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.
Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths
This map shows the geographic impact of Lili Mou'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 Lili Mou with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lili Mou more than expected).
This network shows the impact of papers produced by Lili Mou. 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 Lili Mou. The network helps show where Lili Mou may publish in the future.
Co-authorship network of co-authors of Lili Mou
This figure shows the co-authorship network connecting the top 25 collaborators of Lili Mou.
A scholar is included among the top collaborators of Lili Mou 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 Lili Mou. Lili Mou is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Asghar, Nabiha, et al.. (2020). Progressive Memory Banks for Incremental Domain Adaptation. International Conference on Learning Representations.7 indexed citations
8.
Li, Jingjing, Zichao Li, Lili Mou, et al.. (2020). Unsupervised Text Generation by Learning from Search. Neural Information Processing Systems. 33. 10820–10831.5 indexed citations
9.
Mou, Lili, et al.. (2018). Disentangled Representation Learning for Text Style Transfer.. arXiv (Cornell University).14 indexed citations
Xu, Yan, Ran Jia, Lili Mou, et al.. (2016). Improved Relation Classification by Deep Recurrent Neural Networks with Data Augmentation. arXiv (Cornell University). 1461–1470.69 indexed citations
Mou, Lili, Hao Peng, Ge Li, et al.. (2015). Tree-based Convolution: A New Neural Architecture for Sentence Modeling.. arXiv (Cornell University).5 indexed citations
17.
Mou, Lili, Rui-Jun Yan, Ge Li, Lu Zhang, & Zhi Jin. (2015). Backbone Language Modeling for Constrained Natural Language Generation.. arXiv (Cornell University).2 indexed citations
18.
Mou, Lili, Rui Men, Ge Li, et al.. (2015). Recognizing Entailment and Contradiction by Tree-based Convolution. arXiv (Cornell University).12 indexed citations
19.
Xu, Yan, Lili Mou, Ge Li, et al.. (2015). Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths. 1785–1794.415 indexed citations breakdown →
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.