Matching Items (111)
Filtering by
- Creators: Liu, Huan
- Member of: ASU Electronic Theses and Dissertations
Description
The increasing popularity of Twitter renders improved trustworthiness and relevance assessment of tweets much more important for search. However, given the limitations on the size of tweets, it is hard to extract measures for ranking from the tweet's content alone. I propose a method of ranking tweets by generating a reputation score for each tweet that is based not just on content, but also additional information from the Twitter ecosystem that consists of users, tweets, and the web pages that tweets link to. This information is obtained by modeling the Twitter ecosystem as a three-layer graph. The reputation score is used to power two novel methods of ranking tweets by propagating the reputation over an agreement graph based on tweets' content similarity. Additionally, I show how the agreement graph helps counter tweet spam. An evaluation of my method on 16~million tweets from the TREC 2011 Microblog Dataset shows that it doubles the precision over baseline Twitter Search and achieves higher precision than current state of the art method. I present a detailed internal empirical evaluation of RAProp in comparison to several alternative approaches proposed by me, as well as external evaluation in comparison to the current state of the art method.
ContributorsRavikumar, Srijith (Author) / Kambhampati, Subbarao (Thesis advisor) / Davulcu, Hasan (Committee member) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Automating aspects of biocuration through biomedical information extraction could significantly impact biomedical research by enabling greater biocuration throughput and improving the feasibility of a wider scope. An important step in biomedical information extraction systems is named entity recognition (NER), where mentions of entities such as proteins and diseases are located within natural-language text and their semantic type is determined. This step is critical for later tasks in an information extraction pipeline, including normalization and relationship extraction. BANNER is a benchmark biomedical NER system using linear-chain conditional random fields and the rich feature set approach. A case study with BANNER locating genes and proteins in biomedical literature is described. The first corpus for disease NER adequate for use as training data is introduced, and employed in a case study of disease NER. The first corpus locating adverse drug reactions (ADRs) in user posts to a health-related social website is also described, and a system to locate and identify ADRs in social media text is created and evaluated. The rich feature set approach to creating NER feature sets is argued to be subject to diminishing returns, implying that additional improvements may require more sophisticated methods for creating the feature set. This motivates the first application of multivariate feature selection with filters and false discovery rate analysis to biomedical NER, resulting in a feature set at least 3 orders of magnitude smaller than the set created by the rich feature set approach. Finally, two novel approaches to NER by modeling the semantics of token sequences are introduced. The first method focuses on the sequence content by using language models to determine whether a sequence resembles entries in a lexicon of entity names or text from an unlabeled corpus more closely. The second method models the distributional semantics of token sequences, determining the similarity between a potential mention and the token sequences from the training data by analyzing the contexts where each sequence appears in a large unlabeled corpus. The second method is shown to improve the performance of BANNER on multiple data sets.
ContributorsLeaman, James Robert (Author) / Gonzalez, Graciela (Thesis advisor) / Baral, Chitta (Thesis advisor) / Cohen, Kevin B (Committee member) / Liu, Huan (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Ranging in subject from a Tuareg festival outside Timbuktu to the 1975 "Battle of the Sexes" race at Belmont track to a Mississippi classroom in the Delta flood plains, the poems in The Body Snatcher's Complaint explore the blurring of self hood, a feeling of foreignness within one's own physical experience of the world, in the most intimate and global contexts.
ContributorsMurray, Catherine (Author) / Hogue, Cynthia (Thesis advisor) / Ball, Sally (Committee member) / Hummer, Terry (Committee member) / Arizona State University (Publisher)
Created2013
Description
Cruz del Sur is an exploration of what it means to be an outsider: as a resident, as a foreigner, from the perspective of the human eye, or from the perspective of a camera lens. An unlikely blending of voices, these poems embark the reader on a journey across a continent, and also into an interior: a mystical quest.
