28,353 research outputs found

    Segmenting broadcast news streams using lexical chains

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    In this paper we propose a course-grained NLP approach to text segmentation based on the analysis of lexical cohesion within text. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e. distinct news stories from broadcast news programmes. Our system SeLeCT first builds a set of lexical chains, in order to model the discourse structure of the text. A boundary detector is then used to search for breaking points in this structure indicated by patterns of cohesive strength and weakness within the text. We evaluate this technique on a test set of concatenated CNN news story transcripts and compare it with an established statistical approach to segmentation called TextTiling

    SeLeCT: a lexical cohesion based news story segmentation system

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    In this paper we compare the performance of three distinct approaches to lexical cohesion based text segmentation. Most work in this area has focused on the discovery of textual units that discuss subtopic structure within documents. In contrast our segmentation task requires the discovery of topical units of text i.e., distinct news stories from broadcast news programmes. Our approach to news story segmentation (the SeLeCT system) is based on an analysis of lexical cohesive strength between textual units using a linguistic technique called lexical chaining. We evaluate the relative performance of SeLeCT with respect to two other cohesion based segmenters: TextTiling and C99. Using a recently introduced evaluation metric WindowDiff, we contrast the segmentation accuracy of each system on both "spoken" (CNN news transcripts) and "written" (Reuters newswire) news story test sets extracted from the TDT1 corpus

    Form, science, and narrative in the anthropocene

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    A significant strand of contemporary fiction engages with scientific models that highlight a constitutive interdependency between humanity and material realities such as the climate or the geological history of our planet. This article looks at the ways in which narrative may capture this human-nonhuman interrelation, which occupies the foreground of debates on the so-called Anthropocene. I argue that the formal dimension of scientific knowledge-as manifested by diagrams or metaphors used by scientists-is central to this narrative remediation. I explore two analogical strategies through which narrative may pursue a formal dialogue with science: clusters of metaphorical language and the global structuring of the plot. Rivka Galchen's novel Atmospheric Disturbances (2008), for instance, builds on a visual representation of meteorological patterns in a storm (lifted from an actual scientific paper) to stage the narrator's mental illness. Two other contemporary works (Orfeo by Richard Powers and A Tale for the Time Being by Ruth Ozeki) integrate scientific models through the overall design of the plot. By offering close readings of these novels, I seek to expand work in the area of New Formalism and show how formal choices are crucial to bringing together the human-scale world and more-than-human phenomena

    National Curriculum for English Key Stages 1 and 2 – draft : National Curriculum review

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    National Curriculum for English key stages 1 and 2 : draft : National Curriculum review

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    Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text

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    The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding. This paper introduces the task of identifying if a desire expressed by a subject in a given short piece of text was fulfilled. We propose various unstructured and structured models that capture fulfillment cues such as the subject's emotional state and actions. Our experiments with two different datasets demonstrate the importance of understanding the narrative and discourse structure to address this task
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