Sökning: "WIkipedia"
Visar resultat 1 - 5 av 123 uppsatser innehållade ordet WIkipedia.
1. Multilingual Text Robots for Abstract Wikipedia – Using Grammatical Framework to generate multilingual articles on Swedish localities
Kandidat-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknikSammanfattning : The vast amount of Wikipedia articles and languages has resulted in a high cost of Wikipedia, i.e. the required time and dedication for making every article available in every language. LÄS MER
2. Mitigating Unintended Bias in Toxic Comment Detection using Entropy-based Attention Regularization
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The proliferation of hate speech is a growing challenge for social media platforms, as toxic online comments can have dangerous consequences also in real life. There is a need for tools that can automatically and reliably detect hateful comments, and deep learning models have proven effective in solving this issue. LÄS MER
3. A Study in Describing Complex Words Using Wikipedia's Categorisation System : Adding Descriptive Terms to Increase the Comprehension of Swedish Texts
Master-uppsats, Linköpings universitet/Institutionen för datavetenskapSammanfattning : This thesis offers new input in the field of generating epithets to aid the comprehension of Swedish texts. For whatever reason, a reader might find certain words in a text difficult to understand. LÄS MER
4. Generating Wikipedia Articles with Grammatical Framework : A Case Study
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Natural language generation is a method used to produce understandable texts in human languages from data [1]. Grammatical Framework is a grammar formalism and a functional programming language using a nonstatistical approach to build natural language applications. LÄS MER
5. A Hybrid Approach to Hate Speech Detection
Master-uppsats, Umeå universitet/Institutionen för datavetenskapSammanfattning : An interesting question is to what extent can background knowledge help in the context of text classification. To address this in more detail, can a traditional rulebased classifier help boost the accuracy of learned models? We explore this here for detecting hate speech and offensive language in online text. LÄS MER