43 citations · 104 across the 9 of their papers we have counts for
9 papers
Common TF-IDF variants arise as key components in the test statistic of a penalized likelihood-ratio test for word burstiness
Zeyad Ahmed, Paul Sheridan, Michael McIsaac +1
TF-IDF is a classical formula that is widely used for identifying important terms within documents. We show that TF-IDF-like scores arise naturally from the test statistic of a pen…
Nonparametric estimation of the preferential attachment function from one network snapshot
Thong Pham, Paul Sheridan, Hidetoshi Shimodaira
Preferential attachment is commonly invoked to explain the emergence of those heavy-tailed degree distributions characteristic of growing network representations of diverse real-wo…
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks
Paul Sheridan, Mikael Onsjö
Term frequency-inverse document frequency, or TF-IDF for short, and its many variants form a class of term weighting functions the members of which are widely used in text analysis…
The Literary Theme Ontology for Media Annotation and Information Retrieval
Paul Sheridan, Mikael Onsjö, Janna Hastings
Literary theme identification and interpretation is a focal point of literary studies scholarship. Classical forms of literary scholarship, such as close reading, have flourished w…
An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
Paul Sheridan, Mikael Onsjö, Claudia Becerra +2
Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filteri…
Theme Enrichment Analysis: A Statistical Test for Identifying Significantly Enriched Themes in a List of Stories with an Application to the Star Trek Television Franchise
Mikael Onsjö, Paul Sheridan
In this paper, we describe how the hypergeometric test can be used to determine whether a given theme of interest occurs in a storyset at a frequency more than would be expected by…
PAFit: an R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
Thong Pham, Paul Sheridan, Hidetoshi Shimodaira
Many real-world systems are profitably described as complex networks that grow over time. Preferential attachment and node fitness are two simple growth mechanisms that not only ex…
A Preferential Attachment Paradox: How Preferential Attachment Combines with Growth to Produce Networks with Log-normal In-degree Distributions
Paul Sheridan, Taku Onodera
Every network scientist knows that preferential attachment combines with growth to produce networks with power-law in-degree distributions. How, then, is it possible for the networ…
A preferential attachment model with Poisson growth for scale-free networks
Paul Sheridan, Yuichi Yagahara, Hidetoshi Shimodaira
We propose a scale-free network model with a tunable power-law exponent. The Poisson growth model, as we call it, is an offshoot of the celebrated model of Barabási and Albert wher…