Comparative analyses of NLP research, such as identifying who is shaping a subfield, how a topic is shifting geographically, where method- ological gaps lie between research communi- ties, require institutional and topical metadata at a density and granularity the ACL Anthol- ogy does not itself provide. Large-scale re- sources offer partial support: OpenAlex pro- vides high-quality institutional metadata where it can match ACL papers (32% of our gold set), but the DOI dependency limits its cov- erage on older Anthology publications; and the S2ORC bulk release contains 52,013 ACL Anthology fulltexts but populates structured author–affiliation annotations on a small fraction of them in its current state. We present ANTHEM (ANTHology with Enriched Meta- data), a complementary resource that narrows this gap for NLP publications. ANTHEM links 117,386 ACL Anthology papers to Research Or- ganization Registry (ROR)-normalized author affiliations and Computer Science Ontology (CSO) topic labels via a multi-parser, LLM- based extraction pipeline that retains evidence spans for every decision, achieving F1hier of 0.760 on a 781-paper decade-stratified gold set, where parent–child ROR identifiers are treated as equivalent (e.g., a department and its parent university). On top of the corpus we release a benchmark of 99 structured comparison ques- tions across five categories, evaluated under a programmatic gold-set construction in which ANTHEM’s own metadata defines the evalu- ation ground truth. All data, code, and infras- tructure are released under CC-BY-SA-NC.

Fact-check retrieval plays a crucial role in combating misinformation by ensuring that claims are accurately matched with relevant fact-checks. In this work, we present a hybrid retrieval pipeline that integrates lexical and semantic retrieval models, leveraging their complementary strengths. We evaluate different retrieval and reranking strategies, demonstrating that hybrid ensembling consistently outperforms individual models, while reranking provides only marginal improvements.

Taking note of the current challenges of the peer review system, this paper inventories the research tasks for analysing and possibly automating parts of the reviewing process, like matching submissions with a reviewer’s domain of expertise. For each of these tasks we list their associated datasets, analysing their quality in terms of available documentation of creation and use. Building up on this, we give a set of recommendations to take into account when collecting and releasing data.