Digging up the frequency of phrasal verbs in English for the Police: the case of "up"

The present study focuses on the frequency of phrasal verbs with the particle up in the context of crime and police investigative work. This research emerges from the need to enlarge McCarthy and O’Dell’s (2004) scope from purely criminal behavior to police investigative actions. To do so, we relied on a corpus of 504,124 running words made up of spoken dialogues extracted from the script of the American TV series Castle shown on ABC since 2009. Based on Rudzka-Ostyn’s (2003) cognitive motivations for the particle up, we have identified five different meaning extensions for our phrasal verbs. Drawing from these findings, we have designed pedagogical activities for those L2 learners that study English at the Police Academy.

Machine learning classification of surgical pathology reports and chunk recognition for information extraction noise reduction

Background and aims: Machine learning techniques for the text mining of cancer-related clinical documents have not been sufficiently explored. Here some techniques are presented for the pre-processing of free-text breast cancer pathology reports, with the aim of facilitating the extraction of information relevant to cancer staging.

Materials and methods: The first technique was implemented using the freely available software RapidMiner to classify the reports according to their general layout: ‘semi-structured’ and ‘unstructured’. The second technique was developed using the open source language engineering framework GATE and aimed at the prediction of chunks of the report text containing information pertaining to the cancer morphology, the tumour size, its hormone receptor status and the number of positive nodes. The classifiers were trained and tested respectively on sets of 635 and 163 manually classified or annotated reports, from the Northern Ireland Cancer Registry.

Results: The best result of 99.4% accuracy – which included only one semi-structured report predicted as unstructured – was produced by the layout classifier with the k nearest algorithm, using the binary term occurrence word vector type with stopword filter and pruning. For chunk recognition, the best results were found using the PAUM algorithm with the same parameters for all cases, except for the prediction of chunks containing cancer morphology. For semi-structured reports the performance ranged from 0.97 to 0.94 and from 0.92 to 0.83 in precision and recall, while for unstructured reports performance ranged from 0.91 to 0.64 and from 0.68 to 0.41 in precision and recall. Poor results were found when the classifier was trained on semi-structured reports but tested on unstructured.

Conclusions: These results show that it is possible and beneficial to predict the layout of reports and that the accuracy of prediction of which segments of a report may contain certain information is sensitive to the report layout and the type of information sought.

A Machine Learning Analysis of Twitter Sentiment to the Sandy Hook Shootings

Gun related violence is a complex issue and accounts for a large proportion of violent incidents. In the research reported in this paper, we set out to investigate the pro-gun and anti-gun sentiments expressed on a social media platform, namely Twitter, in response to the 2012 Sandy Hook Elementary School shooting in Connecticut, USA. Machine learning techniques are applied to classify a data corpus of over 700,000 tweets. The sentiments are captured using a public sentiment score that considers the volume of tweets as well as population. A web-based interactive tool is developed to visualise the sentiments and is available at this http://www.gunsontwitter.com. The key findings from this research are: (i) There are elevated rates of both pro-gun and anti-gun sentiments on the day of the shooting. Surprisingly, the pro-gun sentiment remains high for a number of days following the event but the anti-gun sentiment quickly falls to pre-event levels. (ii) There is a different public response from each state, with the highest pro-gun sentiment not coming from those with highest gun ownership levels but rather from California, Texas and New York.

Evaluating statistical and machine learning methods to predict risk of in-hospital child mortality in Uganda

Thesis (Master's)--University of Washington, 2016-08

Dimensionality hyper-reduction and machine learning for dynamical systems with varying parameters

Thesis (Ph.D.)--University of Washington, 2016-08

Machine learning and data decompositions for complex networked dynamical systems

Thesis (Ph.D.)--University of Washington, 2016-08

Modelo predictivo de radiación solar mediante técnicas de machine learning: aplicación a la isla de Gran Canaria

Programa de doctorado: Tecnología Industrial. La fecha de publicación es la fecha de lectura.

Essays on Machine Learning and Hedonic Models

Thesis (Ph.D.)--University of Washington, 2016-08

A heuristic algorithm for the two-machine re-entrant shop scheduling

Abstract not available

Heuristics for the two-stage job shop scheduling problem with a bottleneck machine

Abstract not available

Earliness penalties on a single machine subject to precedence constraints: SLK due date assignment

Abstract not available

A polynomial algorithm for the three-machine open shop with a bottleneck machine

Abstract not available

Ghost Machine: a Sound Performance

Ghost Machine is an encounter between a person and a machine in a suburban shed. The machine reveals the story of a man haunted by an image that seems to shift and change as it sits on the wall of his study. In an attempt to locate the origins of the image he builds a viewing machine to finally confront it. Ghost Machine is based on The Mezzotint (1904) by M.R James, retold as a suburban ghost story. It was part of SENSE at Mayfest in Bristol between 24th - 26th May 2013.