Research Fellow
Dec. 2023 - Present | UK, England, Bournemouth

Developing:	MARK-MI platform, integrating NLP into radiology report evaluation using multi-step reasoning and fine-tuning techniques.
Tools:	LLangChain, LlamaIndex, PyTorch, Transformers.
Stack:	OHIF (ReactJS), NodeJS (utilities), ORTHANC (PACS), KeyCloak, FastAPI, SSL+certbot, NGINX.
Deploying:	Proof-of-Concept via Docker containers on cloud (DigitalOcean) with authorization (KeyCloak) and MLOps practices.
Leadership:	Supervised MSc and PhD-students in publications for: CLEF'25, BIBM'24, SemEval'26.

Research Assistant
Dec. 2022 - Dec. 2023 | UK, England, Newcastle upon Tyne

Contribute:	Built an NLP pipeline for embedding character personalities from 13K novel books (Project Gutenberg).
Tools:	Meta ParlAI, scikit-learn, PyTorch, Transformers, pandas, NLTK, seaborn, Google Colab.
Leadership:	Supervised MSc and PhD-students in SemEval23/24.
Publications:	ECIR'24, LOD'25, ACL/SemEval'23.

bulk-chain: No-string framework for Time-Efficient LLM Querying in Information Retrieval Tasks
Nicolay Rusnachenko
Research Colloquium (Winter Semester 2025/2026)
Online
(Online Presentation)

Deep Learning Frameworks and Their Evolution in Sentiment Analysis
Nicolay Rusnachenko
CSC8644 @ Newcastle University
Urban Sciences Building (Newcastle University), Newcastle Upon Tyne, 11'th March, 2023
England, United Kingdom (UK)
(Online Presentation)

IEEE CertifAIEd™ AuthorizedLead Assessor
Nicolay Rusnachenko
IEEE Standard Association (IEEE SA), Online
17-07-2025, 2 years
(Certificate)

NCL-BU at SemEval-2026 Task 3: Fine-tuning XLM-RoBERTa for Multilingual Dimensional Sentiment Regression
Tong Wu, Huizhi Liang Nicolay Rusnachenko
ACL 2026, San Diego, California, USA / SemEval-2026 Task 3 (DimABSA 2026)
(Contest)

We use bulk-chain for fast inference.

hdharpure at SemEval-2026 Task 3: BERT-Based Modeling and Prediction Behavior Analysis for Multilingual Valence–Arousal Scoring
Harshal Dharpure, Nicolay Rusnachenko
ACL 2026, San Diego, California, USA / SemEval-2026 Task 3 (DimABSA 2026)
(Contest)

MedEvi-NS at ArchEHR-QA 2026: Using clinical reasoning principles to improve zero-shot capabilities of Large Language Models in Evidence Alignment
Mengxuan Sun, Nicolay Rusnachenko
LREC-COLING 2026 / ArchEHR-QA 2026 Task 4 Competition
(Contest)

Using Decoder-Based Distillation for Enhancing Multilingual Clinical Case Report Summarization
Nicolay Rusnachenko, Xiaoxiao Liu, Jian Chang, Jian Jun Zhang
CLEF-2025 / BioASQ-2025
9-12 September 2025, Madrid, Spain
(Oral Presentation)

Proceeding of the earlier proposed MediExtract distil-tuning framework, which has been reforged to support decoder-based models (LLMs) with role-based prompt formation. The key contribution of the novel framework is its scalable potentials to other modalities.

Using Large Language Models for Evaluation of Radiological Textual Reports
Nicolay Rusnachenko, James Franklin, Theophilus Akudjedu, Neel Doshi, Michael Board, Jian Chang, Jian Jun Zhang
CASA-2025 and AniNex Workshop
Offline, 02 June - 04 June 2025, Strasbourg, France
(Poster Presentation)

RuOpinionNE-2024 -- extracting opinion tuples for the Russian Language
Natalia Loukachevitch, Lapanitsina Anna, Tkachenko Natalia, Mikhail Tikhomirov, Nicolay Rusnachenko
The second competitions organized by DIALOGUE association.
Online, 01 October - 15 November 2024
(Competitions)

RuOpinionNE-2024 proceeds the past year RuSentNE-2023 that go further with:
↗️ annotation of other sources of opinion causes: entities, out-of-context object (None), and 📏 evaluation of factual statements that support the extracted sentiment.

