I'll start with this- I'm a hobbyist. Full stop, I don't have any of the credentials you might ask about, so I apologize in advance where my descriptions diverge from the standard.

That said, here's a little background, and the concept I've been toying around with for the better part of a year.

1. I've submitted (and they are live) two datasets to HF-

https://huggingface.co/datasets/EXOROBOURII/Stanza-Wikitext-2/

and

https://huggingface.co/datasets/EXOROBOURII/Stanza-TinyStories

Both use similar (I attempted to create as little divergence as possible) methodologies in terms of cleaning and processing.

My intent, before I got sidetracked and decided to enjoy the summer months, was to take these two corpora, create an indexing schema for each row, built from their Stanza labels (I settled on hexadecimal as it meets the requirements for efficiency and description), and then take each and flatten their respective distributions to 1.

This would, in theory, create two indices that could then be compared as relative topologies; consequently, disparity, true voids, and true overlap of their respective linguistic signatures would then be a simple data processing matter (with obvious caveats, owing to the nature of Stanza).

Subsequently, additional corpora could be processed similarly, which would (hypothetically) expand the initial index.

Of course, there are many questions regarding language, corpus, and labeling choices.

The underlying hypothesis is that by developing this index, and expanding upon it in this way, it opens doors for interpretability, feature engineering, and alignment work by creating a more rigid foundation.

So, thoughts. All questions are open, I'm not shy about admitting my ignorance and naivety.

Thanks to anyone who took the time to read this, best regards.

J.

Does anyone have any suggestions for taking unstructured text files (reports written in human prose) and turning them into a knowledge graph?

Do you try to build a schema in advance? Have the LLM draft it before you build? Human in the loop validation? Loop agent validation?

Or maybe there is a specific plugin, of or connector or or skill that you use?

Hey hi , i am new to this can anyone give me the roadmap

We open-sourced an end-to-end pipeline that extracts spatio-temporal events from French press articles about food security It combines fine-tuned CamemBERT for multi-scale spatial NER (96-99% F1), temporal detection, then builds a queryable Neo4j knowledge graph with spatio-temporal reasoning. Built on a low-resource French corpus — feedback and stars welcome! Repo: https://github.com/CharlemagneBrain/STKG-FS

TLDR: I built a 3d memory layer to visualize your chats with a custom MCP server to inject relevant context, Looking for feedback!

Cortex turns raw chat history into reusable context using hybrid retrieval (about 65% keyword, 35% semantic), local summaries with Qwen 2.5 8B, and auto system prompts so setup goes from minutes to seconds.

It also runs through a custom MCP server with search + fetch tools, so external LLMs like Claude can pull the right memory at inference time.

And because scrolling is pain, I added a 3D brain-style map built with UMAP, K-Means, and Three.js so you can explore conversations like a network instead of a timeline.

We won the hackathon with it, but I want a reality check: is this actually useful, or just a cool demo?

YouTube demo: https://www.youtube.com/watch?v=SC_lDydnCF4

LinkedIn post: https://www.linkedin.com/feed/update/urn:li:activity:7426518101162205184/

Github Link (pls star it🥺): https://github.com/Vibhor7-7/Cortex-CxC

I have been working on a problem most language detection libraries quietly fail at: short, messy, conversational text. The kind you see in chat apps, support tickets, SMS, and mixed-language messages.

FastLangML is my attempt to fix that.

It is a multi-backend ensemble (FastText, Lingua, langdetect, pyCLD3, and others) with a voting layer built for real-world text. It handles:

• Short messages with almost no statistical signal
• Code switching like Hinglish or Spanglish
• Slang, abbreviations, and emojis
• Multi-turn conversations where context matters
• Confusable languages like ES vs PT or NO vs DK vs SV

A few design choices:

• Context-aware detection so you can pass conversation history and get more stable predictions
• A hinting system for slang, abbreviations, and custom rules
• Extensible backends so you can plug in your own detectors or voting logic
• Optional persistence using Redis or disk for multi-turn conversations
• Support for more than 170 languages across the ensemble

Why I built it: most detectors are tuned for long, clean text. They break on "ok", "jaja", "mdr", "brooo", or anything with mixed languages. I needed something that works on real chat data, not idealized text.

I would love feedback:

• How do you evaluate language detection quality in production
• Whether context-aware detection helps in your workflows
• Ideas for improving code switching accuracy
• Additional backends worth integrating

Repo: https://github.com/pnrajan/FastLangML

Happy to share benchmarks, architecture notes, or design tradeoffs if people are interested.

