ASU Electronic Theses and Dissertations
- 4 English
- 4 Public
- Natural Language Processing
- 4 Computer science
- 2 Machine Learning
- 1 Artificial intelligence
- 1 Automatic Identification
- 1 Data Mining
- 1 Framing Analysis
- 1 Framing Shifts
- 1 Generalized Concepts
- 1 Hierarchical Merging
- 1 Pyramid Evaluation
- 1 Sentence embeddings
- 1 Social Network Analysis
- 1 Social sciences education
- 1 Story Detection
- 1 Term Rank
- 1 Text Mining
- 1 Text Summarization
- 1 Time Series Data
- 1 sentence classification
- 1 word and relation embedding
- 1 word2vec
With the advent of Internet, the data being added online is increasing at enormous rate. Though search engines are using IR techniques to facilitate the search requests from users, the results are not effective towards the search query of the user. The search engine user has to go through certain webpages before getting at the webpage he/she wanted. This problem of Information Overload can be solved using Automatic Text Summarization. Summarization is a process of obtaining at abridged version of documents so that user can have a quick view to understand what exactly the document is about. Email threads from ...
- Nadella, Sravan, Davulcu, Hasan, Li, Baoxin, et al.
- Created Date
Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model ...
- Alzahrani, Sultan, Davulcu, Hasan, Corman, Steve R., et al.
- Created Date
A major challenge in automated text analysis is that different words are used for related concepts. Analyzing text at the surface level would treat related concepts (i.e. actors, actions, targets, and victims) as different objects, potentially missing common narrative patterns. Generalized concepts are used to overcome this problem. Generalization may result into word sense disambiguation failing to find similarity. This is addressed by taking into account contextual synonyms. Concept discovery based on contextual synonyms reveal information about the semantic roles of the words leading to concepts. Merger engine generalize the concepts so that it can be used as features in ...
- Kedia, Nitesh, Davulcu, Hasan, Corman, Steve R, et al.
- Created Date
In recent years, several methods have been proposed to encode sentences into fixed length continuous vectors called sentence representation or sentence embedding. With the recent advancements in various deep learning methods applied in Natural Language Processing (NLP), these representations play a crucial role in tasks such as named entity recognition, question answering and sentence classification. Traditionally, sentence vector representations are learnt from its constituent word representations, also known as word embeddings. Various methods to learn the distributed representation (embedding) of words have been proposed using the notion of Distributional Semantics, i.e. “meaning of a word is characterized by the company ...
- Rath, Trideep, Baral, Chitta, Li, Baoxin, et al.
- Created Date
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries.
For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at email@example.com.