Evaluating Large Language Models in Understanding Atomic Concepts of Medical Image-Series Textual Narratives
Nicolay Rusnachenko
The 6'th Healthcare Summit hosted by JohnSnowLabs
NLP Healthcare Summit, Online, 1-2 April 2025
(Online Presentation)


In this talk we cover LLM evaluation concepts through the detailed overview of manual reporting directions in liver cancer imaging. We cover radiological imaging aspects that doctors tend to mention ✍️ and see how these aspects could be retrieved by LLM 🤖 by means of NLP and IR techniques. We provide recommendations on model scale choice and findings out of result analysis. This contributes to the similar studies presented at CASA-2025.

IEEE CertifAIEd™ Assessor Training
Nicolay Rusnachenko
IEEE Standard Association (IEEE SA), Online, 24-27 February 2025
(Training)

Enhancing Medical Dialogue Summarization: A MediExtract Distillation Framework
Xiaoxiao Liu, Mengqing Huang, Nicolay Rusnachenko, Julia Ive, Jian Chang, Jian Jun Zhang
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Lisbon, Portugal, 3-6 Dec. 2024
(Oral Presentation)


MediExtract Distillation Framework (MEDF) is a a hybrid teacher-student distillation process that leverages the power of LLMs in information capturing to enhance the performance of a smaller student model.

bulk-translate
Nicolay Rusnachenko
(Framework)

A tiny Python no-string package for performing translation of a massive stream of texts with native support of pre-annotated fixed-spans that are invariant for translator. Powered by AREkit-pipelines.

Reading Between The Lines: Using GenAI for Implicit Information Retrieval
Nicolay Rusnachenko
NCCA Research Seminar
13'th November 2024
Bournemouth University, Poole House, Lawrence Lecture Theatre, United Kingdom
(Oral Presentation)


In this talk, by saying reading in between the lines, we refer to performing such and Implicit IR that involves extraction of such information that is related to, i.e. ✍ ️author / 👩‍⚕️ patient / 🧑‍🦰 character etc.

Marking Medical Images with Natural Language Processing — The end-to-end system concept for training doctor practitioners
Nicolay Rusnachenko
BFX'24 Bournemouth University
30'th October 2024
Hilton Bournemouth, United Kingdom
(Project Presentation)

Presented an update on NLP advances in MMI-NLP project in end-to-end system for training novice practitioners. We showcase the importance on NLP application for processing medical narratives of liver-related MRI/CT scan series, such as one mentioned in "Series Descriptions" of the DICOM metadata. The end-to-end concept advances were demonstrated as well.

bulk-chain
Nicolay Rusnachenko
(Framework)
code / twitter / 📙 colab-notebook / providers / share
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

bulk-ner
Nicolay Rusnachenko
(Framework)

A no-strings framework for Named Entity Recognition (NER) in large textual collection with third-party AI models via very accessible API. Powered by AREkit-pipelines.

Reasoning with Large Language Models in Sentiment Analysis in Russian
Nicolay Rusnachenko
The 5'th summit hosted by JohnSnowLabs
NLPSummit, Online, 24-26 September 2024
(Online Presentation)

The talk is devoted to application of Large Language Models (LLMs) for retrieving implicit information from non-structured texts via reasoning the result sentiment label. To enhance model reasoning capabilities 🧠, we adopt Chain-of-Thought technique and explore its proper adaptation in Sentiment Analysis task.

