Selecting and Generating Computational Meaning Representations for Short Texts

Language conveys meaning, so natural language processing (NLP) requires representations of meaning. This work addresses two broad questions: (1) What meaning representation should we use? and (2) How can we transform text to our chosen meaning representation? In the first part, we explore different meaning representations (MRs) of short texts, ranging from surface forms to deep-learning-based models. We show the advantages and disadvantages of a variety of MRs for summarization, paraphrase detection, and clustering. In the second part, we use SQL as a running example for an in-depth look at how we can parse text into our chosen MR. We examine the text-to-SQL problem from three perspectives—methodology, systems, and applications—and show how each contributes to a fuller understanding of the task.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143967/1/cfdollak_1.pd

Web knowledge bases

Knowledge is key to natural language understanding. References to specific people, places and things in text are crucial to resolving ambiguity and extracting meaning. Knowledge Bases (KBs) codify this information for automated systems — enabling applications such as entity-based search and question answering. This thesis explores the idea that sites on the web may act as a KB, even if that is not their primary intent. Dedicated kbs like Wikipedia are a rich source of entity information, but are built and maintained at an ongoing cost in human effort. As a result, they are generally limited in terms of the breadth and depth of knowledge they index about entities. Web knowledge bases offer a distributed solution to the problem of aggregating entity knowledge. Social networks aggregate content about people, news sites describe events with tags for organizations and locations, and a diverse assortment of web directories aggregate statistics and summaries for long-tail entities notable within niche movie, musical and sporting domains. We aim to develop the potential of these resources for both web-centric entity Information Extraction (IE) and structured KB population. We first investigate the problem of Named Entity Linking (NEL), where systems must resolve ambiguous mentions of entities in text to their corresponding node in a structured KB. We demonstrate that entity disambiguation models derived from inbound web links to Wikipedia are able to complement and in some cases completely replace the role of resources typically derived from the KB. Building on this work, we observe that any page on the web which reliably disambiguates inbound web links may act as an aggregation point for entity knowledge. To uncover these resources, we formalize the task of Web Knowledge Base Discovery (KBD) and develop a system to automatically infer the existence of KB-like endpoints on the web. While extending our framework to multiple KBs increases the breadth of available entity knowledge, we must still consolidate references to the same entity across different web KBs. We investigate this task of Cross-KB Coreference Resolution (KB-Coref) and develop models for efficiently clustering coreferent endpoints across web-scale document collections. Finally, assessing the gap between unstructured web knowledge resources and those of a typical KB, we develop a neural machine translation approach which transforms entity knowledge between unstructured textual mentions and traditional KB structures. The web has great potential as a source of entity knowledge. In this thesis we aim to first discover, distill and finally transform this knowledge into forms which will ultimately be useful in downstream language understanding tasks

Proceedings

Proceedings of the Ninth International Workshop on Treebanks and Linguistic Theories. Editors: Markus Dickinson, Kaili Müürisep and Marco Passarotti. NEALT Proceedings Series, Vol. 9 (2010), 268 pages. © 2010 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/15891

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Argumentative zoning information extraction from scientific text

Let me tell you, writing a thesis is not always a barrel of laughs—and strange things can happen, too. For example, at the height of my thesis paranoia, I had a re-current dream in which my cat Amy gave me detailed advice on how to restructure the thesis chapters, which was awfully nice of her. But I also had a lot of human help throughout this time, whether things were going fine or beserk. Most of all, I want to thank Marc Moens: I could not have had a better or more knowledgable supervisor. He always took time for me, however busy he might have been, reading chapters thoroughly in two days. He both had the calmness of mind to give me lots of freedom in research, and the right judgement to guide me away, tactfully but determinedly, from the occasional catastrophe or other waiting along the way. He was great fun to work with and also became a good friend. My work has profitted from the interdisciplinary, interactive and enlightened atmosphere at the Human Communication Centre and the Centre for Cognitive Science (which is now called something else). The Language Technology Group was a great place to work in, as my research was grounded in practical applications develope

