Social 📸 Photography

FIZ ISE Research GroupTeam ISE (Mary Ann Tan and Shufan Jiang) attending <a href="https://sigmoid.social/tags/ACL2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#ACL2025</a> in Vienna to present our paper at the Fifth Workshop on Scholarly Document Processing at ACL 2025Shufan Jiang, Mary Ann Tan, Harald Sack: MathD2: Towards Disambiguation of Mathematical Terms: <a href="https://aclanthology.org/2025.sdp-1.3/" rel="nofollow noopener" translate="no" target="_blank">https://aclanthology.org/2025.sdp-1.3/</a><a href="https://sigmoid.social/@shufan" class="u-url mention" rel="nofollow noopener" target="_blank">@shufan</a> <a href="https://wisskomm.social/@fiz_karlsruhe" class="u-url mention" rel="nofollow noopener" target="_blank">@fiz_karlsruhe</a> <a href="https://sigmoid.social/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://sigmoid.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://sigmoid.social/@lysander07" class="u-url mention" rel="nofollow noopener" target="_blank">@lysander07</a>

UKP Lab🤔 What is <a href="https://sigmoid.social/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> research 𝘳𝘦𝘢𝘭𝘭𝘺 about? We analyzed 29k+ papers to find out! 📚🔍📌 Our NLPContributions dataset, from the ACL Anthology, reveals what authors actually contribute—artifacts, insights, and more.📈 Trends show a swing back towards language & society. Curious where you fit in?🎁 Tools, data, and analysis await you:📄 Paper: <a href="https://arxiv.org/abs/2409.19505" rel="nofollow noopener" translate="no" target="_blank">https://arxiv.org/abs/2409.19505</a> 🌐Project: <a href="https://ukplab.github.io/acl25-nlp-contributions/" rel="nofollow noopener" translate="no" target="_blank">https://ukplab.github.io/acl25-nlp-contributions/</a> 💻 Code: <a href="https://github.com/UKPLab/acl25-nlp-contributions" rel="nofollow noopener" translate="no" target="_blank">https://github.com/UKPLab/acl25-nlp-contributions</a> 💾 Data: <a href="https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4678" rel="nofollow noopener" translate="no" target="_blank">https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4678</a>(1/🧵)

AIagent.at 🤖 AI, GenAI, AGI»Can AI really code? Study maps the roadblocks to autonomous software engineering« <a href="https://news.mit.edu/2025/can-ai-really-code-study-maps-roadblocks-to-autonomous-software-engineering-0716" rel="nofollow noopener" translate="no" target="_blank">https://news.mit.edu/2025/can-ai-really-code-study-maps-roadblocks-to-autonomous-software-engineering-0716</a> <a href="https://defcon.social/tags/AIagent" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIagent</a> <a href="https://defcon.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://defcon.social/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ML</a> <a href="https://defcon.social/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://defcon.social/tags/LLM" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLM</a> <a href="https://defcon.social/tags/GenAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#GenAI</a>

Alexandre DulaunoyVLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification.This paper presents VLAI, a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service.We ( <a href="https://fosstodon.org/@cedric" class="u-url mention" rel="nofollow noopener" target="_blank">@cedric</a> and I) decided to make a paper to better document how VLAI is implemented. We hope it will give other ideas and improvements in such model.<a href="https://infosec.exchange/tags/vulnerability" class="mention hashtag" rel="nofollow noopener" target="_blank">#vulnerability</a> <a href="https://infosec.exchange/tags/cybersecurity" class="mention hashtag" rel="nofollow noopener" target="_blank">#cybersecurity</a> <a href="https://infosec.exchange/tags/vulnerabilitymanagement" class="mention hashtag" rel="nofollow noopener" target="_blank">#vulnerabilitymanagement</a> <a href="https://infosec.exchange/tags/ai" class="mention hashtag" rel="nofollow noopener" target="_blank">#ai</a> <a href="https://infosec.exchange/tags/nlp" class="mention hashtag" rel="nofollow noopener" target="_blank">#nlp</a> <a href="https://infosec.exchange/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#opensource</a> <a href="https://social.circl.lu/@circl" class="u-url mention" rel="nofollow noopener" target="_blank">@circl</a> 🔗 <a href="https://arxiv.org/abs/2507.03607" rel="nofollow noopener" translate="no" target="_blank">https://arxiv.org/abs/2507.03607</a>