ContributorsMontgomery, Scott (Author) / Dubie, Norman (Thesis advisor) / Hogue, Cynthia (Committee member) / Hummer, Terry (Committee member) / Arizona State University (Publisher)
Created2013
Description
Black Laurel is a book-length manuscript which has at its center poems that reveal and explore issues related to Michele Poulos's identity as a Greek-American writer, discovering the connections that link the past and present of both Greece and America. These poems often work as a quest to recover identity. They explore the idea that it is her own privileged perspective as an educated Greek-American woman that both allows and in some ways prevents her seeing herself in the Greeks who today are struggling economically, emotionally, and psychologically. Many of the poems work to achieve a complex understanding of both an individual as well as a broader cultural history. These poems sometimes take on the personas of striking figures from other times and other landscapes, while others draw on materials which are somewhat more autobiographical. In one poem titled "Before My Mother Set Herself on Fire," the speaker is an imagined daughter in a modern-day Greek family. The poem, inspired by a news story about an elderly man who shot himself in the head in front of Syntagma Square in Athens to protest the austerity measures imposed on the Greek population, explores the various ways in which a national crisis may affect an individual family. Alternatively, Poulos delves into her personal family history in "When the Wind Falls," a poem about the Nazi invasions of northern Greece. At the same time, this focus on past and present Greece is only one strand in a wide-ranging manuscript woven of materials which also include a variety of subjects related to science, history, eroticism, mysticism, and much more.
ContributorsPoulos, Michele (Author) / Dubie, Norman (Thesis advisor) / Hogue, Cynthia (Committee member) / Hummer, Terry (Committee member) / Arizona State University (Publisher)
Created2013
Description
In most social networking websites, users are allowed to perform interactive activities. One of the fundamental features that these sites provide is to connecting with users of their kind. On one hand, this activity makes online connections visible and tangible; on the other hand, it enables the exploration of our connections and the expansion of our social networks easier. The aggregation of people who share common interests forms social groups, which are fundamental parts of our social lives. Social behavioral analysis at a group level is an active research area and attracts many interests from the industry. Challenges of my work mainly arise from the scale and complexity of user generated behavioral data. The multiple types of interactions, highly dynamic nature of social networking and the volatile user behavior suggest that these data are complex and big in general. Effective and efficient approaches are required to analyze and interpret such data. My work provide effective channels to help connect the like-minded and, furthermore, understand user behavior at a group level. The contributions of this dissertation are in threefold: (1) proposing novel representation of collective tagging knowledge via tag networks; (2) proposing the new information spreader identification problem in egocentric soical networks; (3) defining group profiling as a systematic approach to understanding social groups. In sum, the research proposes novel concepts and approaches for connecting the like-minded, enables the understanding of user groups, and exposes interesting research opportunities.
ContributorsWang, Xufei (Author) / Liu, Huan (Thesis advisor) / Kambhampati, Subbarao (Committee member) / Sundaram, Hari (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering is applied to find projects which involve resources of similar skillsets and which involve similar complexities and size. This results in "resource utilization templates" for groups of related projects from a resource consumption perspective. Then project characteristics are identified which generate this diversity in headcounts and skillsets. These characteristics are not currently contained in the data base and are elicited from the managers of historical projects. This represents an opportunity to improve the usefulness of the data collection system for the future. The ultimate goal is to match the product technical features with the resource requirement for projects in the past as a model to forecast resource requirements by skill set for future projects. The forecasting model is developed using linear regression with cross validation of the training data as the past project execution are relatively few in number. Acceptable levels of forecast accuracy are achieved relative to human experts' results and the tool is applied to forecast some future projects' resource demand.
ContributorsBhattacharya, Indrani (Author) / Sen, Arunabha (Thesis advisor) / Kempf, Karl G. (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
Description
Contemporary online social platforms present individuals with social signals in the form of news feed on their peers' activities. On networks such as Facebook, Quora, network operator decides how that information is shown to an individual. Then the user, with her own interests and resource constraints selectively acts on a subset of items presented to her. The network operator again, shows that activity to a selection of peers, and thus creating a behavioral loop. That mechanism of interaction and information flow raises some very interesting questions such as: can network operator design social signals to promote a particular activity like sustainability, public health care awareness, or to promote a specific product? The focus of my thesis is to answer that question. In this thesis, I develop a framework to personalize social signals for users to guide their activities on an online platform. As the result, we gradually nudge the activity distribution on the platform from the initial distribution p to the target distribution q. My work is particularly applicable to guiding collaborations, guiding collective actions, and online advertising. In particular, I first propose a probabilistic model on how users behave and how information flows on the platform. The main part of this thesis after that discusses the Influence Individuals through Social Signals (IISS) framework. IISS consists of four main components: (1) Learner: it learns users' interests and characteristics from their historical activities using Bayesian model, (2) Calculator: it uses gradient descent method to compute the intermediate activity distributions, (3) Selector: it selects users who can be influenced to adopt or drop specific activities, (4) Designer: it personalizes social signals for each user. I evaluate the performance of IISS framework by simulation on several network topologies such as preferential attachment, small world, and random. I show that the framework gradually nudges users' activities to approach the target distribution. I use both simulation and mathematical method to analyse convergence properties such as how fast and how close we can approach the target distribution. When the number of activities is 3, I show that for about 45% of target distributions, we can achieve KL-divergence as low as 0.05. But for some other distributions KL-divergence can be as large as 0.5.
