Discourse relation parsing plays a crucial role in uncovering the logical structure of text, yet existing corpora focus almost exclusively on general-domain genres, leaving specialized fields like engineering under-resourced. We introduce ENG‐DRB, the first PDTB‐style discourse relation corpus derived from transcripts of hands‐on engineering tutorial videos. ENG‐DRB comprises 11 tutorials spanning civil, mechanical, and electrical/electronics engineering (155 minutes total) with 1,215 annotated relations. Compared to general‐domain benchmarks, this dataset features a high proportion of explicit senses, dense causal and temporal relations, and frequent overlapping and embedded senses. Our benchmarking experiments underscore the dataset’s difficulty. A top parser (HITS) detects segment boundaries well (98.6% F1), but its relation classification is more than 11 F1 percentages lower than on the standard PDTB. In addition, state‐of‐the‐art LLMs (OpenAI o4‐mini, Claude 3.7, LLaMA‐3.1) achieve at best 41% F1 on explicit relations and less than 9% F1 on implicit relations, revealing systematic errors in temporal and causal sense detection. The dataset can be accessed at: https://doi.org/10.57967/hf/6895. Code to reproduce our results is available at: https://github.com/chengzhangedu/ENG-DRB.

General domain Named Entity Recognition (NER) datasets like CoNLL-2003 mostly annotate coarse-grained location entities such as a country or a city. But many applications require identifying fine-grained locations from texts and mapping them precisely to geographic sites, e.g., a crossroad, an apartment building, or a grocery store. In this paper, we introduce a new dataset HarveyNER with fine-grained locations annotated in tweets. This dataset presents unique challenges and characterizes many complex and long location mentions in informal descriptions. We built strong baseline models using Curriculum Learning and experimented with different heuristic curricula to better recognize difficult location mentions. Experimental results show that the simple curricula can improve the system’s performance on hard cases and its overall performance, and outperform several other baseline systems. The dataset and the baseline models can be found at https://github.com/brickee/HarveyNER.