title: Domain-specific keyphrase extraction

authors: Frank, Eibe

subjects: computer science

abstract: Keyphrases are an important means of document summarization, clustering, and topic search. Only a small minority of documents have author-assigned keyphrases, and manually assigning keyphrases to existing documents is very laborious. Therefore it is highly desirable to automate the keyphrase extraction process.
This paper shows that a simple procedure for keyphrase extraction based on the naive Bayes learning scheme performs comparably to the state of the art. It goes on to explain how this procedure's performance can be boosted by automatically tailoring the extraction process to the particular document collection at hand. Results on a large collection of technical reports in computer science show that the quality of the extracted keyphrases improves significantly when domain-specific information is exploited.

text: Domain-specific keyphrase extraction Keyphrases are an important means of document summarization, clustering, and topic search. Only a small minority of documents have author-assigned keyphrases, and manually assigning keyphrases to existing documents is very laborious. Therefore it is highly desirable to automate the keyphrase extraction process.
This paper shows that a simple procedure for keyphrase extraction based on the naive Bayes learning scheme performs comparably to the state of the art. It goes on to explain how this procedure's performance can be boosted by automatically tailoring the extraction process to the particular document collection at hand. Results on a large collection of technical reports in computer science show that the quality of the extracted keyphrases improves significantly when domain-specific information is exploited.

year: 1999

title: Fully supervised training of Gaussian radial basis function networks in WEKA

authors: Frank, Eibe

subjects: Machine learning

abstract: Radial basis function networks are a type of feedforward network with a long history in machine learning. In spite of this, there is relatively little literature on how to train them so that accurate predictions are obtained. A common strategy is to train the hidden layer of the network using k-means clustering and the output layer using supervised learning. However, Wettschereck and Dietterich found that supervised training of hidden layer parameters can improve predictive performance. They investigated learning center locations, local variances of the basis functions, and attribute weights, in a supervised manner.

This document discusses supervised training of Gaussian radial basis function networks in the WEKA machine learning software. More specifically, we discuss the RBFClassifier and RBFRegressor classes available as part of the RBFNetwork package for WEKA 3.7 and consider (a) learning of center locations and one global variance parameter, (b) learning of center locations and one local variance parameter per basis function, and (c) learning center locations with per-attribute local variance parameters. We also consider learning attribute weights jointly with other parameters.

text: Fully supervised training of Gaussian radial basis function networks in WEKA Radial basis function networks are a type of feedforward network with a long history in machine learning. In spite of this, there is relatively little literature on how to train them so that accurate predictions are obtained. A common strategy is to train the hidden layer of the network using k-means clustering and the output layer using supervised learning. However, Wettschereck and Dietterich found that supervised training of hidden layer parameters can improve predictive performance. They investigated learning center locations, local variances of the basis functions, and attribute weights, in a supervised manner.

This document discusses supervised training of Gaussian radial basis function networks in the WEKA machine learning software. More specifically, we discuss the RBFClassifier and RBFRegressor classes available as part of the RBFNetwork package for WEKA 3.7 and consider (a) learning of center locations and one global variance parameter, (b) learning of center locations and one local variance parameter per basis function, and (c) learning center locations with per-attribute local variance parameters. We also consider learning attribute weights jointly with other parameters.

year: 2014

title: Are we really colour-blind? The normalisation of mass female incarceration

authors: Norris, Adele N.

abstract: The sharp rise in female incarceration rates in both the United States and New Zealand has received increased attention. Even more pressing are the racial disparities among imprisoned females. This exploratory case study examines 13 peer-reviewed articles published between 2005 and 2016 to understand the nature of colour-blind ideology in discussions of female imprisonment in New Zealand. Several themes emerged including the homogenisation of female prisoners. Apart from moderately linking vast racial disparities between incarcerated White women and Indigenous women to ill-defined colonial practices, contemporary explanations for the substantial racial disparities receive little attention. This article concludes that the absence of a critical lens toward contemporary forms and experiences of racism undergirding the mass criminalisation of Indigenous people perpetuates a colour blindness that in turn works to normalise mass female incarceration. Even in attempts to be unbiased, the way race/ethnicity, gender, age, and class are discussed in academic research exploring female incarceration seems to reflect the influential nature of controlling images rather than critique them.

text: Are we really colour-blind? The normalisation of mass female incarceration The sharp rise in female incarceration rates in both the United States and New Zealand has received increased attention. Even more pressing are the racial disparities among imprisoned females. This exploratory case study examines 13 peer-reviewed articles published between 2005 and 2016 to understand the nature of colour-blind ideology in discussions of female imprisonment in New Zealand. Several themes emerged including the homogenisation of female prisoners. Apart from moderately linking vast racial disparities between incarcerated White women and Indigenous women to ill-defined colonial practices, contemporary explanations for the substantial racial disparities receive little attention. This article concludes that the absence of a critical lens toward contemporary forms and experiences of racism undergirding the mass criminalisation of Indigenous people perpetuates a colour blindness that in turn works to normalise mass female incarceration. Even in attempts to be unbiased, the way race/ethnicity, gender, age, and class are discussed in academic research exploring female incarceration seems to reflect the influential nature of controlling images rather than critique them.

year: 2017