Matching Items (4)
Filtering by
- All Subjects: Natural Language Processing
- Creators: Greenes, Robert
Description
The rapid advancements of technology have greatly extended the ubiquitous nature of smartphones acting as a gateway to numerous social media applications. This brings an immense convenience to the users of these applications wishing to stay connected to other individuals through sharing their statuses, posting their opinions, experiences, suggestions, etc on online social networks (OSNs). Exploring and analyzing this data has a great potential to enable deep and fine-grained insights into the behavior, emotions, and language of individuals in a society. This proposed dissertation focuses on utilizing these online social footprints to research two main threads – 1) Analysis: to study the behavior of individuals online (content analysis) and 2) Synthesis: to build models that influence the behavior of individuals offline (incomplete action models for decision-making).
A large percentage of posts shared online are in an unrestricted natural language format that is meant for human consumption. One of the demanding problems in this context is to leverage and develop approaches to automatically extract important insights from this incessant massive data pool. Efforts in this direction emphasize mining or extracting the wealth of latent information in the data from multiple OSNs independently. The first thread of this dissertation focuses on analytics to investigate the differentiated content-sharing behavior of individuals. The second thread of this dissertation attempts to build decision-making systems using social media data.
The results of the proposed dissertation emphasize the importance of considering multiple data types while interpreting the content shared on OSNs. They highlight the unique ways in which the data and the extracted patterns from text-based platforms or visual-based platforms complement and contrast in terms of their content. The proposed research demonstrated that, in many ways, the results obtained by focusing on either only text or only visual elements of content shared online could lead to biased insights. On the other hand, it also shows the power of a sequential set of patterns that have some sort of precedence relationships and collaboration between humans and automated planners.
A large percentage of posts shared online are in an unrestricted natural language format that is meant for human consumption. One of the demanding problems in this context is to leverage and develop approaches to automatically extract important insights from this incessant massive data pool. Efforts in this direction emphasize mining or extracting the wealth of latent information in the data from multiple OSNs independently. The first thread of this dissertation focuses on analytics to investigate the differentiated content-sharing behavior of individuals. The second thread of this dissertation attempts to build decision-making systems using social media data.
The results of the proposed dissertation emphasize the importance of considering multiple data types while interpreting the content shared on OSNs. They highlight the unique ways in which the data and the extracted patterns from text-based platforms or visual-based platforms complement and contrast in terms of their content. The proposed research demonstrated that, in many ways, the results obtained by focusing on either only text or only visual elements of content shared online could lead to biased insights. On the other hand, it also shows the power of a sequential set of patterns that have some sort of precedence relationships and collaboration between humans and automated planners.
ContributorsManikonda, Lydia (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Li, Baoxin (Committee member) / De Choudhury, Munmun (Committee member) / Kamar, Ece (Committee member) / Arizona State University (Publisher)
Created2019
Description
Text mining of biomedical literature and clinical notes is a very active field of research in biomedical science. Semantic analysis is one of the core modules for different Natural Language Processing (NLP) solutions. Methods for calculating semantic relatedness of two concepts can be very useful in solutions solving different problems such as relationship extraction, ontology creation and question / answering [1–6]. Several techniques exist in calculating semantic relatedness of two concepts. These techniques utilize different knowledge sources and corpora. So far, researchers attempted to find the best hybrid method for each domain by combining semantic relatedness techniques and data sources manually. In this work, attempts were made to eliminate the needs for manually combining semantic relatedness methods targeting any new contexts or resources through proposing an automated method, which attempted to find the best combination of semantic relatedness techniques and resources to achieve the best semantic relatedness score in every context. This may help the research community find the best hybrid method for each context considering the available algorithms and resources.
