Anastasia Zhukova

Anastasia Zhukova
Georg-August-Universität Göttingen | GAUG · Institute of Computer Science

Master of Science

About

18
Publications
4,164
Reads
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74
Citations
Citations since 2017
18 Research Items
74 Citations
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Introduction
Doctoral researcher in applied NLP for automated media bias identification and domain adaptation of language models, e.g., for the process industry. Research areas: cross-document coreference resolution, information extraction, domain-specific recommender systems, domain adaptation of language models, semantic search, and user-driven development of NLP applications.

Publications

Publications (18)
Preprint
Full-text available
User experience (UX) is a part of human-computer interaction (HCI) research and focuses on increasing intuitiveness, transparency, simplicity, and trust for system users. Most of the UX research for machine learning (ML) or natural language processing (NLP) focuses on a data-driven methodology, i.e., it fails to focus on users' requirements, and en...
Preprint
Full-text available
In a world overwhelmed with news, determining which information comes from reliable sources or how neutral is the reported information in the news articles poses a challenge to news readers. In this paper, we propose a methodology for automatically identifying bias by commission, omission, and source selection (COSS) as a joint threefold objective,...
Conference Paper
Full-text available
Established cross-document coreference resolution (CDCR) datasets contain event-centric coreference chains of events and entities with identity relations. These datasets establish strict definitions of the coreference relations across related tests but typically ignore anaphora with more vague context-dependent loose coreference relations. In this...
Chapter
Full-text available
Datasets and methods for cross-document coreference resolution (CDCR) focus on events or entities with strict coreference relations. They lack, however, annotating and resolving coreference mentions with more abstract or loose relations that may occur when news articles report about controversial and polarized events. Bridging and loose coreference...
Preprint
Full-text available
Named entity recognition (NER) is an important task that aims to resolve universal categories of named entities, e.g., persons, locations, organizations, and times. Despite its common and viable use in many use cases, NER is barely applicable in domains where general categories are suboptimal, such as engineering or medicine. To facilitate NER of d...
Preprint
Full-text available
Media bias and its extreme form, fake news, can decisively affect public opinion. Especially when reporting on policy issues, slanted news coverage may strongly influence societal decisions, e.g., in democratic elections. Our paper makes three contributions to address this issue. First, we present a system for bias identification, which combines st...
Preprint
Full-text available
Datasets and methods for cross-document coreference resolution (CDCR) focus on events or entities with strict coreference relations. They lack, however, annotating and resolving coreference mentions with more abstract or loose relations that may occur when news articles report about controversial and polarized events. Bridging and loose coreference...
Preprint
Full-text available
Cross-document coreference resolution (CDCR) datasets, such as ECB+, contain manually annotated event-centric mentions of events and entities that form coreference chains with identity relations. ECB+ is a state-of-the-art CDCR dataset that focuses on the resolution of events and their descriptive attributes, i.e., actors, location, and date-time....
Conference Paper
Media bias and its extreme form, fake news, can decisively affect public opinion. Especially when reporting on policy issues, slanted news coverage may strongly influence societal decisions, e.g., in democratic elections. Our paper makes three contributions to address this issue. First, we present a system for bias identification, which combines st...
Preprint
Full-text available
Unsupervised concept identification through clustering, i.e., identification of semantically related words and phrases, is a common approach to identify contextual primitives employed in various use cases, e.g., text dimension reduction, i.e., replace words with the concepts to reduce the vocabulary size, summarization, and named entity resolution....
Preprint
Full-text available
News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, na...
Chapter
Full-text available
Unsupervised concept identification through clustering, i.e., identification of semantically related words and phrases, is a common approach to identify contextual primitives employed in various use cases, e.g., text dimension reduction, i.e., replace words with the concepts to reduce the vocabulary size, summarization, and named entity resolution....
Conference Paper
Full-text available
News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, na...
Conference Paper
Full-text available
Media bias can strongly impact the individual and public perception of news events. One difficult-to-detect, yet powerful form of slanted news coverage is bias by word choice and labeling (WCL). Bias by WCL can occur when journalists refer to the same concept , yet use different terms, which results in different sentiments being sparked in the read...
Chapter
Full-text available
Media bias, i.e., slanted news coverage, can strongly impact the public perception of topics reported in the news. While the analysis of media bias has recently gained attention in computer science, the automated methods and results tend to be simple when compared to approaches and results in the social sciences, where researchers have studied medi...
Preprint
Full-text available
Media bias, i.e., slanted news coverage, can strongly impact the public perception of topics reported in the news. While the analysis of media bias has recently gained attention in computer science, the automated methods and results tend to be simple when compared to approaches and results in the social sciences, where researchers have studied medi...

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