scholarly journals Beyond facts – a survey and conceptualisation of claims in online discourse analysis

Semantic Web ◽  
2022 ◽  
pp. 1-35
Author(s):  
Katarina Boland ◽  
Pavlos Fafalios ◽  
Andon Tchechmedjiev ◽  
Stefan Dietze ◽  
Konstantin Todorov
Keyword(s):  
Discourse Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
Factual Knowledge ◽  
Online Discourse ◽  
Knowledge Based ◽  
Research Areas ◽  
Argumentation Mining ◽  
Knowledge Base Construction ◽  
Fact Checking

Analyzing statements of facts and claims in online discourse is subject of a multitude of research areas. Methods from natural language processing and computational linguistics help investigate issues such as the spread of biased narratives and falsehoods on the Web. Related tasks include fact-checking, stance detection and argumentation mining. Knowledge-based approaches, in particular works in knowledge base construction and augmentation, are concerned with mining, verifying and representing factual knowledge. While all these fields are concerned with strongly related notions, such as claims, facts and evidence, terminology and conceptualisations used across and within communities vary heavily, making it hard to assess commonalities and relations of related works and how research in one field may contribute to address problems in another. We survey the state-of-the-art from a range of fields in this interdisciplinary area across a range of research tasks. We assess varying definitions and propose a conceptual model – Open Claims – for claims and related notions that takes into consideration their inherent complexity, distinguishing between their meaning, linguistic representation and context. We also introduce an implementation of this model by using established vocabularies and discuss applications across various tasks related to online discourse analysis.

Biomedical Computational Linguistics and Natural Language Processing

2021 ◽  
Author(s):  
Kevin Bretonnel Cohen
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Scientific Journal ◽  
The United States ◽  
Russian Language ◽  
Research Areas ◽  
Semantic Classes ◽  
Genomic Assays

Computational linguistics has its origins in the post-Second World War research on translation of Russian-language scientific journal articles in the United States. Today, biomedical natural language processing treats clinical data, the scientific literature, and social media, with use cases ranging from studying adverse effects of drugs to interpreting high-throughput genomic assays (Névéol and Zweigenbaum 2018). Many of the most prominent research areas in the field involve extracting information from text and normalizing it to enormous databases of domain-relevant semantic classes, such as genes, diseases, and biological processes. Moving forward, the field is expected to play a significant role in understanding reproducibility in natural language processing.

Using NLP for Fact Checking: A Survey

Designs ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 42
Author(s):  
Eric Lazarski ◽  
Mahmood Al-Khassaweneh ◽  
Cynthia Howard
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computer Science ◽  
Language Processing ◽  
The Internet ◽  
Fake News ◽  
Fact Checking ◽  
The Many ◽  
Human Powered ◽  
The Web

In recent years, disinformation and “fake news” have been spreading throughout the internet at rates never seen before. This has created the need for fact-checking organizations, groups that seek out claims and comment on their veracity, to spawn worldwide to stem the tide of misinformation. However, even with the many human-powered fact-checking organizations that are currently in operation, disinformation continues to run rampant throughout the Web, and the existing organizations are unable to keep up. This paper discusses in detail recent advances in computer science to use natural language processing to automate fact checking. It follows the entire process of automated fact checking using natural language processing, from detecting claims to fact checking to outputting results. In summary, automated fact checking works well in some cases, though generalized fact checking still needs improvement prior to widespread use.

The Verbmobil prototype system – a software engineering perspective

1999 ◽  
Vol 5 (1) ◽  
pp. 95-112 ◽  
Author(s):  
THOMAS BUB ◽  
JOHANNES SCHWINN
Keyword(s):  
Software Engineering ◽  
Computational Linguistics ◽  
Language Processing ◽  
System Integration ◽  
Evolutionary Process ◽  
Prototype System ◽  
Functional Architecture ◽  
Seamless Integration ◽  
Extensible Software ◽  
Transfer Problems

Verbmobil represents a new generation of speech-to-speech translation systems in which spontaneously spoken language, speaker independence and adaptability as well as the combination of deep and shallow approaches to the analysis and transfer problems are the main features. The project brought together researchers from the fields of signal processing, computational linguistics and artificial intelligence. Verbmobil goes beyond the state-of-the-art in each of these areas, but its main achievement is the seamless integration of them. The first project phase (1993–1996) has been followed up by the second project phase (1997–2000), which aims at applying the results to further languages and at integrating innovative telecooperation techniques. Quite apart from the speech and language processing issues, the size and complexity of the project represent an extreme challenge on the areas of project management and software engineering:[bull ] 50 researchers from 29 organizations at different sites in different countries are involved in the software development process,[bull ] to reuse existing software, hardware, knowledge and experience, only a few technical restrictions could be given to the partners.In this article we describe the Verbmobil prototype system from a software-engineering perspective. We discuss:[bull ] the modularized functional architecture,[bull ] the flexible and extensible software architecture which reflects that functional architecture,[bull ] the evolutionary process of system integration,[bull ] the communication-based organizational structure of the project,[bull ] the evaluation of the system operational by the end of the first project phase.

scholarly journals Evolution of Semantic Similarity—A Survey

2021 ◽  
Vol 54 (2) ◽  
pp. 1-37
Author(s):  
Dhivya Chandrasekaran ◽  
Vijay Mago
Keyword(s):  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Hybrid Methods ◽  
Research Work ◽  
Similarity Measures ◽  
Text Data ◽  
Knowledge Based ◽  
Open Research ◽  
Research Problems

Estimating the semantic similarity between text data is one of the challenging and open research problems in the field of Natural Language Processing (NLP). The versatility of natural language makes it difficult to define rule-based methods for determining semantic similarity measures. To address this issue, various semantic similarity methods have been proposed over the years. This survey article traces the evolution of such methods beginning from traditional NLP techniques such as kernel-based methods to the most recent research work on transformer-based models, categorizing them based on their underlying principles as knowledge-based, corpus-based, deep neural network–based methods, and hybrid methods. Discussing the strengths and weaknesses of each method, this survey provides a comprehensive view of existing systems in place for new researchers to experiment and develop innovative ideas to address the issue of semantic similarity.