Personality Profiling for Literary Character Dialogue Agents with Human Level Attributes
Nicolay Rusnachenko, Huizhi Liang
The 10th International Conference on Machine Learning, Optimization, and Data Science
September 22–25, 2024
Tuscany, Italy
(Oral Presentation)

This paper proposes the workflow of automatic profiling fictional character from literature novel books. The workflow is aimed at character personalities construction by solely rely on their comments in book: dialogue utterances and surrounding text, paragraphs.

hyy33 at WASSA 2024 Empathy and Personality Shared Task: Using the CombinedLoss and FGM for Enhancing BERT-based Models in Emotion and Empathy Prediction from Conversation Turns
Huiyu Yang, Liting Huang, 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)


We propose a loss function which combines: (1) structured contrastive loss and (2) Pearson loss. For exploiting the related function in BERT optimization process, we propose "Adversarial Training with Fast Gradient Method (FGM)". To improve model generalization ability we exploit "mix-up" as a data augmentation technique to mix inputs with the labels in specific range.

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)

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).

nicolay-r at SemEval-2024 Task 3.1: Reasoning Emotion Cause Supported by Emotion State with Chain-of-Thoughts
Nicolay Rusnachenko, Huizhi Liang
The 18th International Workshop on Semantic Evaluation
Co-located with NAACL 2024, June 16-21, 2024
Mexico City, Mexico
(Contest)

We fine-tune Flan-T5-base (250M) with the reforged THoR-ISA framework for Emotion Cause Extraction. Our final submission is 3'rd place by F1-proportional and 4-5'th by F1-strict which counts the emotion cause span borders. Our THOR-ECAC is publicly available.

NCL_NLP at SemEval-2024 Task 7: CoT-NumHG: A CoT-Based SFT Training Strategy with Large Language Models for Number-Focused Headline Generation
Junzhe Zhao, Yingxi Wang, Huizhi Liang, Nicolay Rusnachenko
The 18th International Workshop on Semantic Evaluation
Co-located with NAACL 2024, June 16-21, 2024
Mexico City, Mexico
(Contest)

We propose a two-phase SFT training strategy: (i) data-processing and (ii) model training. In (i) we combine CoT + knowledge-distillation concept (using GPT-3.5) + CoT steps for CoT-NumHG; this resource is then adopted in (ii) for the full-param SFT

Reasoning with Large Language Models in Sentiment Analysis in Russian / Рассуждение с помощью больших языковых моделей
Nicolay Rusnachenko
DataFest-2024, Online, 31 May 2024,
(Oral Presentation)

This is the Russian version of the talk devoted to application of Large Language Models (LLMs) for retrieving implicit information from non-structured texts via reasoning the result sentiment label. To enhance model reasoning capabilities 🧠, we adopt Chain-of-Thought technique and explore its proper adaptation in Sentiment Analysis task.

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)

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.

Large Language Models in Targeted Sentiment Analysis for Russian
Nicolay Rusnachenko, Anton Golubev, Natalia Loukachevitch
The 18th Lobachevskii Journal of Mathematics No.8 / LJoM-2024
Submitted on 16'th April 2024
(Journal Paper)


We explore LLMs reasoning capabilities in Targeted Sentiment Analysis task. In particular we assess LLM models in two modes: (i) zero-shot-learning (ZSL) (ii) fine-tuning with prompt and CoT THoR proposed at ACL-2023. The fine-tuned Flan-T5-xl outperforms the prior top submission at RuSentNE-2023.

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)
Old Pre-release ARElight-0.22.0 related materials:
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

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)

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.

Using Sentence Embedding Techniques for Enhancing Terms-of-Service Text Summarization
Harry Peach, Nicolay Rusnachenko, Mayank Baraskar, Huizhi Liang,
14th International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA-2023), December 14-15, 2023
Kochi, India
(Oral Presentation, Online)

ARElight: Context Sampling of Large Texts for Deep Learning Relation Extraction
Nicolay Rusnachenko, Huizhi Liang, Maxim Kolomeets, Lei Shi
Scalalable Research Group Seminars
Urban Sciences Building (Newcastle University), Newcastle Upon Tyne, 11'th March, 2023
England, United Kingdom (UK)
(Oral Presentation)

AREkit-based application for a granular view onto sentiments between entities in a mass-media texts written in Russian. Project presentation.

