activity
19972022
most citedNeural Morphological Tagging from Characters for Morphologically Rich Languages

16 citations · 29 across the 9 of their papers we have counts for

collaborators

9 papers

cs.LG2022

Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations

Ioannis Dikeoulias, Saadullah Amin, Günter Neumann

Temporal knowledge graph completion (TKGC) has become a popular approach for reasoning over the event and temporal knowledge graphs, targeting the completion of knowledge with accu…

cs.CL2022

Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts

Saadullah Amin, Noon Pokaratsiri Goldstein, Morgan Kelly Wixted +3

Despite the advances in digital healthcare systems offering curated structured knowledge, much of the critical information still lies in large volumes of unlabeled and unstructured…

cs.LG2020★ 3 cited

LowFER: Low-rank Bilinear Pooling for Link Prediction

Saadullah Amin, Stalin Varanasi, Katherine Ann Dunfield +1

Knowledge graphs are incomplete by nature, with only a limited number of observed facts from the world knowledge being represented as structured relations between entities. To part…

cs.CL2020

A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction

Saadullah Amin, Katherine Ann Dunfield, Anna Vechkaeva +1

Fact triples are a common form of structured knowledge used within the biomedical domain. As the amount of unstructured scientific texts continues to grow, manual annotation of the…

cs.CL2018

LightRel SemEval-2018 Task 7: Lightweight and Fast Relation Classification

Tyler Renslow, Günter Neumann

We present LightRel, a lightweight and fast relation classifier. Our goal is to develop a high baseline for different relation extraction tasks. By defining only very few data-inte…

cs.CL2017★ 7 cited

How Robust Are Character-Based Word Embeddings in Tagging and MT Against Wrod Scramlbing or Randdm Nouse?

Georg Heigold, Günter Neumann, Josef van Genabith

This paper investigates the robustness of NLP against perturbed word forms. While neural approaches can achieve (almost) human-like accuracy for certain tasks and conditions, they…