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bulk-chain
Nicolay Rusnachenko
(Framework)
github /
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📙 colab-notebook
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A lightweight, no-strings-attached Chain-of-Thought framework for your LLM, ensuring reliable results for bulk input requests stored in CSV / JSONL / sqlite.
It allows applying series of prompts formed into schema
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bulk-ner
Nicolay Rusnachenko
(Framework)
github /
twitter /
📙 colab-notebook
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A no-strings inference implementation framework Named Entity Recognition (NER) service of
wrapped AI models powered by AREkit and the related text-processing pipelines.
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Chinchunmei at WASSA 2024 Empathy and Personality Shared Task:
Boosting LLM’s Prediction with Role-play Augmentation and Contrastive
Reasoning Calibration
Tian Li,
Nicolay Rusnachenko,
Huizhi Liang
14th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA-2024)
Co-located with ACL 2024, 11-16 August
Bangkok, Thailand
(Oral Presentation)
(Contest)
paper /
video /
poster /
🤗 models /
codalab /
twitter
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We adopt two-stage method SFT (🟥) + inference (🟩) as contrastive reasoning calibration (CRC).
For the training, we enrich input samples: standard, role-play, contrastive.
We concatenate the article and task content togerther in input to train model predict all track results (1,2,3).
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Marking Medical Images with Natural Language Processing
Nicolay Rusnachenko
MergeXR Studio Limited, 10 Dallow Road, Luton, United Kingdom
9-10'th May 2024
(Promotion and Project Presentation)
project-overview /
bournemouth-research-post /
youtube-presentation /
slides
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Presented a multi-disciplinary research to transform the UK/global healthcare sector and allied
training using digital technologies derived from the creative industries sector.
In particular, the personal findings on Multimodal LLM-based system development
and research to be conducted has been demonstrated in a form of the demo setups.
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ARElight-0.24.0: Context Sampling of Large Text for Deep Learning RE
Nicolay Rusnachenko,
Huizhi Liang,
Maxim Kolomeets,
Lei Shi
46th European Conference on Information Retrieval, 24th-28th March, 2024
Glasgow, Scotland
(Demonstration Paper, Oral Presentation, Offline)
paper /
demo /
code /
poster /
old-landing-page
Old Pre-release ARElight-0.22.0 related materials:
presentation /
ml-trainings-youtube /
video[rus]
share card
AREkit-based application for a granular view onto sentiments
between entities for large document collections, including books, mass-media, Twitter/X, and more.
See our online demo
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Advances in Sentiment Analysis of the Large Mass-Media Documents
Nicolay Rusnachenko
Glasgow IR Research Group, Sir Alwyn Williams Building, Glasgow,
G12 8QN, 22 January 2024
Scotland, United Kingdom (UK)
(Oral Presentation)
event /
video /
paper /
slides
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Unlike to the similar talks in past,
in this one we overview the capabilities the most-recent instructive Large Language Models (LLM).
This list includes: Mistral, Mixtral-7B, Flan-T5, Microsoft-Phi2, LLama2, etc.
We also cover advances of LLM-fine tuning by exploiting Chain-of-Thought techniques
to get the most out of the LLM capabilities.
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Dialogue Agents with Literary Character Personality Traits
Junzhe Zhao,
Huizhi Liang,
Nicolay Rusnachenko
Proceedings of the The 22nd IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology October 26-29, 2023
Venice, Italy
(Oral Presentation, Online)
doi /
paper /
eprint /
code
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To enhance the engagement of chatbots, we need to imbue them with unique personalities and speech patterns.
We propose an automated system that uses character dialogues from literary works.
We used the Chinese classic, Dream of the Red Chamber.
Our system efficiently extracts dialogues and personality traits from the book, creates a personality map for each character, generates responses that reflect these traits.