AaronHow big of a deal would it be if someone developed a language model (kind of like ChatGPT) which didn't hallucinate, didn't use prodigious amounts of electricity/water/compute/memory, which ran locally or on a distributed user mesh instead of corporate server farms, and which remembered and learned from what you say if you want it to? Something which was reliable and testable and even interpretable -- meaning you could pop the hood and see what it's really doing. Would you be inclined to use a system like this? Are there other things you'd still take issue with?<a href="https://techhub.social/tags/LLM" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLM</a> <a href="https://techhub.social/tags/ChatGPT" class="mention hashtag" rel="nofollow noopener" target="_blank">#ChatGPT</a> <a href="https://techhub.social/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://techhub.social/tags/NLU" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLU</a>

Leshem ChoshenThe Swedish National Archives shared 1.2m images of handwritten documents from 1600-1900+ In addition, they share a model for writing to text Such marvelous data opportunities, kudos 📈🤖🧠 <a href="https://medium.com/@linnea.karlberg.lundin/from-handwriting-to-searchable-text-usi[…]o-make-millions-of-historical-documents-accessible-3431b9c2673c" rel="nofollow noopener" translate="no" target="_blank">https://medium.com/@linnea.karlberg.lundin/from-handwriting-to-searchable-text-usi[…]o-make-millions-of-historical-documents-accessible-3431b9c2673c</a> Example: <a href="https://sok.riksarkivet.se/om-soktjansten/ai-transkriberade-arkiv" rel="nofollow noopener" translate="no" target="_blank">https://sok.riksarkivet.se/om-soktjansten/ai-transkriberade-arkiv</a><a href="https://huggingface.co/datasets/Riksarkivet/frihetstidens_utskottshandlingar_seg" rel="nofollow noopener" translate="no" target="_blank">https://huggingface.co/datasets/Riksarkivet/frihetstidens_utskottshandlingar_seg</a><a href="https://sigmoid.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://sigmoid.social/tags/history" class="mention hashtag" rel="nofollow noopener" target="_blank">#history</a> <a href="https://sigmoid.social/tags/swedish" class="mention hashtag" rel="nofollow noopener" target="_blank">#swedish</a> <a href="https://sigmoid.social/tags/nlp" class="mention hashtag" rel="nofollow noopener" target="_blank">#nlp</a> <a href="https://sigmoid.social/tags/data" class="mention hashtag" rel="nofollow noopener" target="_blank">#data</a> <a href="https://sigmoid.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a>

AIagent.at 🤖 AI, GenAI, AGI»Small Purchases, Big Risks: Shadow AI Use In Government« <a href="https://www.forrester.com/blogs/small-purchases-big-risks-shadow-ai-use-in-government/" rel="nofollow noopener" translate="no" target="_blank">https://www.forrester.com/blogs/small-purchases-big-risks-shadow-ai-use-in-government/</a> <a href="https://defcon.social/tags/AIagent" class="mention hashtag" rel="nofollow noopener" target="_blank">#AIagent</a> <a href="https://defcon.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://defcon.social/tags/ML" class="mention hashtag" rel="nofollow noopener" target="_blank">#ML</a> <a href="https://defcon.social/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://defcon.social/tags/LLM" class="mention hashtag" rel="nofollow noopener" target="_blank">#LLM</a> <a href="https://defcon.social/tags/GenAI" class="mention hashtag" rel="nofollow noopener" target="_blank">#GenAI</a>

WriterOfMinds (she)Small demo this month! I always wanted to be able to see Acuitas "thinking" and I've updated the semantic memory visualization to make that possible. <a href="https://writerofminds.blogspot.com/2025/06/acuitas-diary-85-june-2025.html" rel="nofollow noopener" translate="no" target="_blank">https://writerofminds.blogspot.com/2025/06/acuitas-diary-85-june-2025.html</a><a href="https://sigmoid.social/tags/ArtificialIntelligence" class="mention hashtag" rel="nofollow noopener" target="_blank">#ArtificialIntelligence</a> <a href="https://sigmoid.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://sigmoid.social/tags/chatbots" class="mention hashtag" rel="nofollow noopener" target="_blank">#chatbots</a> <a href="https://sigmoid.social/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a>