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)

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.

How well ChatGPT-3.5 understands semantic relations in mass-media texts? / Насколько хорошо ChatGPT-3.5 справляется с семантическими отношениями в тексте?
Nicolay Rusnachenko
(Personal Report)

This report represent a study of how well the annotated relations in NEREL could be predicted by ChatGPT-3.5.

nclu_team at SemEval-2023 Task 6C1 and 6C2: Attention-based Approaches for Large Court Judgement Prediction with Explanation
Nicolay Rusnachenko, Thanet Markchom, Huizhi Liang
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023) Association for Computational Linguistics (ACL)
Toronto, Canada
(Contest)

Legal_try at SemEval-2023 Task 6: Voting Heterogeneous Models for Entities identification in Legal Documents
Junzhe Zhao, Yingxi Wang, Nicolay Rusnachenko, Huizhi Liang
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023) Association for Computational Linguistics (ACL)
Toronto, Canada
(Contest)

Application of the transformer-based model for the named entity recognition task in legal texts.

RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts
Anton Golubev, Nicolay Rusnachenko, Natalia Loukachevitch
Dialogue-2023, Codalab platform, 2023
(Competitions)


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.

Pre-training LongT5 for Vietnamese Mass-Media Multi-document Summarization Task
Nicolay Rusnachenko, The Anh Le, Ngoc Diep Nguyen
Proceedings of the Artificial Intelligence and Natural Language Conference (AINL) /
Journal of Mathematical Sciences (Springer Nature)
20-22'th April, 2023
Yerevan, Armenia
(Oral Presentation, Online)

A first LongT5-based model pre-trained on a large amount of unlabeled Vietnamese texts and fine-tuned within the manually summarized texts from ViMS and VMDS and VLSP2022 collections

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)

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.

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)

In this talk we cover the advances of machine-learning approaches in sentiment analysis of large mass-media documents. Extends talk at Wolfson College (Oxford) with the detailed description of long-input transformers, RLHF training overview.

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) / (Skimming Session)

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.

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)

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.

AREnets: Tensorflow-based framework of attentive neural-network models for text classfication and relation extraction tasks
Nicolay Rusnachenko
(Github Repository)

Advances in Sentiment Attitude Extraction from Mass-Media Analytical Texts in Russian
Nicolay Rusnachenko
School of Informatics, Newcastle University, 9'th December, 2022
Newcastle Upon Tyne, England, United Kingdom (UK)
(Oral Presentation)

Introduction to Natural Language Processing for Reputation Tracking
Nicolay Rusnachenko
Marketing Trends and AI (DIFC Innovation HUB, Open Stage, 17'th August, 2022
(Presentation)

AREkit -- Attitude and Relation Extraction Toolkit
Nicolay Rusnachenko
(Framework)


Document level Attitude and Relation Extraction toolkit (AREkit) for sampling mass-media news into datasets for your ML-model training and evaluation

ARElight -- application for processing and extracting sentiment attutides from large mass-media news
Nicolay Rusnachenko
DataFest-2022 3.0, Online, 18'th June, 2022
(Presentation)


AREkit-based application for a granular view onto sentiments between entities in a mass-media texts written in Russian. Project presentation.

Research Assistant
Moscow State University · Indirect Contract
Jun 2022 - Nov 2022 · 6 mos
Moscow, Moscow City, Russia · Remote

Contribution:

Perform Aspect-Level Sentiment Analysis competition organizer RuSentNE-2023 dataset

Model, Methods and Software Toolset for Sentiment Attitude Extraction Task via Frame-based Knowledge-Base
Nicolay Rusnachenko
Bauman Moscow State Technical University, Moscow, 28'th April, 2022
Published by High Attestation Comission
(Thesis Defence)

We propose distant-supervision approach for mass-media annotation, based on RuSentiFrames knowledge-base. We built and propose RuAttitudes collection for Sentiment Attitude Extraction task and adopt it for neural-networks training process, including BERT-based models.

