Understanding Lists: Umberto Eco\u27s Rhetoric of Communication and Signification

This project, Understanding Lists: Umberto Eco’s Rhetoric of Communication and Signification, begins and ends with an observation and warning suggested throughout Eco’s work: lists are the origin of culture and the Internet as the Mother of All Lists threatens to end culture. To understand this warning, I turn to Eco’s work on lists, contextualized within a 2009 exhibition at the Musée du Louvre and in an illustrated collection, The Infinity of Lists. This project offers an analysis of Eco’s understanding of lists concurrent to his commentary on the social and cultural implications of the algorithmic-obsessed Internet age. To understand his argument, this project collects hints of insight through his corpus. In Eco’s cultural aesthetics, he celebrates the notion of openness that invites and encourages audience participation in the interpretation of texts with multiple possibilities. With his interpretive semiotics, Eco offers a theory of culture grounded in signification and communication. Signification consists of the codes of culture that make meaning and interpretive response possible. Communication is the labor of sign production and interpretation. Throughout his literary praxis, Eco implements these theoretical notions into story-form, and with his fifth novel, The Mysterious Flame of Queen Loana, affirms the mutual necessity of communication and signification. Ultimately, Eco urges us to list as a response to the threats of algorithmic processing of big data that displaces and replaces the human interpreter. For Eco, listing a form of communication that requires the labor to wade through information, activate codes of signification, and interpret cultural meaning

"At the edges of perception": William Gaddis and the encyclopedic novel from Joyce to David foster Wallace

"Longer works of fiction," a character in William Gaddis's JR complains of the current literary scene, are now "dismissed as classics and remain . . . largely unread due to the effort involved in reading and turning any more than two hundred pages" (527). This study argues that despite most literary critics constructing American postmodernism as a movement that privileges short works, in contrast to the encyclopedic masterworks of modernism, there are in fact a large number of artistically sophisticated contemporary novels of encyclopedic scope that demonstrate often ignored lines of continuity from works like James Joyce's Ulysses. In arguing this, I attempt not just to draw attention to a neglected strain in contemporary American fiction, but also to provide a more accurate context in which those few recent encyclopedic novels that have assumed centrality, like Gravity's Rainbow, might be evaluated. In doing so, this thesis also seeks to demonstrate the pivotal position of William Gaddis who, despite publishing four impressive novels that engage with the legacy of modernism and pre-empt elements of postmodernism, has been excluded from most studies dealing with the transition between the two movements. Through detailed readings of four encyclopedic novels - Gaddis's The Recognitions, Don DeLillo's Underworld, Richard Powers's The Gold Bug Variations, and David Foster Wallace’s Infinite Jest - I show Gaddis's continuation of encyclopedic modernism, the importance of his example to later writers, and the continuing vitality of the encyclopedic novel beyond the defined limits of modernism. However, as these novels try to encompass the full circle of knowledge, in order to do justice to their diverse learning I have adopted a different approach in each chapter. Very broadly, they attempt to encircle art, psychology, science, and literature, which, taken together, attempt to synthesise a defence of the contemporary encyclopedic novel. While minimalist writers from Raymond Carver to Ann Beattie have affirmed that less is more, this thesis argues that, in some cases, more really is more

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

Crowdsourcing for web genre annotation

Recently, genre collection and automatic genre identification for the web has attracted much attention. However, currently there is no genre-annotated corpus of web pages where inter-annotator reliability has been established, i.e. the corpora are either not tested for inter-annotator reliability or exhibit low inter-coder agreement. Annotation has also mostly been carried out by a small number of experts, leading to concerns with regard to scalability of these annotation efforts and transferability of the schemes to annotators outside these small expert groups. In this paper, we tackle these problems by using crowd-sourcing for genre annotation, leading to the Leeds Web Genre Corpus—the first web corpus which is, demonstrably reliably annotated for genre and which can be easily and cost-effectively expanded using naive annotators. We also show that the corpus is source and topic diverse

Exceptional scale: metafiction and the maximalist tradition in contemporary American literary history

