ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2310.15569
  4. Cited By
MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in
  the Materials Science Domain

MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain

24 October 2023
Timo Pierre Schrader
Matteo Finco
Stefan Grünewald
Felix Hildebrand
Annemarie Friedrich
    AI4CE
ArXivPDFHTML

Papers citing "MuLMS: A Multi-Layer Annotated Text Corpus for Information Extraction in the Materials Science Domain"

1 / 1 papers shown
Title
Applying Occam's Razor to Transformer-Based Dependency Parsing: What
  Works, What Doesn't, and What is Really Necessary
Applying Occam's Razor to Transformer-Based Dependency Parsing: What Works, What Doesn't, and What is Really Necessary
Stefan Grünewald
Annemarie Friedrich
Jonas Kuhn
56
11
0
23 Oct 2020
1