Research Assistant
Nov 2017 - Dec 2017 · 2 mos | Charles Sturt University · Remote

Contribution:	Search-Logs Analysis for medical data
Published paper:	Search Interfaces for Biomedical Searching (CHIIR 2022)

Search Interfaces for Biomedical Searching: How do Gaze, User Perception, Search Behaviour and Search Performance Relate?
Ying-Hsang Liu, Paul Thomas, Tom Gedeon, Nicolay Rusnachenko
ACM SIGIR Conference on Human Information Interaction and Retrieval, March 2022
Published by ACM
** Personal contribution were related to evaluation section and funded by Charles-Sturt-University, Australia (November 2017). See report for greater details.
(Online Presentation)

For Biomedical domain. We investigate: (1) which search interface elements searchers are look at when searching for docs to answer complex questions, and (2) relationship between individual differences and the interface elements which users are looked at.

Language Models Application in Sentiment Attitude Extraction Task
Nicolay Rusnachenko
Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS), vol.33
Russia, Moscow, August 9'th, 2021
(Journal Paper)

This lecture is devoted to the works that were done within last 4 years, which is related sentiment attitude extraction task.

Advances in Sentiment Attitude Extraction Task
Nicolay Rusnachenko
Introduction to Humanistic Informatics course
Denmark, Odense, November 26'th, 2020
(Online Lecture)

This lecture is devoted to the works that were done within the past 4 years, which is related sentiment attitude extraction task.

10-th Lisbon Machine Learning Summer School
Portugal, Lisbon, July 21-29, 2020
(Summer School)

Attention-Based Neural Networks for Sentiment Attitude Extraction using Distant Supervision
Nicolay Rusnachenko, Natalia Loukachevitch
The 10th International Conference on Web Intelligence, Mining and Semantics (WIMS 2020), June 30-July 3 (arXiv:2006.13730)
France, Biarritz, 2020
(Oral Presentation)

Application of Distant Supervision in model training process results in a weight distribution biasing: frames in between subject and object of attitude got more weight values; the latter reflects the pattern of frame-based approach, utilized in RuAttitudes collection development.

Studying Attention Models in Sentiment Attitude Extraction Task
Nicolay Rusnachenko, Natalia Loukachevitch
Proceedings of the 25th International Conference on Natural Language and Information Systems NLDB 2020. Lecture Notes in Computer Science, vol 12089. Springer, Cham (arXiv:2006.11605)
Germany, Saarbrücken, 2020
(Oral Presentation)

Sentiment Frames for Attitude Extraction in Russian
Natalia Loukachevitch, Nicolay Rusnachenko
Proceedings of International Conference on Computational Linguistics and Intellectual Technologies Dialogue-2020 (arXiv:2006.10973)
Russia, Moscow, 2020
(Oral Presentation)

Provides a description of the developed RuSentiFrames lexicon with the related application for attitude extraction

Automatic Sentiment Attitude Extraction via Neural Networks
Natalia Loukachevitch, Nicolay Rusnachenko
Conversations Conference
Moscow, Loft Hall, 26'th November, 2019
(Oral Presentation by supervisor)

Automatic Extraction of Implicit Attitudes From Texts
Natalia Loukachevitch, Karnaukhova V.A., Nicolay Rusnachenko
Tatarstan Academy of Sciences, 2018
(Plenary)

Pages: 169-179. The paper describes the task of automatic extraction of attitudes between the subjects mentioned in the text, as well as their connection with the implicit expression of the author's attitude to these subjects. A RuSentiFrames vocabulary is presented, in which the basic attitudes associated of Russian predicate words are described.

