750 research outputs found

    Recognizing speculative language in research texts

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    This thesis studies the use of sequential supervised learning methods on two tasks related to the detection of hedging in scientific articles: those of hedge cue identification and hedge cue scope detection. Both tasks are addressed using a learning methodology that proposes the use of an iterative, error-based approach to improve classification performance, suggesting the incorporation of expert knowledge into the learning process through the use of knowledge rules. Results are promising: for the first task, we improved baseline results by 2.5 points in terms of F-score by incorporating cue cooccurence information, while for scope detection, the incorporation of syntax information and rules for syntax scope pruning allowed us to improve classification performance from an F-score of 0.712 to a final number of 0.835. Compared with state-of-the-art methods, the results are very competitive, suggesting that the approach to improving classifiers based only on the errors commited on a held out corpus could be successfully used in other, similar tasks. Additionaly, this thesis presents a class schema for representing sentence analysis in a unique structure, including the results of different linguistic analysis. This allows us to better manage the iterative process of classifier improvement, where different attribute sets for learning are used in each iteration. We also propose to store attributes in a relational model, instead of the traditional text-based structures, to facilitate learning data analysis and manipulation

    Embedding Predications

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    Written communication is rarely a sequence of simple assertions. More often, in addition to simple assertions, authors express subjectivity, such as beliefs, speculations, opinions, intentions, and desires. Furthermore, they link statements of various kinds to form a coherent discourse that reflects their pragmatic intent. In computational semantics, extraction of simple assertions (propositional meaning) has attracted the greatest attention, while research that focuses on extra-propositional aspects of meaning has remained sparse overall and has been largely limited to narrowly defined categories, such as hedging or sentiment analysis, treated in isolation. In this thesis, we contribute to the understanding of extra-propositional meaning in natural language understanding, by providing a comprehensive account of the semantic phenomena that occur beyond simple assertions and examining how a coherent discourse is formed from lower level semantic elements. Our approach is linguistically based, and we propose a general, unified treatment of the semantic phenomena involved, within a computationally viable framework. We identify semantic embedding as the core notion involved in expressing extra-propositional meaning. The embedding framework is based on the structural distinction between embedding and atomic predications, the former corresponding to extra-propositional aspects of meaning. It incorporates the notions of predication source, modality scale, and scope. We develop an embedding categorization scheme and a dictionary based on it, which provide the necessary means to interpret extra-propositional meaning with a compositional semantic interpretation methodology. Our syntax-driven methodology exploits syntactic dependencies to construct a semantic embedding graph of a document. Traversing the graph in a bottom-up manner guided by compositional operations, we construct predications corresponding to extra-propositional semantic content, which form the basis for addressing practical tasks. We focus on text from two distinct domains: news articles from the Wall Street Journal, and scientific articles focusing on molecular biology. Adopting a task-based evaluation strategy, we consider the easy adaptability of the core framework to practical tasks that involve some extra-propositional aspect as a measure of its success. The computational tasks we consider include hedge/uncertainty detection, scope resolution, negation detection, biological event extraction, and attribution resolution. Our competitive results in these tasks demonstrate the viability of our proposal

    Precision Medicine: Viable Pathways to Address Existing Research Gaps

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    Precision Medicine (PM) seeks to customize medical treatments for patients based on measurable and identifiable characteristics. Unlike personalized medicine, this effort is not intended to result in tailored care for each patient. Instead, this effort seeks to improve overall care within the medical domain by shifting the focus from one-size-fits-all care to optimized care for specified subgroups. In order for the benefits of PM to be expeditiously realized, the diverse skills sets of the scientific community must be brought to bear on the problem. This research effort explores the intersection of quality engineering (QE) and healthcare to outline how existing methodologies within the QE field could support existing PM research goals. Specifically this work examines how to determine the value of patient characteristics for use in disease prediction models with select machine learning algorithms, proposes a method to incorporate patient risk into treatment decisions through the development of performance functions, and investigates the potential impact of incorrect assumptions on estimation methods used in optimization models

    Proceedings of the Conference on Natural Language Processing 2010

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    This book contains state-of-the-art contributions to the 10th conference on Natural Language Processing, KONVENS 2010 (Konferenz zur Verarbeitung natürlicher Sprache), with a focus on semantic processing. The KONVENS in general aims at offering a broad perspective on current research and developments within the interdisciplinary field of natural language processing. The central theme draws specific attention towards addressing linguistic aspects ofmeaning, covering deep as well as shallow approaches to semantic processing. The contributions address both knowledgebased and data-driven methods for modelling and acquiring semantic information, and discuss the role of semantic information in applications of language technology. The articles demonstrate the importance of semantic processing, and present novel and creative approaches to natural language processing in general. Some contributions put their focus on developing and improving NLP systems for tasks like Named Entity Recognition or Word Sense Disambiguation, or focus on semantic knowledge acquisition and exploitation with respect to collaboratively built ressources, or harvesting semantic information in virtual games. Others are set within the context of real-world applications, such as Authoring Aids, Text Summarisation and Information Retrieval. The collection highlights the importance of semantic processing for different areas and applications in Natural Language Processing, and provides the reader with an overview of current research in this field

    GEOBIA 2016 : Solutions and Synergies., 14-16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC): open access e-book

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    Leeway to operate with plant genetic resources

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