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Computational Models of Argument Structure and Argument Quality for Understanding Misinformation
With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that can find checkworthy information, detect fallacious argumentation of online content, retrieve relevant evidence from authoritative sources and analyze the veracity of claims given the retrieved evidence. The robustness and applicability of these systems depend on the availability of annotated resources to train machine learning models in a supervised fashion, as well as machine learning models that capture patterns beyond domain-specific lexical clues or genre-specific stylistic insights. In this thesis, we investigate the role of models for argument structure and argument quality in improving tasks relevant to fact-checking and furthering our understanding of misinformation and disinformation. We contribute to argumentation mining, misinformation detection, and fact-checking by releasing multiple annotated datasets, developing unified models across datasets and task formulations, and analyzing the vulnerabilities of such models in adversarial settings.
We start by studying the argument structure's role in two downstream tasks related to fact-checking. As it is essential to differentiate factual knowledge from opinionated text, we develop a model for detecting the type of news articles (factual or opinionated) using highly transferable argumentation-based features. We also show the potential of argumentation features to predict the checkworthiness of information in news articles and provide the first multi-layer annotated corpus for argumentation and fact-checking.
We then study qualitative aspects of arguments through models for fallacy recognition. To understand the reasoning behind checkworthiness and the relation of argumentative fallacies to fake content, we develop an annotation scheme of fallacies in fact-checked content and investigate avenues for automating the detection of such fallacies considering single- and multi-dataset training. Using instruction-based prompting, we introduce a unified model for recognizing twenty-eight fallacies across five fallacy datasets. We also use this model to explain the checkworthiness of statements in two domains.
Next, we show our models for end-to-end fact-checking of statements that include finding the relevant evidence document and sentence from a collection of documents and then predicting the veracity of the given statements using the retrieved evidence. We also analyze the robustness of end-to-end fact extraction and verification by generating adversarial statements and addressing areas for improvements for models under adversarial attacks. Finally, we show that evidence-based verification is essential for fine-grained claim verification by modeling the human-provided justifications with the gold veracity labels
Workshop Proceedings of the 12th edition of the KONVENS conference
The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut fΓΌr Informationswissenschaft und Sprachtechnologie of UniversitΓ€t Hildesheim: it has long been one of the instituteβs research topics, and it has received even more attention over the last few years
Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΡ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ ΠΆΠΈΠ·Π½Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠΊΠ»Π° ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈ ΡΠΎΠ²ΠΌΠ΅ΡΡΠΈΠΌΡΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ ΡΠΈΡΡΠ΅ΠΌ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ
Π ΠΈΠ·Π΄Π°Π½ΠΈΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΎ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ ΡΠ΅ΠΊΡΡΠ΅ΠΉ Π²Π΅ΡΡΠΈΠΈ ΠΎΡΠΊΡΡΡΠΎΠΉ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΎΠ½ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΈ ΡΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΠΈΠΈ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈ ΡΠΎΠ²ΠΌΠ΅ΡΡΠΈΠΌΡΡ
Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΡ
ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ (Π’Π΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ OSTIS). ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΡΡΠ°Π½Π΄Π°ΡΡΠΈΠ·Π°ΡΠΈΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΡΠ°Π½Π΄Π°ΡΡΠΈΠ·Π°ΡΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈ
ΡΡΠ΅Π΄ΡΡΠ² ΠΈΡ
ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΡΡΠΎ ΡΠ²Π»ΡΠ΅ΡΡΡ Π²Π°ΠΆΠ½Π΅ΠΉΡΠΈΠΌ ΡΠ°ΠΊΡΠΎΡΠΎΠΌ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠΈΠΌ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΡΡ ΡΠΎΠ²ΠΌΠ΅ΡΡΠΈΠΌΠΎΡΡΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΈ ΠΈΡ
ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠΎΠ², ΡΡΠΎ
ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΡΡΡΠ΄ΠΎΠ΅ΠΌΠΊΠΎΡΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ°ΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ.
ΠΠ½ΠΈΠ³Π° ΠΏΡΠ΅Π΄Π½Π°Π·Π½Π°ΡΠ΅Π½Π° Π²ΡΠ΅ΠΌ, ΠΊΡΠΎ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΡΠ΅ΡΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΠΌΠΈ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ°, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠ°ΠΌ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΈ ΠΈΠ½ΠΆΠ΅Π½Π΅ΡΠΈΠΈ Π·Π½Π°Π½ΠΈΠΉ. ΠΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π° ΡΡΡΠ΄Π΅Π½ΡΠ°ΠΌΠΈ, ΠΌΠ°Π³ΠΈΡΡΡΠ°Π½ΡΠ°ΠΌΠΈ ΠΈ Π°ΡΠΏΠΈΡΠ°Π½ΡΠ°ΠΌΠΈ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΡΡΠΈ Β«ΠΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΒ».
Π’Π°Π±Π». 8. ΠΠ». 223. ΠΠΈΠ±Π»ΠΈΠΎΠ³Ρ.: 665 Π½Π°Π·Π²
Central and Eastern European Literary Theory and the West
The twentieth century saw intensive intellectual exchange between Eastern and Central Europe and the West. Yet political and linguistic obstacles meant that many important trends in East and Central European thought and knowledge hardly registered in Western Europe and the US. This book uncovers the hidden westward movements of Eastern European literary theory and its influence on Western scholarship
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