The diagnosis of a nasopharyngeal mass lesion should be considered in neonates with nasal obstructive symptoms. It is wise to place an index finger in the oropharynx when passing catheters to rule out choanal atresia to feel a dislodged mass lesion before it can become an airway foreign body. Should passage of nasal catheters result in bleeding and/or respiratory distress, the possibility of a displaced mass lesion must be considered immediately to institute prompt intervention.
We present an unsupervised model of dialogue act sequences in conversation. By modeling topical themes as transitioning more slowly than dialogue acts in conversation, our model de-emphasizes contentrelated words in order to focus on conversational function words that signal dialogue acts. We also incorporate speaker tendencies to use some acts more than others as an additional predictor of dialogue act prevalence beyond temporal dependencies. According to the evaluation presented on two dissimilar corpora, the CNET forum and NPS Chat corpus, the effectiveness of each modeling assumption is found to vary depending on characteristics of the data. De-emphasizing contentrelated words yields improvement on the CNET corpus, while utilizing speaker tendencies is advantageous on the NPS corpus. The components of our model complement one another to achieve robust performance on both corpora and outperform state-of-the-art baseline models.
Code-switching has been found to have social motivations in addition to syntactic constraints. In this work, we explore the social effect of code-switching in an online community. We present a task from the Arabic Wikipedia to capture language choice, in this case code-switching between Arabic and other languages, as a predictor of social influence in collaborative editing. We find that code-switching is positively associated with Wikipedia editor success, particularly borrowing technical language on pages with topics less directly related to Arabic-speaking regions.
Moderators are believed to play a crucial role in ensuring the quality of discussion in online political debate forums. The line between moderation and illegitimate censorship, where certain views or individuals are unfairly suppressed, however, is often difficult to define. To better understand the relationship between moderation and censorship, we investigate whether users' perception of moderator bias is supported by how moderators act, using the Big Issues Debate (BID) group on Ravelry as our platform of study. We present our method for measuring bias while taking into account the posting behavior of a user, then apply our method to investigate whether moderators make decisions biased against viewpoints that they may have the incentive to suppress. We find evidence to suggest that while moderators may make decisions biased against individuals with unpopular viewpoints, the effect of this bias is small and often overblown by the users experiencing bias.We argue that the perception of bias by itself is an issue in online political discussions and suggest technological interventions to counteract the discrepancy between perceived and actual censorship in moderation.
Fanfiction presents an opportunity as a data source for research in NLP, education, and social science. However, answering specific research questions with this data is difficult, since fanfiction contains more diverse writing styles than formal fiction. We present a text processing pipeline for fanfiction, with a focus on identifying text associated with characters. The pipeline includes modules for character identification and coreference, as well as the attribution of quotes and narration to those characters. Additionally, the pipeline contains a novel approach to character coreference that uses knowledge from quote attribution to resolve pronouns within quotes. For each module, we evaluate the effectiveness of various approaches on 10 annotated fanfiction stories. This pipeline outperforms tools developed for formal fiction on the tasks of character coreference and quote attribution.
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