Introduction into Recurrent Neural Networks and Convolutional Neural Networks Moscow, Bauman Moscow State Technical University (BMSTU), October, 2019
Nicolay Rusnachenko
(Presentation)
cnn-example
Lecture extra materials (English):

Distant Supervision for Sentiment Attitude Extraction
Nicolay Rusnachenko, Natalia Loukachevitch, Elena Tutubalina
First Athens Natural Language Processing Summer School
Greece, Athens, September 18-26, 2019
(Poster Presentation)

Distant Supervision for Sentiment Attitude Extraction
Nicolay Rusnachenko, Natalia Loukachevitch, Elena Tutubalina
Proceedings of Recent Advances in Natural Language Processing Conference
Bulgaria, Varna, 2019
(Poster Presentation)

First time application of automatic attitudes extraction from raw news, based on news title presentation simplicity.

Neural Network Approach for Extracting Aggregated Opinions from Analytical Articles
Nicolay Rusnachenko, Natalia Loukachevitch
International Conference on Data Analytics and Management in Data Intensive Domains, Springer, 2018

Utilizing a set of sentence level attitudes with related metrics to perform a sentiment prediction. As for input of neural network based models, in prior works all the models deals with an attitudes limited by a single sentence.

Extracting Sentiment Attitudes from Analytical Texts via Piecewise Convolutional Neural Network
Nicolay Rusnachenko, Natalia Loukachevitch
DAMDID-2018, CEUR Workshop Proceedings (ceur-ws.org)
Russia, Moscow (MSU), 2018
(Oral Presentation), (Poster Presentation)

An application of CNN based architecture (adapted for relation extraction) towards sentiment attitudes extraction task.

Using Convolutional Neural Networks for Sentiment Attitude Extraction from Analytical Texts
Nicolay Rusnachenko, Natalia Loukachevitch
EPiC Series in Language and Linguistics 4, 1-10, 2019

An application of CNN based architecture (adapted for relation extraction) towards sentiment attitudes extraction task. Presented at Third Workshop "Computational linguistics and language science" (CCLS-2018), HSE, Moscow.

Extracting Sentiment Attitudes from Analytical Texts
Natalia Loukachevitch, Nicolay Rusnachenko
Proceedings of International Conference on Computational Linguistics and Intellectual Technologies Dialogue-2018 (arXiv:1808.08932)
Russia, Moscow, 2018
(Oral Presentation)

The first approach, i.e introduction of sentiment attitudes extraction task. Includes developed dataset with related experiments.

Sentiment Attitudes and Their Extraction from Analytical Texts
Nicolay Rusnachenko, Natalia Loukachevitch
International Conference on Text, Speech, and Dialogue (TSD)
Czech Republic, Brno, 2018
(Oral Presentation)

Introduction of sentiment attitudes extraction task. Includes developed RuSentRel with related experiments.

Methods of Lexicon Integration with Machine Learning for Sentiment Analysis System
Nicolay Rusnachenko, Natalia Loukachevitch
Artificial Intelligence and decision making (AIDM) journal, ISSN 2071-8594, 2017
(Best Student Paper Award at DIALOGUE-2016),
(Poster Presentation)
Provides details on lexicon development using twitter messages (related works [pmi], [dev], [sota]). Sentiment classification of user reviews using SVM. Master degree paper.

Extracting Sentiment Attitudes from Analytical Texts via Piecewise Convolutional Neural Network
Russia, Kazan, Kazan Federal University, 2018
(Poster Presentation)
poster / paper / certificate / share

Methods of Lexicon Integration with Machine Learning for Sentiment Analysis System
Russia, Saratov, SGU, 2016
Russia, Yekaterinburg, 2017
(Poster Presentation)

.NET Software Developer
May 2016 - Dec. 2021 | Russia, Moscow

Organization:	CJSC NORSI-TRANS
Contribute:	Developed surveillance analytics software in .NET/C#, including backend logic, UI components, and data-visualization modules for large operational datasets.
Stack:	.NET/C# (backend and UI), DevExpress Controls, MongoDB, Python (backend automation), XPO-Objects.
Analytics:	Built data visualization components for rendering large volumes of surveillance data via DevExpress Charts: interactive tables, timelines, and graphs.