This dissertation reexamines the narrative practice of self-reflexivity through the lens of aesthetic size to advance a new approach to reading long-form novels of the late twentieth and early twenty-first centuries. Whereas previous scholarship on the maximalist tradition relies on the totalizing rhetorics of endlessness, exhaustion, encyclopedism, and excess, I interpret the form’s reflexive awareness of its own enlarged scale as a uniquely narrative “knowledge work” that mediates the reader’s experience of information-rich texts. Thus, my narrative and network theory-informed approach effectively challenges the analytical modes of prominent genre theories such as the Mega-Novel, encyclopedic narrative, the systems novel, and modern epic to propose a critical reading method that recovers the extra-literary discourses through which scalarity is framed. Following this logic, each chapter historicizes prior theories of literary scale in postwar U.S. fiction toward redefining cross-national differences that vary across the boundaries of class, race, ethnicity, religion, gender, and sexuality. Chapter two addresses the scholarly discourse of encyclopedism surrounding the Mega-Novels of Thomas Pynchon and Joseph McElroy. Posing an ethical challenge to popular critiques of metafictional aesthetics, both authors, I argue, contest one of the critical orthodoxies of realist form—the “exceptionality thesis”—which rests on an assumed separation between an audience’s experience of fictional minds in a literary work and its understanding of actual minds in everyday life. In constructing a suitably massive networked platform on which to stage identity as a pluralistic work-in-progress, Gravity’s Rainbow and Women and Men, I contend, narrativize those operations of mind typically occluded from narrative discourse, and so make literal their authors’ meta-ethical visions of a “multiplying real” as much a part of our world as the novel’s own. Chapter three focuses on the mise en abyme as a discursive practice in the labyrinthine narratives of Samuel R. Delany and Mark Z. Danielewski. My analysis posits The Mad Man and House of Leaves as immersive case studies on the academic reading experience by interrogating the satirical strategy of “mock scholarship,” in which a textual object at plot’s center is gradually displaced by the intra-textual reception history that surrounds it. Subtly complicating an increasingly imperceptible line between fact and its fictional counterpart, Delany and Danielewski, I assert, propose new forms of knowledge production through a multiplicity of potential “research spaces” that micromanage the interpretive process while exceeding the structural contours that frame it. Chapter four considers the problem of literary canon formation in the polemical epics of Gayl Jones and Joshua Cohen. Across vast surveys of the stereotypes that mark their marginalization, Jones and Cohen transgress the metaphorical borders constructed between individual voice, collective identity, and the literary institutions that reify “ethnoracial diversity” as a belated form of cultural capital. Explicitly foregrounding the ideological gaps, errors, and omissions against which canonical classification is typically defined, Mosquito and Witz, I suggest, promote not so much a representative widening of the canon’s historically restrictive archive as a complete dissolution of the exclusionary practices it honors and preserves

Similarity Learning Over Large Collaborative Networks

In this thesis, we propose novel solutions to similarity learning problems on collaborative networks. Similarity learning is essential for modeling and predicting the evolution of collaborative networks. In addition, similarity learning is used to perform ranking, which is the main component of recommender systems. Due to the the low cost of developing such collaborative networks, they grow very quickly, and therefore, our objective is to develop models that scale well to large networks. The similarity measures proposed in this thesis make use of the global link structure of the network and of the attributes of the nodes in a complementary way. We first define a random walk model, named Visiting Probability (VP), to measure proximity between two nodes in a graph. VP considers all the paths between two nodes collectively and thus reduces the effect of potentially unreliable individual links. Moreover, using VP and the structural characteristics of small-world networks (a frequent type of networks), we design scalable algorithms based on VP similarity. We then model the link structure of a graph within a similarity learning framework, in which the transformation of nodes to a latent space is trained using a discriminative model. When trained over VP scores, the model is able to better predict the relations in a graph in comparison to models learned directly from the network’s links. Using the VP approach, we explain how to transfer knowledge from a hypertext encyclopedia to text analysis tasks. We consider the graph of Wikipedia articles with two types of links between them: hyperlinks and content similarity ones. To transfer the knowledge learned from the Wikipedia network to text analysis tasks, we propose and test two shared representation methods. In the first one, a given text is mapped to the corresponding concepts in the network. Then, to compute similarity between two texts, VP similarity is applied to compute the distance between the two sets of nodes. The second method uses the latent space model for representation, by training a transformation from words to the latent space over VP scores. We test our proposals on several benchmark tasks: word similarity, document similarity / clustering / classification, information retrieval, and learning to rank. The results are most often competitive compared to state-of-the-art task-specific methods, thus demonstrating the generality of our proposal. These results also support the hypothesis that both types of links over Wikipedia are useful, as the improvement is higher when both are used. In many collaborative networks, different link types can be used in a complementary way. Therefore, we propose two joint similarity learning models over the nodes’ attributes, to be used for link prediction in networks with multiple link types. The first model learns a similarity metric that consists of two parts: the general part, which is shared between all link types, and the specific part, which is trained specifically for each type of link. The second model consists of two layers: the first layer, which is shared between all link types, embeds the objects of the network into a new space, and then a similarity is learned specifically for each link type in this new space. Our experiments show that the proposed joint modeling and training frameworks improve link prediction performance significantly for each link type in comparison to multiple baselines. The two-layer similarity model outperforms the first one, as expected, due to its capability of modeling negative correlations among different link types. Finally, we propose a learning to rank algorithm on network data, which uses both the attributes of the nodes and the structure of the links for learning and inference. Link structure is used in training through a neighbor-aware ranker which considers both node attributes and scores of neighbor nodes. The global link structure of the network is used in inference through an original propagation method called the Iterative Ranking Algorithm. This propagates the predicted scores in the graph on condition that they are above a given threshold. Thresholding improves performance, and makes a time-efficient implementation possible, for application to large scale graphs. The observed improvements are explained considering the structural properties of small-world networks