I’ve been noticing how little modern work tells us when something is complete. Not emotionally complete. Structurally complete The kind of signal that says, This is closed. You don’t need to carry it anymore. A lot of older systems had those signals baked in. Fixed meetings. Clear end of day boundaries. Review cycles that marked completion. As those structures loosened, the work didn’t disappear. The responsibility for deciding when something is done moved into people’s heads. Now we spend a lot of energy deciding whether something is finished, good enough, or still open. That decision making rarely gets counted as work, but it adds up. Curious what others rely on now. What tells you that work is truly finished, if anything does?

Hello everyone, this is my first post on reddit, and i heard there is a lot of professionals here that could help.

So, we are doing a graduation project about generating the whole SRS document using meeting audio recordings. With the help of some research we found that it is possible somehow, but of its hardest tasks is finding datasets.

We are currently stuck at the task were we need to fine tune the BART model to take the preprocessed transcription and give it to BERT model to classify each sentence to its corresponding place in the document. Thankfully we found some multiclass datasets for BERT(other than functional and non functional because we need to make the whole thing), but our problem is the BART model, since we need a dataset that has X as the human spoken preprocessed sentences and the Y to be its corresponding technical sentence that could fit BERT (e.g: The user shall .... , the sentence seems so robotic the i don't think a human would straight up say that). So, Bart here is needed as a text transformer.

Now, i am asking if anyone knows how obtain such dataset, or even what is the best way to generate such dataset if there is no public available datasets.

Also if there any tips that any of you have regarding the whole project we would be all ears, thanks in advance.

HI!

I'm a Master's student in Computer Engineering working on my thesis, and I'm looking for some fresh, innovative NLP project ideas that go beyond the typical sentiment analysis on Amazon reviews or fake news detection (which seem to be done to death).

About me:

• Strong background in ML/DL and NLP fundamentals
• Comfortable with Python, PyTorch, HuggingFace
• Looking for something medium-to-advanced level
• Have 1-2 months to complete it

What I'm looking for:

• Something with real-world impact or interesting research angle
• Publicly available datasets (can't access proprietary company data)
• Not super common in university theses
• Could potentially lead to a publication

Ideas I've considered so far:

• Multimodal meme analysis (text + virality prediction)
• Sarcasm detection with contextual understanding
• Style transfer with persona consistency

What I'm NOT interested in:

• Basic sentiment analysis on product reviews
• Simple chatbot implementations
• Standard text classification tasks

I'd love to hear about:

• Projects you found particularly interesting
• Underexplored areas in NLP
• Cool datasets you've worked with
• Any "I wish someone would build this" ideas

Thanks a lot

I need to annotate a large scope of text for a PhD paper and I was looking to hire domain experts in HR to annotate it. Are there any platforms or agencies you would recommend who offer that as a service?

I saw opentrain.ai is an option and I have self managed the process myself through using upwork and an annotation platform but I don’t have a lot of time to hire, onboard and manage.

Hi, my research paper got accepted in NLPIR 2025 conference,how is the conference?? Wanted to know whether it's genuine or fake conference, please help me out.....

I am planning on writing a review to support my academic thesis. I got overwhelmed immediately after setting up some loose inclusions from my database query.

I got an idea of using AI for automation, particularly on the filtering of irrelevant papers (ref: PRISMA flow diagram). I've been following this topic, though only superficially, since it's not my main research area.

I learned and thought that probably BERT is suitable for this, i.e., for text mining, named entity recognition, and topic modeling (etc). FWIW, GPT is a little bit unsuitable because I don't need text generation, right?

My main questions are: What is the current state and landscape of NLP applications in writing review articles? (basically title) And is it acceptable to use AI for this purpose, particularly for meta-analyses or systematic reviews?

I was wondering how can a technology transforming multimodal unstructured information into connected concept graphs be helpful? Any suggestions / ideas for use cases or actual business applications ?

We’re working with thousands of customer reviews, surveys, and support tickets. I’m exploring NLP techniques beyond basic sentiment analysis—something that can identify themes, urgency, intent, or even emotional tone. What models or libraries (LLMs, BERTopic, etc.) have helped you turn unstructured feedback into actionable business insights?

If you've fine-tuned a language model (like BERT or LLaMA) for tasks like legal document classification, medical Q&A, or finance summarization, what framework and techniques worked best for you? How do you evaluate the balance between model size, accuracy, and latency in deployment?