8,717 research outputs found
An Investigation into the Pedagogical Features of Documents
Characterizing the content of a technical document in terms of its learning
utility can be useful for applications related to education, such as generating
reading lists from large collections of documents. We refer to this learning
utility as the "pedagogical value" of the document to the learner. While
pedagogical value is an important concept that has been studied extensively
within the education domain, there has been little work exploring it from a
computational, i.e., natural language processing (NLP), perspective. To allow a
computational exploration of this concept, we introduce the notion of
"pedagogical roles" of documents (e.g., Tutorial and Survey) as an intermediary
component for the study of pedagogical value. Given the lack of available
corpora for our exploration, we create the first annotated corpus of
pedagogical roles and use it to test baseline techniques for automatic
prediction of such roles.Comment: 12th Workshop on Innovative Use of NLP for Building Educational
Applications (BEA) at EMNLP 2017; 12 page
Co-Following on Twitter
We present an in-depth study of co-following on Twitter based on the
observation that two Twitter users whose followers have similar friends are
also similar, even though they might not share any direct links or a single
mutual follower. We show how this observation contributes to (i) a better
understanding of language-agnostic user classification on Twitter, (ii)
eliciting opportunities for Computational Social Science, and (iii) improving
online marketing by identifying cross-selling opportunities.
We start with a machine learning problem of predicting a user's preference
among two alternative choices of Twitter friends. We show that co-following
information provides strong signals for diverse classification tasks and that
these signals persist even when (i) the most discriminative features are
removed and (ii) only relatively "sparse" users with fewer than 152 but more
than 43 Twitter friends are considered.
Going beyond mere classification performance optimization, we present
applications of our methodology to Computational Social Science. Here we
confirm stereotypes such as that the country singer Kenny Chesney
(@kennychesney) is more popular among @GOP followers, whereas Lady Gaga
(@ladygaga) enjoys more support from @TheDemocrats followers.
In the domain of marketing we give evidence that celebrity endorsement is
reflected in co-following and we demonstrate how our methodology can be used to
reveal the audience similarities between Apple and Puma and, less obviously,
between Nike and Coca-Cola. Concerning a user's popularity we find a
statistically significant connection between having a more "average"
followership and having more followers than direct rivals. Interestingly, a
\emph{larger} audience also seems to be linked to a \emph{less diverse}
audience in terms of their co-following.Comment: full version of a short paper at Hypertext 201
ICAP: An Interactive Cluster Analysis Procedure for analyzing remotely sensed data
An Interactive Cluster Analysis Procedure (ICAP) was developed to derive classifier training statistics from remotely sensed data. The algorithm interfaces the rapid numerical processing capacity of a computer with the human ability to integrate qualitative information. Control of the clustering process alternates between the algorithm, which creates new centroids and forms clusters and the analyst, who evaluate and elect to modify the cluster structure. Clusters can be deleted or lumped pairwise, or new centroids can be added. A summary of the cluster statistics can be requested to facilitate cluster manipulation. The ICAP was implemented in APL (A Programming Language), an interactive computer language. The flexibility of the algorithm was evaluated using data from different LANDSAT scenes to simulate two situations: one in which the analyst is assumed to have no prior knowledge about the data and wishes to have the clusters formed more or less automatically; and the other in which the analyst is assumed to have some knowledge about the data structure and wishes to use that information to closely supervise the clustering process. For comparison, an existing clustering method was also applied to the two data sets
Six Noise Type Military Sound Classifier
Blast noise from military installations often has a negative impact on the quality of life of residents living in nearby communities. This negatively impacts the military's testing \& training capabilities due to restrictions, curfews, or range closures enacted to address noise complaints. In order to more directly manage noise around military installations, accurate noise monitoring has become a necessity. Although most noise monitors are simple sound level meters, more recent ones are capable of discerning blasts from ambient noise with some success. Investigators at the University of Pittsburgh previously developed a more advanced noise classifier that can discern between wind, aircraft, and blast noise, while simultaneously lowering the measurement threshold. Recent work will be presented from the development of a more advanced classifier that identifies additional classes of noise such as machine gun fire, vehicles, and thunder. Additional signal metrics were explored given the increased complexity of the classifier. By broadening the types of noise the system can accurately classify and increasing the number of metrics, a new system was developed with increased blast noise accuracy, decreased number of missed events, and significantly fewer false positives
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