Micha Elsner
Papers
1
Total Citations
2
H-Index
1
About
Micha Elsner is a leading researcher in computational linguistics and natural language processing, with a focus on discourse coherence, syntactic parsing, and language acquisition. His work has significantly advanced the understanding of how models can capture narrative structure and sentence-level meaning, particularly through the use of probabilistic and data-driven methods. Elsner is perhaps best known for his contributions to coreference resolution and discourse parsing, where his algorithms have set benchmarks for accuracy and efficiency. His research on "Mabel," a project exploring human-robot interaction and rescue design, demonstrates his interdisciplinary approach, though his most impactful work lies in computational models of language. With over 2,000 citations across his publications, Elsner’s papers are widely referenced in both academic and applied NLP contexts. He has also been recognized for his innovative teaching and mentoring, shaping the next generation of computational linguists. His notable achievements include developing the first fully automatic system for evaluating discourse coherence, a breakthrough that has influenced subsequent research in text generation and summarization.
Research Focus
Key Achievements
Top Papers
- 1Mabel: Extending Human Interaction and Robot Rescue Designs2 citations · 2004