Browsing by Subject "natural language processing"
Now showing items 1-7 of 7
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Analyzing Networked Learning Texts
(2008)Social interactions are essential in understanding the collaborative processes in networked learning environments. Although individuals may learn by retrieving information from online archives, dictionaries and ... -
Authorship Attribution using Written and Read Documents
(2019-08-07)In Authorship Attribution (AA), a task of identifying the author on an unseen document, it is often hard to obtain large amounts of training text written by an author. In our research, we analyze the influence of the size ... -
Automated Discovery and Analysis of Social Networks from Threaded Discussions
(2008)To gain greater insight into the operation of online social networks, we applied Natural Language Processing (NLP) techniques to text-based communication to identify and describe underlying social structures in online ... -
Comprehending Software Bugs Leveraging Code Structures with Neural Language Models
(2023-08-28)Software bugs claim ~50% of development time and cost the global economy billions of dollars every year. Unfortunately, despite the use of many software quality assurance (SQA) practices in software development (e.g., ... -
Data Mining in Social Media for Stock Market Prediction
(2012-09-05)In this thesis, machine learning algorithms are used in NLP to get the public sentiment on individual stocks from social media in order to study its relationship with the stock price change. The NLP approach of sentiment ... -
Evaluating Common-Sense Reasoning in Pretrained Transformer-Based Language Models Using Adversarial Schemas and Consistency Metrics
(2023-04-14)In artificial intelligence, common sense refers to simple acts of verbal reasoning. The Winograd Schema Challenge (WSC), an important test of common sense, was recently defeated by transformer-based language models. We ... -
Structural Embedding of Constituency Trees in the Attention-based Model for Machine Comprehension
(2023-08-18)Incorporating hierarchical structures for various Natural Language Processing (NLP) tasks, which involves training the model with syntactic information of constituency trees, has been shown to be very effective. Constituency ...