This work presents QQ, a metadata toolkit and browser explorer that compiles language metadata sources into a graph of language varieties, scripts, regions, identifiers, names, and relations, and exposes it through a Python API, a command-line interface, and a browser-based explorer.
Abstract
Multilingual NLP research increasingly involves hundreds or thousands of languages across different datasets. Managing, discovering, and reporting language metadata becomes a common hurdle at these scales. We present QQ, a metadata toolkit and browser explorer. QQ compiles language metadata sources into a graph of language varieties, scripts, regions, identifiers, names, and relations, and exposes it through a Python API, a command-line interface, and a browser-based explorer. Users can normalize identifiers, retrieve metadata, traverse relations, and discover which external resources contain a language. We demonstrate QQ on three workflows: an audit of the HuggingFace Hub, linking resources that use different identifier systems, and generating reproducible language-reporting tables. QQ supports FAIR-oriented metadata practices through versioning, open formats, and reusable interfaces.
PolyglotQL provides an extensible and modular architecture that aggregates, normalizes, and augments heterogeneous question–SPARQL pairs from established text-to-SPARQL datasets, highlighting the benefits of structured context in multilingual semantic parsing.
J. Pérez, Fabio Barth, Georg Rehm· International Conference on...· 0 citations
We present TextLens , a web-based platform for automated linguistic annotation designed to lower technical barriers for researchers in digital humanities, linguistics and translation studies. Hosted by the Dutch Language Institute (INT), TextLens allows users to upload and annotate corpora in a variety of formats (.txt...
Cynthia Van Hee, Jonas Doumen, Vincent Prins et al.· International Conference on...· 0 citations
This article explores asymmetric extractive semantic search, using the large language model (LLM) SBERT. It first examines traditional exact, keyword, and fuzzy match search algorithms, discussing their features and limitations. It then explores how semantic search leverages the capacity of an LLM to capture query inte...
TABERTA is presented, a unified retrieval framework for dataset discovery that adapts a bi-encoder language model to relational data by fine-tuning it on serialized representations of tables by fine-tuning the language encoder to diverse structural patterns using optimized strategies that serialize schema and table con...
Enas Khwaileh, L. Traeger, Albert Gatt et al.· International Conference on...· 0 citations
Collaborative Research Centers rely on FAIR-compliant, richly structured metadata, yet manual annotation is a major bottleneck. We implemented a search-augmented large language model (LLM) workflow within a local research data management system to pre-annotate biomedical entities, using human-in-the-loop verifica...
M. Watter, F. Engel, Aref Kalantari et al.· Bioinformatics Advances· 0 citations
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 24, 2026