Mark Wyner Won’t Comply :vm:What I told Siri to say (“I hate this sap smear across our windshield”) vs what Siri said.<a href="https://mas.to/tags/VoiceRecognition" class="mention hashtag" rel="nofollow noopener" target="_blank">#VoiceRecognition</a> <a href="https://mas.to/tags/Siri" class="mention hashtag" rel="nofollow noopener" target="_blank">#Siri</a> <a href="https://mas.to/tags/Apple" class="mention hashtag" rel="nofollow noopener" target="_blank">#Apple</a> <a href="https://mas.to/tags/VoiceUX" class="mention hashtag" rel="nofollow noopener" target="_blank">#VoiceUX</a> <a href="https://mas.to/tags/NaturalLanguageDetection" class="mention hashtag" rel="nofollow noopener" target="_blank">#NaturalLanguageDetection</a> <a href="https://mas.to/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a>

AaronThe only actual machine learning the system uses, aside from its purely emergent ability to learn language from context, is in the parser, where I adjust the probability of matching certain patterns based on prior success in understanding the user.<a href="https://techhub.social/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://techhub.social/tags/NLU" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLU</a>

AaronI wrote this code in my spare time, over about a decade. I wrote the parser, the knowledge graph, the conversational engine, everything from scratch in pure python. It starts out literally knowing nothing except English grammar, and learns everything from interactions with the user. These tests I posted... It is literally learning the words from context as I use them. It had no idea what a dog or an apple or the color red is until I tell it things about them.<a href="https://techhub.social/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://techhub.social/tags/NLU" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLU</a>

AaronDo you see how the system answers questions it doesn't know? That's right. It says, "I don't know." Say no to hallucinations.<a href="https://techhub.social/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://techhub.social/tags/NLU" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLU</a>

AaronHere's another passing unit test. Again, this is stuff I wrote almost a decade ago, before LLMs were even invented. > The brown dog bites me. > The black dog barks at me. > The dog that bites me hates me. > Which dog hates me? < The brown dog. > Does the brown dog bite me? < Yes. > Does the brown dog hate me? < Yes. > Does the black dog hate me? > Does the dog that barks at me hate me? > Is the brown dog the dog that bites me? < Yes.<a href="https://techhub.social/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://techhub.social/tags/NLU" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLU</a>

AaronJust dug out some of my code from almost a decade ago, pre-LLM, and got it working again. Some examples of what it could do, taken from actual passing unit tests, are below. Lines starting with > are what the user says, and lines starting with < are what the system says. > The apple is red. > The worm is in the apple. > The apple is in the basket. > Where is the worm? < In the apple. > Where is the apple? < In the basket. > Is the worm in the apple? < Yes. > Is the apple in the basket? < Yes. > Is the worm in the basket? > What is in the apple? < The worm. > Is the apple red? < Yes.<a href="https://techhub.social/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://techhub.social/tags/NLU" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLU</a>

Seán FobbeQuestion for the digital humanities people:Is there any good <a href="https://fediscience.org/tags/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenSource</a> graphical tool for natural language processing that is both easy to use and performs a reasonable number of analyses? I am looking for something that the average lawyer or student with a couple of weeks training could operate.Thanks!<a href="https://fediscience.org/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://fediscience.org/tags/DigitalHumanities" class="mention hashtag" rel="nofollow noopener" target="_blank">#DigitalHumanities</a>

DiSC_uibkHave you ever struggled to find the best document retrieval model for your project? Or had to combine multiple frameworks just to get a basic <a href="https://social.uibk.ac.at/tags/InformationRetrieval" class="mention hashtag" rel="nofollow noopener" target="_blank">#InformationRetrieval</a> pipeline running?Check out Rankify, developed by Abdelrahman Abdallah from the <a href="https://social.uibk.ac.at/tags/DataScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#DataScience</a> Group <a href="https://social.uibk.ac.at/@uniinnsbruck" class="u-url mention" rel="nofollow noopener" target="_blank">@uniinnsbruck</a>, which provides an all-in-one retrieval, re-ranking, and retrieval-augmented generation toolkit: <a href="https://www.doi.org/10.48763/000013" rel="nofollow noopener" translate="no" target="_blank">https://www.doi.org/10.48763/000013</a><a href="https://social.uibk.ac.at/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://social.uibk.ac.at/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a> <a href="https://social.uibk.ac.at/tags/RAG" class="mention hashtag" rel="nofollow noopener" target="_blank">#RAG</a> <a href="https://social.uibk.ac.at/tags/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenSource</a> <a href="https://social.uibk.ac.at/tags/FOSS" class="mention hashtag" rel="nofollow noopener" target="_blank">#FOSS</a> <a href="https://social.uibk.ac.at/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://social.uibk.ac.at/tags/research" class="mention hashtag" rel="nofollow noopener" target="_blank">#research</a>

