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Nicolay Rusnachenko

nicolay-r

https://nicolay-r.github.io/

AI & ML interests

Information Retrieval・Medical Multimodal NLP (🖼+📝) Research Fellow @BU_Research・software developer http://arekit.io・PhD in NLP

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Posts 23

Post

470

📢 This year I made decent amout of experiments on LLM reasoning capabilities in author opinion extraction.
However, they did not go further with:
↗️ annoation of other sources of opinion causes: entities, out-of-context object (None).
📏 evaluation of factual statements that support the extracted sentiment.

To address these limitations, so far we launch 🚀 RuOpinionNE-2024 competition on the Codalab platform:
📊 https://codalab.lisn.upsaclay.fr/competitions/20244

The competition is aimed at extraction of opinion tuples (see attached images) from texts written in Russian.
It proceeds the past RuSentNE-2023 codalab competition findings:
🔎 Past year competition: https://www.dialog-21.ru/media/5896/golubevaplusetal118.pdf
🔎 LLM reasoning 🧠: https://arxiv.org/abs/2404.12342

For those who interested to adopt Generative AI, the complete information about competition is below:
📊 RuOpinionNE-2024: https://codalab.lisn.upsaclay.fr/competitions/20244
🗒 Task description: https://codalab.lisn.upsaclay.fr/competitions/20244#learn_the_details-overview
🔔 To follow updates: https://t.me/RuOpinionNE2024
⏰ Stages Deadlines (might be extended)
📦 Submission details (bottom of the competition page)

🙋 For questions you can contact @nicolay-r : https://nicolay-r.github.io/
🧪 Most recent findings on LLM application: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark

Post

634

📢 The fast application of named entity recognition (NER) model towards vast amout of texts usually serves two major pitfalls:
🔴 Limitation of the input window size
🔴 Drastically slows down the downstream pipeline of the whole application

⭐ https://github.com/nicolay-r/bulk-ner

To address these problems, bulk-ner represent a no-string framework with the handy wrapping over any dynamically linked NER-ml model by providing:
☑️ Native long-input contexts handling.
☑️ Native support of batching (assuming that ML-model engine has the related support too)

To quick start, sharing the wrapper over DeepPavlov NER models.
With the application of such models you can play and bulk your data here:
📙 https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb
(You have to have your data in CSV / JSONL format)

Lastly, it is powered by AREkit pipelines, and therefore could be a part of the relation extraction and complex information retrieval systems:
💻 https://github.com/nicolay-r/AREkit
📄 https://openreview.net/forum?id=nRybAsJMUt

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