5,998 research outputs found

    Isomorphy and Syntax-Prosody Relations in English

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    abstract: This dissertation investigates the precise degree to which prosody and syntax are related. One possibility is that the syntax-prosody mapping is one-to-one (“isomorphic”) at an underlying level (Chomsky & Halle 1968, Selkirk 1996, 2011, Ito & Mester 2009). This predicts that prosodic units should preferably match up with syntactic units. It is also possible that the mapping between these systems is entirely non-isomorphic, with prosody being influenced by factors from language perception and production (Wheeldon & Lahiri 1997, Lahiri & Plank 2010). In this work, I argue that both perspectives are needed in order to address the full range of phonological phenomena that have been identified in English and related languages, including word-initial lenition/flapping, word-initial segment-deletion, and vowel reduction in function words, as well as patterns of pitch accent assignment, final-pronoun constructions, and the distribution of null complementizer allomorphs. In the process, I develop models for both isomorphic and non-isomorphic phrasing. The former is cast within a Minimalist syntactic framework of Merge/Label and Bare Phrase Structure (Chomsky 2013, 2015), while the latter is characterized by a stress-based algorithm for the formation of phonological domains, following Lahiri & Plank (2010).Dissertation/ThesisDoctoral Dissertation English 201

    Investigating the build-up of precedence effect using reflection masking

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    The auditory processing level involved in the build‐up of precedence [Freyman et al., J. Acoust. Soc. Am. 90, 874–884 (1991)] has been investigated here by employing reflection masked threshold (RMT) techniques. Given that RMT techniques are generally assumed to address lower levels of the auditory signal processing, such an approach represents a bottom‐up approach to the buildup of precedence. Three conditioner configurations measuring a possible buildup of reflection suppression were compared to the baseline RMT for four reflection delays ranging from 2.5–15 ms. No buildup of reflection suppression was observed for any of the conditioner configurations. Buildup of template (decrease in RMT for two of the conditioners), on the other hand, was found to be delay dependent. For five of six listeners, with reflection delay=2.5 and 15 ms, RMT decreased relative to the baseline. For 5‐ and 10‐ms delay, no change in threshold was observed. It is concluded that the low‐level auditory processing involved in RMT is not sufficient to realize a buildup of reflection suppression. This confirms suggestions that higher level processing is involved in PE buildup. The observed enhancement of reflection detection (RMT) may contribute to active suppression at higher processing levels

    Controversy trend detection in social media

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    In this research, we focus on the early prediction of whether topics are likely to generate significant controversy (in the form of social media such as comments, blogs, etc.). Controversy trend detection is important to companies, governments, national security agencies, and marketing groups because it can be used to identify which issues the public is having problems with and develop strategies to remedy them. For example, companies can monitor their press release to find out how the public is reacting and to decide if any additional public relations action is required, social media moderators can moderate discussions if the discussions start becoming abusive and getting out of control, and governmental agencies can monitor their public policies and make adjustments to the policies to address any public concerns. An algorithm was developed to predict controversy trends by taking into account sentiment expressed in comments, burstiness of comments, and controversy score. To train and test the algorithm, an annotated corpus was developed consisting of 728 news articles and over 500,000 comments on these articles made by viewers from CNN.com. This study achieved an average F-score of 71.3% across all time spans in detection of controversial versus non-controversial topics. The results suggest that it is possible for early prediction of controversy trends leveraging social media
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