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Skip to Search Results- 25Natural Language Processing
- 10Machine Learning
- 5Artificial Intelligence
- 3NLP
- 2Computer Vision
- 2Deep Learning
- 1Alexander, Graham
- 1Campbell, Hazel V
- 1Dhankar, Abhishek
- 1Dziri, Nouha
- 1Farruque, Nawshad
- 1Hauer, Bradley
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Spring 2021
It has gotten increasingly harder for laypersons to determine the veracity of online health information. This is because of the explosion of content in health social media, allowing anyone with an Internet connection to create and propagate health-related content. This includes both innocuous and...
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Spring 2023
Traditional survey based methods for clinical depression detection are not always effective; the patient may not reflect their actual mental health condition because of the cognitive bias exhibited while filling out questionnaires about depression. Established through ample earlier work, social...
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Spring 2023
This thesis describes the design of a system that is capable of the generation of a Knowledge Graph (KG), referred to as Knowledge Graph Population (KGP), from conversations, specifically with elderly people. While this system still follows a traditional KGP approach with Entity Recognition (ER),...
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Fall 2022
Sentence reconstruction and generation are essential applications in Natural Language Processing (NLP). Early studies were based on classic methods such as production rules and statistical models. Recently, the prevailing models typically use deep neural networks. In this study, we utilize deep...
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Fall 2022
Medical Fake News is a pervasive part of the information that people consume on the internet. It may lead people to take actions which may put the lives of their family and community in danger - such actions include vaccine hesitancy, administering unverified and harmful treatments, etc. First...
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Fall 2018
Neural approaches to sequence labeling often use a Conditional Random Field (CRF) to model their output dependencies, while Recurrent Neural Networks (RNN) are used for the same purpose in other tasks. We set out to establish RNNs as an attractive alternative to CRFs for sequence labeling. To do...
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Fall 2017
Inflectional morphology presents numerous problems for traditional computational models, not least of which is an increase in the number of rare types in any corpus. Although few annotated corpora exist for morphologically complex languages, it is possible for lay-speakers of the language to...
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Spring 2016
Morphologically complex languages such as Arabic pose several challenges in Natural Language Processing (NLP) due to their complexity and token sparsity. Most techniques approach the problem by transforming the words of the language from their sparse surface form representation to a less sparse...
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Fall 2020
Language Modeling (LM) is often formulated as a next-word prediction problem over a large vocabulary, which makes it challenging. To effectively perform the task of next-word prediction, Long Short Term Memory networks (LSTMs) must keep track of many types of information. Some information is...
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Fall 2016
The field of biomedicine is reeling from “information overload”. Indeed, biomedical researchers find it almost impossible to stay current with published literature due to the vast amounts of data being generated and published. As a result, they are turning to text mining. Over the past two...