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RuSentNE-2023: Named Entity Oriented Sentiment Analysis Task
Anton Golubev,
Nicolay Rusnachenko,
Natalia Loukachevitch
Dialogue-2023, Codalab platform, 2023
(Competitions)
bibtex /
doi /
preprint-arXiv /
paper /
code /
codalab /
info
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Participants are offered the task of extracting sentiments of three classes
(neg, pos, neutral) from news texts in relation to pre-marked entities such as
PERSON, ORG, PROFESSION, COUNTRY, NATIONALITY within a separate sentence.
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The Rivers Trust: The application of GNNs for river water quality sensor networks
Nicolay Rusnachenko,
Andrew Johnson,
Al-Amin Bashir Bugaje,
Lingyi Yang,
Tai-Ying Lee,
Mohammad Matin Saddiqi,
Mansi Mungee (PI)
The Catalyst (Newcastle University), Newcastle Upon Tyne, 21-24'th March, 2023
England, United Kingdom (UK)
(Oral Presentation)
youtube-presentation /
event-link
/ share card
Taking a facilitator Role in the
The Rivers Trust project.
The Rivers Trust company leads the
Catchment Systems Thinking Cooperative (CaSTCo) project,
which has been set up to provide a national framework for sharing water environment data
across a range of stakeholders.
To the best of our personal knowledge we were the first who experiment with the
Graph Neural Networks application (GNN)
for water quality assessment.
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Advances in Sentiment Analysis of the Large Mass-Media Documents
Nicolay Rusnachenko
Scalalable Research Group Seminars
Urban Sciences Building (Newcastle University), Newcastle Upon Tyne, 11'th March, 2023
England, United Kingdom (UK)
(Oral Presentation)
slides
/ share card
In this talk we cover the advances of machine-learning approaches in sentiment
analysis of large mass-media documents. Complements the past talk at Wolfson College (Oxford)
with the detailed description of long-input transformers, RLHF training overview.
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Paper Overview: ETC -- Encoding Long and Structured Inputs in Transformers
Nicolay Rusnachenko
The Alan Turing Institute, London, 3'rd March, 2023
England, United Kingdom (UK)
(Oral Presentation)
/
(Impromptu)
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(Skimming Session)
slides /
video
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In this talk we examine the limitations of BERT model from the input size perspective.
To address the shortcommings, authors propose a related position embeddings in
order to implement local-window based sparse attention. To attend the distant tokens,
authors propose a Global-Attention mechanism in addition to the sparsed one for the main input.
Another main contribution is input structuring in attention mechanism.
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Advances in Sentiment Analysis of the Large Mass-Media Documents
Nicolay Rusnachenko
OxfordXML, OxfordTalks
Wolfson College, University of Oxford, 10'th February, 2023
Oxford, England, United Kingdom (UK)
(Oral Presentation)
slides /
details /
awesome-repository /
organizer
/ share card
In this talk we cover the advances of machine-learning approaches in sentiment
analysis of large mass-media documents. We provide both evolution of the task over time
including a survey of task-oriented models starting from the conventional linear
classification approaches to the applications findings of the recently announced
ChatGPT model.
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Awesome Sentiment Attitude Extraction
Nicolay Rusnachenko
(Github Repository)
repository
/ share card
This is a curated list of works devoted to sentiment attitude extraction domain.
The latter considered as an area of studies, in which sentiment analysis
and relation extraction are inextricably linked
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Official RuSentRel-1.1 Leaderboard
Nicolay Rusnachenko
(Github Repository)
leaderboard
/ share card
This repository is an official results benchmark for automatic sentiment attitude
extraction task within RuSentRel-1.1
collection, for the following models:
conventional approaches (SVM, RandomForest, kNN, NB, Gradient Boosting),
CNN-based networks,
RNN-based networks,
Attentive models,
BERT-based language models.
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Advances in Sentiment Attitude Extraction Task
Nicolay Rusnachenko
Introduction to Humanistic Informatics course
Denmark, Odense, November 26'th, 2020
(Online Lecture)
video /
slides
/ share card
This lecture is devoted to the works that were done within the past 4 years,
which is related sentiment attitude extraction task.
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