper
43 papers, 1.6k citations indexed

About

Yangfeng Ji is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Yangfeng Ji has authored 43 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Information Systems. Recurrent topics in Yangfeng Ji's work include Topic Modeling (32 papers), Natural Language Processing Techniques (25 papers) and Speech and dialogue systems (6 papers). Yangfeng Ji is often cited by papers focused on Topic Modeling (32 papers), Natural Language Processing Techniques (25 papers) and Speech and dialogue systems (6 papers). Yangfeng Ji collaborates with scholars based in United States, United Kingdom and China. Yangfeng Ji's co-authors include Jacob Eisenstein, Noah A. Smith, Jianfeng Gao, Margaret Mitchell, Chris Brockett, Michael Auli, Alessandro Sordoni, Michel Galley, Bill Dolan and Jian‐Yun Nie and has published in prestigious journals such as Scientific Reports, Accident Analysis & Prevention and Transactions of the Association for Computational Linguistics.

In The Last Decade

Yangfeng Ji

42 papers receiving 1.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A Neural Network Approach to Context-Sensitive Generation of Conversational Responses

2015 432 citations Alessandro Sordoni, Michel Galley et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yangfeng Ji United States	19	1.5k	252	133	50	42	43	1.6k
Loïc Barrault France	11	1.4k 1.0×	405 1.6×	158 1.2×	46 0.9×	13 0.3×	36	1.6k
Alexander Koller Germany	19	1.2k 0.8×	192 0.8×	59 0.4×	36 0.7×	33 0.8×	107	1.5k
Mitesh M. Khapra India	19	1.2k 0.8×	442 1.8×	141 1.1×	43 0.9×	89 2.1×	86	1.5k
Alessandro Sordoni Canada	15	1.6k 1.1×	468 1.9×	243 1.8×	29 0.6×	12 0.3×	39	1.7k
Steve Yuan United States	4	852 0.6×	152 0.6×	164 1.2×	96 1.9×	13 0.3×	4	1.1k
Saizheng Zhang Canada	7	1.5k 1.0×	484 1.9×	147 1.1×	29 0.6×	57 1.4×	10	1.7k
John B. Lowe United States	8	1.7k 1.2×	147 0.6×	193 1.5×	30 0.6×	18 0.4×	15	2.0k
Swabha Swayamdipta United States	12	885 0.6×	132 0.5×	98 0.7×	67 1.3×	15 0.4×	31	1.0k
Rashmi Prasad United States	26	2.7k 1.9×	166 0.7×	193 1.5×	57 1.1×	36 0.9×	78	3.1k
Sandra Kübler United States	17	2.1k 1.4×	119 0.5×	217 1.6×	39 0.8×	15 0.4×	97	2.2k

Countries citing papers authored by Yangfeng Ji

Since Specialization

Citations

This map shows the geographic impact of Yangfeng Ji's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yangfeng Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yangfeng Ji more than expected).

Fields of papers citing papers by Yangfeng Ji

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yangfeng Ji. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yangfeng Ji. The network helps show where Yangfeng Ji may publish in the future.

Co-authorship network of co-authors of Yangfeng Ji

This figure shows the co-authorship network connecting the top 25 collaborators of Yangfeng Ji. A scholar is included among the top collaborators of Yangfeng Ji based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yangfeng Ji. Yangfeng Ji is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Ji, Yangfeng, et al.. (2025). Syntactic Blind Spots: How Misalignment Leads to LLMs’ Mathematical Errors. 1–14.

2.

Ji, Yangfeng, et al.. (2024). Detection of Microdefects in Fabric with Multifarious Patterns and Colors Using Deep Convolutional Neural Network. Advances in Polymer Technology. 2024. 1–10. 1 indexed citations

3.

Chen, Hanjie, et al.. (2023). REV: Information-Theoretic Evaluation of Free-Text Rationales. 2007–2030. 10 indexed citations

4.

Ji, Yangfeng, et al.. (2023). Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values. 266–275. 4 indexed citations

5.

Chen, Hanjie, et al.. (2023). Improving Interpretability via Explicit Word Interaction Graph Layer. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 13528–13537. 2 indexed citations

6.

Chernyavskiy, Pavel, et al.. (2023). Evaluation of Neural Machine translation for conversion of International Classification of disease codes to the Abbreviated injury Scale. Accident Analysis & Prevention. 191. 107183–107183. 2 indexed citations

7.

Chen, Hanjie, et al.. (2022). Identifying the Source of Vulnerability in Explanation Discrepancy: A Case Study in Neural Text Classification. 356–370. 1 indexed citations

8.

Chen, Hannah, Yangfeng Ji, & David Evans. (2022). Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models. 632–647. 2 indexed citations

9.

Zhao, Jianqiao, Yanyang Li, Yangfeng Ji, et al.. (2022). FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows. 10469–10483. 4 indexed citations

10.

Ji, Yangfeng, et al.. (2022). White-box Testing of NLP models with Mask Neuron Coverage. 1547–1558. 1 indexed citations

11.

Zhang, Rui, Yangfeng Ji, Yue Zhang, & Rebecca J. Passonneau. (2022). Contrastive Data and Learning for Natural Language Processing. 39–47. 21 indexed citations

12.

Chen, Hanjie, et al.. (2022). Self-training with Two-phase Self-augmentation for Few-shot Dialogue Generation. 2770–2784. 1 indexed citations

13.

Liu, Xiaodong, et al.. (2021). HittER: Hierarchical Transformers for Knowledge Graph Embeddings. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 10395–10407. 56 indexed citations

14.

Chen, Hanjie, et al.. (2020). Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection. 5578–5593. 48 indexed citations

15.

Ji, Yangfeng, Gholamreza Haffari, & Jacob Eisenstein. (2016). A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models. 332–342. 62 indexed citations

16.

Ji, Yangfeng, Trevor Cohn, Lingpeng Kong, Chris Dyer, & Jacob Eisenstein. (2015). Document Context Language Models. arXiv (Cornell University). 11 indexed citations

17.

Sordoni, Alessandro, Michel Galley, Michael Auli, et al.. (2015). A Neural Network Approach to Context-Sensitive Generation of Conversational Responses. 196–205. 432 indexed citations breakdown →

18.

Ji, Yangfeng, Dilek Hakkani-Tür, Aslı Çelikyılmaz, Larry Heck, & Gökhan Tür. (2014). A variational Bayesian model for user intent detection. 9. 4072–4076. 2 indexed citations

19.

Ji, Yangfeng & Jacob Eisenstein. (2013). Discriminative Improvements to Distributional Sentence Similarity. 891–896. 81 indexed citations

20.

Ji, Yangfeng. (2005). Research on Algorithm of Marching Method for Intersection. Journal of Engineering Graphics. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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