Neoliberalism and Contemporary American Literature

How has American literature responded to the dominance of neoliberalism? Does it make sense to speak of an American literature in neoliberal times? Can literature function as either a neutral category or a privileged narrative of national imagination in a time when paradigms of the nation-state and of liberal capitalism are undergoing a prolonged shift? In the United States, as elsewhere, the association between the nation-state, liberal capitalism, and literary form has a long history, reflecting determinate relations between writer and reader within imagined national community. As this community loses its symbolic efficiency in the age of neoliberal capital, the boundaries and possibilities of literary production and representation shift. This collection of essays examines how American literature both models and interrogates the neoliberal present. Has literary realism been exhausted as a narrative form? Can contemporary literature still imagine either the end of capitalism or an alternative to it?https://digitalcommons.dartmouth.edu/dartmouth_press/1001/thumbnail.jp

Noise in and as music

One hundred years after Luigi Russolo’s “The Art of Noises,” this book exposes a cross-section of the current motivations, activities, thoughts, and reflections of composers, performers, and artists who work with noise in all of its many forms. The book’s focus is the practice of noise and its relationship to music, and in particular the role of noise as musical material—as form, as sound, as notation or interface, as a medium for listening, as provocation, as data. Its contributors are first and foremost practitioners, which inevitably turns attention toward how and why noise is made and its potential role in listening and perceiving. Contributors include Peter Ablinger, Sebastian Berweck, Aaron Cassidy, Marko Ciciliani, Nick Collins, Aaron Einbond, Matthias Haenisch, Alec Hall, Martin Iddon, Bryan Jacobs, Phil Julian, Michael Maierhof, Joan Arnau Pàmies, and James Whitehead (JLIAT). The book also features a collection of short responses to a two-question “interview”—“what is noise (music) to you?” and “why do you make it?”—by some of the leading musicians working with noise today. Their work spans a wide range of artistic practice, including instrumental, vocal, and electronic music; improvisation; notated composition; theater; sound installation; DIY; and software development. Interview subjects include Eryck Abecassis, Franck Bedrossian, Antoine Chessex, Ryan Jordan, Alice Kemp (Germseed), George Lewis, Lasse Marhaug, Maja Solveig Kjelstrup Ratkje, Diemo Schwarz, Ben Thigpen, Kasper Toeplitz, and Pierre Alexandre Tremblay.Publishe

Global Inference for Sentence Compression: An Integer Linear Programming Approach

Institute for Communicating and Collaborative SystemsIn this thesis we develop models for sentence compression. This text rewriting task has recently attracted a lot of attention due to its relevance for applications (e.g., summarisation) and simple formulation by means of word deletion. Previous models for sentence compression have been inherently local and thus fail to capture the long range dependencies and complex interactions involved in text rewriting. We present a solution by framing the task as an optimisation problem with local and global constraints and recast existing compression models into this framework. Using the constraints we instil syntactic, semantic and discourse knowledge the models otherwise fail to capture. We show that the addition of constraints allow relatively simple local models to reach state-of-the-art performance for sentence compression. The thesis provides a detailed study of sentence compression and its models. The differences between automatic and manually created compression corpora are assessed along with how compression varies across written and spoken text. We also discuss various techniques for automatically and manually evaluating compression output against a gold standard. Models are reviewed based on their assumptions, training requirements, and scalability. We introduce a general method for extending previous approaches to allow for more global models. This is achieved through the optimisation framework of Integer Linear Programming (ILP). We reformulate three compression models: an unsupervised model, a semi-supervised model and a fully supervised model as ILP problems and augment them with constraints. These constraints are intuitive for the compression task and are both syntactically and semantically motivated. We demonstrate how they improve compression quality and reduce the requirements on training material. Finally, we delve into document compression where the task is to compress every sentence of a document and use the resulting summary as a replacement for the original document. For document-based compression we investigate discourse information and its application to the compression task. Two discourse theories, Centering and lexical chains, are used to automatically annotate documents. These annotations are then used in our compression framework to impose additional constraints on the resulting document. The goal is to preserve the discourse structure of the original document and most of its content. We show how a discourse informed compression model can outperform a discourse agnostic state-of-the-art model using a question answering evaluation paradigm