@misc{ROCMUILAML19,
author = {Anne Lorenz},
title = {{Recognition of Company Mergers Using Interactive Labeling and Machine Learning Methods}},
advisors = {Julian Kunkel and Doris Birkefeld},
year = {2019},
month = {05},
school = {Universität Hamburg},
howpublished = {{Online \url{{{:research:theses:anne_lorenz_recognition_of_company_mergers_using_interactive_labeling_and_machine_learning_methods.pdf|Thesis}}}}},
type = {Bachelor's Thesis},
abstract = {Being informed about current business events is essential for decision makers in a company.
The aim of this thesis is to develop a strategy for recognizing company mergers in news articles
by applying common and experimental Machine Learning methods. For this text classification
problem, an interactive human-computer labeling technique is explored in order to manually
label an unclassified data set in a more efficient way. Using class probabilities based on
the Naive Bayes algorithm, the iterative approach accelerates the data labeling process by
propagating labels through the data set. Through experimental research on this practical
application problem, it is found that the proposed labeling technique is eight times faster
than conventional labeling, because the number of articles to be labeled can be reduced. On
the resulting data set, a common Support Vector Machines model achieves a Recall score of
86.9\% and a Precision score of 86.1\%. The presented incremental method that is simple to
implement is not only suitable for text classification problems, but universal for all kinds of
large unclassified data sets, as, e.g., in image classification and speech recognition.},
}