ContributorsEmadzadeh, Ehsan (Author) / Gonzalez, Graciela (Thesis advisor) / Greenes, Robert (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Created2016
Description
Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks such as pharmacovigilance via the use of Natural Language Processing (NLP) techniques. One of the critical steps in information extraction pipelines is Named Entity Recognition (NER), where the mentions of entities such as diseases are located in text and their entity type are identified. However, the language in social media is highly informal, and user-expressed health-related concepts are often non-technical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and advanced machine learning-based NLP techniques have been underutilized. This work explores the effectiveness of different machine learning techniques, and particularly deep learning, to address the challenges associated with extraction of health-related concepts from social media. Deep learning has recently attracted a lot of attention in machine learning research and has shown remarkable success in several applications particularly imaging and speech recognition. However, thus far, deep learning techniques are relatively unexplored for biomedical text mining and, in particular, this is the first attempt in applying deep learning for health information extraction from social media.
This work presents ADRMine that uses a Conditional Random Field (CRF) sequence tagger for extraction of complex health-related concepts. It utilizes a large volume of unlabeled user posts for automatic learning of embedding cluster features, a novel application of deep learning in modeling the similarity between the tokens. ADRMine significantly improved the medical NER performance compared to the baseline systems.
This work also presents DeepHealthMiner, a deep learning pipeline for health-related concept extraction. Most of the machine learning methods require sophisticated task-specific manual feature design which is a challenging step in processing the informal and noisy content of social media. DeepHealthMiner automatically learns classification features using neural networks and utilizing a large volume of unlabeled user posts. Using a relatively small labeled training set, DeepHealthMiner could accurately identify most of the concepts, including the consumer expressions that were not observed in the training data or in the standard medical lexicons outperforming the state-of-the-art baseline techniques.
This work presents ADRMine that uses a Conditional Random Field (CRF) sequence tagger for extraction of complex health-related concepts. It utilizes a large volume of unlabeled user posts for automatic learning of embedding cluster features, a novel application of deep learning in modeling the similarity between the tokens. ADRMine significantly improved the medical NER performance compared to the baseline systems.
This work also presents DeepHealthMiner, a deep learning pipeline for health-related concept extraction. Most of the machine learning methods require sophisticated task-specific manual feature design which is a challenging step in processing the informal and noisy content of social media. DeepHealthMiner automatically learns classification features using neural networks and utilizing a large volume of unlabeled user posts. Using a relatively small labeled training set, DeepHealthMiner could accurately identify most of the concepts, including the consumer expressions that were not observed in the training data or in the standard medical lexicons outperforming the state-of-the-art baseline techniques.
ContributorsNikfarjam, Azadeh (Author) / Gonzalez, Graciela (Thesis advisor) / Greenes, Robert (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Created2016
Description
Unstructured texts containing biomedical information from sources such as electronic health records, scientific literature, discussion forums, and social media offer an opportunity to extract information for a wide range of applications in biomedical informatics. Building scalable and efficient pipelines for natural language processing and extraction of biomedical information plays an important role in the implementation and adoption of applications in areas such as public health. Advancements in machine learning and deep learning techniques have enabled rapid development of such pipelines. This dissertation presents entity extraction pipelines for two public health applications: virus phylogeography and pharmacovigilance. For virus phylogeography, geographical locations are extracted from biomedical scientific texts for metadata enrichment in the GenBank database containing 2.9 million virus nucleotide sequences. For pharmacovigilance, tools are developed to extract adverse drug reactions from social media posts to open avenues for post-market drug surveillance from non-traditional sources. Across these pipelines, high variance is observed in extraction performance among the entities of interest while using state-of-the-art neural network architectures. To explain the variation, linguistic measures are proposed to serve as indicators for entity extraction performance and to provide deeper insight into the domain complexity and the challenges associated with entity extraction. For both the phylogeography and pharmacovigilance pipelines presented in this work the annotated datasets and applications are open source and freely available to the public to foster further research in public health.
ContributorsMagge, Arjun (Author) / Scotch, Matthew (Thesis advisor) / Gonzalez-Hernandez, Graciela (Thesis advisor) / Greenes, Robert (Committee member) / Arizona State University (Publisher)
Created2019