Maj - 🇨🇦If you name your kid "bunny winter" you're just going to have to expect named entity extraction tools are not going to catch them...<a href="https://cosocial.ca/tags/nlp" class="mention hashtag" rel="nofollow noopener" target="_blank">#nlp</a> <a href="https://cosocial.ca/tags/textcleanup" class="mention hashtag" rel="nofollow noopener" target="_blank">#textcleanup</a>

Harald SackLast week, we continued our <a href="https://sigmoid.social/tags/ISE2025" class="mention hashtag" rel="nofollow noopener" target="_blank">#ISE2025</a> lecture on distributional semantics with the introduction of neural language models (NLMs) and compared them to traditional statistical n-gram models. Benefits of NLMs: - Capturing Long-Range Dependencies - Computational and Statistical Tractability - Improved Generalisation - Higher Accuracy<a href="https://wisskomm.social/@fiz_karlsruhe" class="u-url mention" rel="nofollow noopener" target="_blank">@fiz_karlsruhe</a> <a href="https://sigmoid.social/@fizise" class="u-url mention" rel="nofollow noopener" target="_blank">@fizise</a> <a href="https://fedihum.org/@tabea" class="u-url mention" rel="nofollow noopener" target="_blank">@tabea</a> <a href="https://fedihum.org/@sourisnumerique" class="u-url mention" rel="nofollow noopener" target="_blank">@sourisnumerique</a> <a href="https://sigmoid.social/@enorouzi" class="u-url mention" rel="nofollow noopener" target="_blank">@enorouzi</a> <a href="https://sigmoid.social/tags/llms" class="mention hashtag" rel="nofollow noopener" target="_blank">#llms</a> <a href="https://sigmoid.social/tags/nlp" class="mention hashtag" rel="nofollow noopener" target="_blank">#nlp</a> <a href="https://sigmoid.social/tags/AI" class="mention hashtag" rel="nofollow noopener" target="_blank">#AI</a> <a href="https://sigmoid.social/tags/lecture" class="mention hashtag" rel="nofollow noopener" target="_blank">#lecture</a>

Seán Fobbe🔔 NEU 🔔 Alle 4566 Plenarprotokolle des Deutschen Bundestages von 1949 bis 2025 (Stichtag: 24. Mai) ab sofort im 'Corpus der Plenarprotokolle des Deutschen Bundestages' (CPP-BT) verfügbar.Auch Einzelreden mit Name, ID und Fraktion der Redner:in!🔶 Download 🔶 💾 Datensatz - <a href="https://doi.org/10.5281/zenodo.4542661" rel="nofollow noopener" translate="no" target="_blank">https://doi.org/10.5281/zenodo.4542661</a>📒 Codebook - <a href="https://zenodo.org/records/15462956/files/CPP-BT_2025-05-24_Codebook.pdf?download=11" rel="nofollow noopener" translate="no" target="_blank">https://zenodo.org/records/15462956/files/CPP-BT_2025-05-24_Codebook.pdf?download=11</a>💻 <a href="https://fediscience.org/tags/RStats" class="mention hashtag" rel="nofollow noopener" target="_blank">#RStats</a> Source Code - <a href="https://doi.org/10.5281/zenodo.4542665" rel="nofollow noopener" translate="no" target="_blank">https://doi.org/10.5281/zenodo.4542665</a>🔶 Features 🔶 + Insgesamt bis zu 35 Variablen in der CSV-Variante + Plenarprotokolle von der 1. Wahlperiode bis zur neuesten 21. Wahlperiode am Stichtag + Aufteilung in Einzelreden u.a. mit ID, Name, Fraktion und Amt der Redner:in (ab 18. Wahlperiode) + Aufteilung in Protokollbestandteile: Inhaltsverzeichnis, Sitzungsverlauf, Anlagen, Rednerliste (ab 18. Wahlperiode) + Fortlaufende Aktualisierung (Datensatz kann zusätzlich via Pipeline täglich aktualisiert werden) + Urheberrechtsfreiheit + Offene und plattformunabhängige Formate (PDF, TXT, CSV, XML, Parquet) + Linguistische Kennzahlen + Umfangreiches Codebook + Compilation Report, um den Erstellungs-Prozess zu erläutern + Dutzende Diagramme und Tabellen für alle Zwecke + Diagramme in einem für den Druck (PDF) und das Web (PNG) optimierten Format + Kryptographische Signaturen + Veröffentlichung des Source Codes (Open Source)<a href="https://a.gup.pe/u/rstats" class="u-url mention" rel="nofollow noopener" target="_blank">@rstats</a> <a href="https://a.gup.pe/u/politicalscience" class="u-url mention" rel="nofollow noopener" target="_blank">@politicalscience</a> <a href="https://a.gup.pe/u/histodons" class="u-url mention" rel="nofollow noopener" target="_blank">@histodons</a> <a href="https://fediscience.org/tags/OpenAccess" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenAccess</a> <a href="https://fediscience.org/tags/OpenSource" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenSource</a> <a href="https://fediscience.org/tags/OpenScience" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenScience</a> <a href="https://fediscience.org/tags/Parliament" class="mention hashtag" rel="nofollow noopener" target="_blank">#Parliament</a> <a href="https://fediscience.org/tags/Bundestag" class="mention hashtag" rel="nofollow noopener" target="_blank">#Bundestag</a> <a href="https://fediscience.org/tags/Plenarprotokoll" class="mention hashtag" rel="nofollow noopener" target="_blank">#Plenarprotokoll</a> <a href="https://fediscience.org/tags/Histodons" class="mention hashtag" rel="nofollow noopener" target="_blank">#Histodons</a> <a href="https://fediscience.org/tags/HistodonsDE" class="mention hashtag" rel="nofollow noopener" target="_blank">#HistodonsDE</a> <a href="https://fediscience.org/tags/NLP" class="mention hashtag" rel="nofollow noopener" target="_blank">#NLP</a> <a href="https://fediscience.org/tags/Dataviz" class="mention hashtag" rel="nofollow noopener" target="_blank">#Dataviz</a> <a href="https://fediscience.org/tags/Legislative" class="mention hashtag" rel="nofollow noopener" target="_blank">#Legislative</a> <a href="https://fediscience.org/tags/Debate" class="mention hashtag" rel="nofollow noopener" target="_blank">#Debate</a>