Knowledge-Based System supported by Chatbot to assist the Risk Classification Process of Patients in Hospital Emergency care (Preprint)

2021 ◽  
Author(s):  
Marciane Mueller ◽  
Rejane Frozza ◽  
Liane Mählmann Kipper ◽  
Ana Carolina Kessler
Keyword(s):  
Medical Care ◽  
Knowledge Base ◽  
Language Processing ◽  
Emergency Care ◽  
Risk Classification ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Hospital Emergency ◽  
Knowledge Based System ◽  
Knowledge Based

BACKGROUND This article presents the modeling and development of a Knowledge Based System, supported by the use of a virtual conversational agent called Dóris. Using natural language processing resources, Dóris collects the clinical data of patients in care in the context of urgency and hospital emergency. OBJECTIVE The main objective is to validate the use of virtual conversational agents to properly and accurately collect the data necessary to perform the evaluation flowcharts used to classify the degree of urgency of patients and determine the priority for medical care. METHODS The agent's knowledge base was modeled using the rules provided for in the evaluation flowcharts comprised by the Manchester Triage System. It also allows the establishment of a simple, objective and complete communication, through dialogues to assess signs and symptoms that obey the criteria established by a standardized, validated and internationally recognized system. RESULTS Thus, in addition to verifying the applicability of Artificial Intelligence techniques in a complex domain of health care, a tool is presented that helps not only in the perspective of improving organizational processes, but also in improving human relationships, bringing professionals and patients closer. The system's knowledge base was modeled on the IBM Watson platform. CONCLUSIONS The results obtained from simulations carried out by the human specialist allowed us to verify that a knowledge-based system supported by a virtual conversational agent is feasible for the domain of risk classification and priority determination of medical care for patients in the context of emergency care and hospital emergency.

Finite-State Technology

2018 ◽  
Author(s):  
Mans Hulden
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Computational Linguistics ◽  
Language Processing ◽  
Finite State Machines ◽  
Regular Languages ◽  
Finite State Automata ◽  
State Machines ◽  
Computational Phonology ◽  
Finite State

Finite-state machines—automata and transducers—are ubiquitous in natural-language processing and computational linguistics. This chapter introduces the fundamentals of finite-state automata and transducers, both probabilistic and non-probabilistic, illustrating the technology with example applications and common usage. It also covers the construction of transducers, which correspond to regular relations, and automata, which correspond to regular languages. The technologies introduced are widely employed in natural language processing, computational phonology and morphology in particular, and this is illustrated through common practical use cases.

scholarly journals The Sentiments Analysis of Donald Trump and Jokowi’s Twitters on Covid-19 Policy Dissemination

Webology ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 389-405
Author(s):  
Rahmad Agus Dwianto ◽  
Achmad Nurmandi ◽  
Salahudin Salahudin
Keyword(s):  
Data Collection ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
The Us ◽  
Twitter Account ◽  
Policy Dissemination ◽  
Collection Period ◽  
Source Materials

As Covid-19 spreads to other nations and governments attempt to minimize its effect by introducing countermeasures, individuals have often used social media outlets to share their opinions on the measures themselves, the leaders implementing them, and the ways in which their lives are shifting. Sentiment analysis refers to the application in source materials of natural language processing, computational linguistics, and text analytics to identify and classify subjective opinions. The reason why this research uses a sentiment case study towards Trump and Jokowi's policies is because Jokowi and Trump have similarities in handling Covid-19. Indonesia and the US are still low in the discipline in implementing health protocols. The data collection period was chosen on September 21 - October 21 2020 because during that period, the top 5 trending on Twitter included # covid19, #jokowi, #miglobal, #trump, and #donaldtrump. So, this period is most appropriate for taking data and discussing the handling of Covid-19 by Jokowi and Trump. The result shows both Jokowi and Trump have higher negative sentiments than positive sentiments during the period. Trump had issued a controversial statement regarding the handling of Covid-19. This research is limited to the sentiment generated by the policies conveyed by the US and Indonesian Governments via @jokowi and @realDonaldTrump Twitter Account. The dataset presented in this research is being collected and analyzed using the Brand24, a software-automated sentiment analysis. Further research can increase the scope of the data and increase the timeframe for data collection and develop tools for analyzing sentiment.

scholarly journals Why People Who Know Less Think They Know about COVID-19: Evidence from US and Singapore

2021 ◽  
pp. 107769902110494
Author(s):  
Sangwon Lee ◽  
Masahiro Yamamoto ◽  
Edson C. Tandoc
Keyword(s):  
Social Media ◽  
The United States ◽  
Factual Knowledge ◽  
Interaction Patterns ◽  
Traditional Media ◽  
Subjective Knowledge ◽  
Types Of Knowledge ◽  
Knowledge Based ◽  
Different Types ◽  
Main Effects

This study explores the effects of traditional media and social media on different types of knowledge about COVID-19. We also explore how surveillance motivation moderates the relationship between media use and different types of knowledge. Based on cross-national data from Singapore and the United States, we find that news seeking via social media is negatively related to factual knowledge and positively related to subjective knowledge and knowledge miscalibration. News seeking via traditional media is not significantly related to factual knowledge. Although the main effects are highly consistent across the two countries, we find some different interaction patterns across these countries.

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