Dr. Nicolay Rusnachenko

My name is Nicolay Runsnachenko, and I am post-doc Research Fellow at Bournemoth University, ex. Research Assistant at Newcastle University, UK. I defend my PhD in Natural Language Processing. I am majoring in Multimodal [image + text] Information Retrieval (IR) with Language Models from large documents of any kind: mass-media articles, news, literature books. I contribute in advances of large document processing in such fields as: Sentiment Analysis, Text Summarization, Dialogue Assistants. At present I am doing studies in medical domain by developing Multimodal [MRI-scans + reports] dialogue assistant and contribute to NHS advances.

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parkrun-code / parkrun-stat

Interested in sentiment analysis, machine learning, relation extraction. Representative papers are highlighted.

Paper Presentation Projects Summer Schools

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.

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.

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

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.

This report represent a study of how well the annotated relations in NEREL collection are aligned with the ChatGPT-3.5 knowledge.

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

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.

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

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.

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.

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.

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.

About Tensorflow-based framework which lists attentive implementation of the conventional neural network models (CNN, RNN-based), applicable for Relation Extraction classification tasks as well as API for custom model implementation

As a task, Sentiment Attitude Extraction is devoted to extraction of the sentiment connections from subjects towards objects mentioned in texts, usually analytical articles. This task has been originally proposed and becomes a part of the studies in RuSentRel dataset, in which texts are written in Russian.

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

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

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 language models.

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.

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

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.

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

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

Participant of summer school.

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.

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

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.

Participant of summer school.

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

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.

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

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.

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

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

Provides details on lexicon development using twitter messages (related works [pmi], [dev], [sota]). Sentiment classification of user reviews using SVM. Master degree paper.

This guy shares a great template!