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researcher

Man Lan

15 papers hereh-index 141.1k citations35 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author12

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.CL7
  • cs.AI5
  • cs.LG2
  • cs.IR1
same name
  • Man Lan — 7 papers, h 3
  • Man Lan — 5 papers, h 6
  • Man Lan — 3 papers, h 0
  • Man Lan — 1 paper, h 24
  • Man Lan — 1 paper, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202025
most citedRelational Reflection Entity Alignment

119 citations · 247 across the 14 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.AI2022★ 1 cited

LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation

Xin Mao, Wenting Wang, Yuanbin Wu +1

Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core step of bridging and integrating multi-source KGs. In this paper, we argue that existing G…

cs.CL2022

Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition

Hao Zhou, Man Lan, Yuanbin Wu +2

Due to the absence of connectives, implicit discourse relation recognition (IDRR) is still a challenging and crucial task in discourse analysis. Most of the current work adopted mu…

cs.AI2022★ 5 cited

A Simple Temporal Information Matching Mechanism for Entity Alignment Between Temporal Knowledge Graphs

Li Cai, Xin Mao, Meirong Ma +3

Entity alignment (EA) aims to find entities in different knowledge graphs (KGs) that refer to the same object in the real world. Recent studies incorporate temporal information to…

cs.CL2022

Few Clean Instances Help Denoising Distant Supervision

Yufang Liu, Ziyin Huang, Yijun Wang +5

Existing distantly supervised relation extractors usually rely on noisy data for both model training and evaluation, which may lead to garbage-in-garbage-out systems. To alleviate…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.