ContributorsLe, Tien D (Author) / Sundaram, Hari (Thesis advisor) / Davulcu, Hasan (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2014
Description
This dissertation addresses the representation of women in the poetry of the Irish poet Thomas Kinsella. Using a variety of theoretical approaches, including historical criticism, French feminist theory and Jungian psychoanalytical theory, I argue that although women are an integral part of Kinsella's ongoing aesthetic project of self-interrogation, their role in his poetry is deeply problematic from a feminist perspective. For purposes of my discussion I have divided my analysis into three categories of female representation: the realistically based figure of the poet's wife Eleanor, often referred to as the Beloved; female archetypes and anima as formulated by the psychologist C.G. Jung; and the poetic trope of the feminized Muse. My contention is that while the underlying effect of the early love and marriage poems is to constrain the female subject by reinforcing stereotypical gender positions, Kinsella's aesthetic representation of this relationship undergoes a transformation as his poetry matures. With regard to Kinsella's mid-career work from the 1970s and the 1980s I argue that the poet's aesthetic integration of Jungian archetypes into his poetry of psychic exploration fundamentally influences his representation of women, whether real or archetypal. These works represent a substantial advance in the complexity of Kinsella's poetry; however, the imaginative power of these poems is ultimately undermined by the very ideas that inspire them - Jungian archetypal thought - since women are represented exclusively as facilitators and symbols on this male-centered journey of self-discovery. Further complicating the gender dynamics in Kinsella's poetry is the presence of the female Muse. This figure, which becomes of increasing importance to the poet, transforms from an aestheticized image of the Beloved, to a sinister snake-like apparition, and finally into a disembodied voice that is a projection of the poet and his alter-ego. Ultimately, Kinsella's Muse is an aesthetic construction, the site of inquiry into the difficulties inherent in the creative process, and a metaphor for the creative process itself. Through his innovative deployment of the trope of the Muse, Kinsella continues to advance the aesthetics of contemporary Irish poetry.
ContributorsLeavy, Adrienne (Author) / Castle, Gregory (Thesis advisor) / Hummer, Terry (Committee member) / Hogue, Cynthia (Committee member) / Arizona State University (Publisher)
Created2013
Description
Most data cleaning systems aim to go from a given deterministic dirty database to another deterministic but clean database. Such an enterprise pre–supposes that it is in fact possible for the cleaning process to uniquely recover the clean versions of each dirty data tuple. This is not possible in many cases, where the most a cleaning system can do is to generate a (hopefully small) set of clean candidates for each dirty tuple. When the cleaning system is required to output a deterministic database, it is forced to pick one clean candidate (say the "most likely" candidate) per tuple. Such an approach can lead to loss of information. For example, consider a situation where there are three equally likely clean candidates of a dirty tuple. An appealing alternative that avoids such an information loss is to abandon the requirement that the output database be deterministic. In other words, even though the input (dirty) database is deterministic, I allow the reconstructed database to be probabilistic. Although such an approach does avoid the information loss, it also brings forth several challenges. For example, how many alternatives should be kept per tuple in the reconstructed database? Maintaining too many alternatives increases the size of the reconstructed database, and hence the query processing time. Second, while processing queries on the probabilistic database may well increase recall, how would they affect the precision of the query processing? In this thesis, I investigate these questions. My investigation is done in the context of a data cleaning system called BayesWipe that has the capability of producing multiple clean candidates per each dirty tuple, along with the probability that they are the correct cleaned version. I represent these alternatives as tuples in a tuple disjoint probabilistic database, and use the Mystiq system to process queries on it. This probabilistic reconstruction (called BayesWipe–PDB) is compared to a deterministic reconstruction (called BayesWipe–DET)—where the most likely clean candidate for each tuple is chosen, and the rest of the alternatives discarded.
ContributorsRihan, Preet Inder Singh (Author) / Kambhampati, Subbarao (Thesis advisor) / Liu, Huan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013