Christoph SchindlerWe present & discuss the new beta <a href="https://eduresearch.social/tags/EduTopics" class="mention hashtag" rel="nofollow noopener" target="_blank">#EduTopics</a> app ( <a href="https://dipf-lis.shinyapps.io/EduTopicsECER/" rel="nofollow noopener" translate="no" target="_blank">https://dipf-lis.shinyapps.io/EduTopicsECER/</a>) at the Workshops »Text Mining in der Erziehungswissenschaft« (<a href="https://eduresearch.social/@bbf_dipfberlin" class="u-url mention" rel="nofollow noopener" target="_blank">@bbf_dipfberlin</a> <a href="https://eduresearch.social/@dipf_aktuell" class="u-url mention" rel="nofollow noopener" target="_blank">@dipf_aktuell</a>) which allows you to interactively analyze more than 30,000 <a href="https://eduresearch.social/tags/ECER" class="mention hashtag" rel="nofollow noopener" target="_blank">#ECER</a> conference abstracts <a href="https://eduresearch.social/@FachportalPaedagogik" class="u-url mention" rel="nofollow noopener" target="_blank">@FachportalPaedagogik</a> <a href="https://eduresearch.social/tags/EduSci" class="mention hashtag" rel="nofollow noopener" target="_blank">#EduSci</a> <a href="https://eduresearch.social/tags/nlp" class="mention hashtag" rel="nofollow noopener" target="_blank">#nlp</a> <a href="https://eduresearch.social/tags/eduresearch" class="mention hashtag" rel="nofollow noopener" target="_blank">#eduresearch</a> <a href="https://eduresearch.social/@alexchrist" class="u-url mention" rel="nofollow noopener" target="_blank">@alexchrist</a> <a href="https://eduresearch.social/@j_roeschlein" class="u-url mention" rel="nofollow noopener" target="_blank">@j_roeschlein</a> <a href="https://eduresearch.social/tags/EERA_NW12" class="mention hashtag" rel="nofollow noopener" target="_blank">#EERA_NW12</a> <a href="https://eduresearch.social/tags/OpenResearchInEducation" class="mention hashtag" rel="nofollow noopener" target="_blank">#OpenResearchInEducation</a> <a href="https://eduresearch.social/tags/EduSci" class="mention hashtag" rel="nofollow noopener" target="_blank">#EduSci</a> <a href="https://eduresearch.social/tags/openscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#openscience</a> <a href="https://eduresearch.social/tags/TopicModeling" class="mention hashtag" rel="nofollow noopener" target="_blank">#TopicModeling</a>

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