49,842 research outputs found
Linguistic Geometries for Unsupervised Dimensionality Reduction
Text documents are complex high dimensional objects. To effectively visualize
such data it is important to reduce its dimensionality and visualize the low
dimensional embedding as a 2-D or 3-D scatter plot. In this paper we explore
dimensionality reduction methods that draw upon domain knowledge in order to
achieve a better low dimensional embedding and visualization of documents. We
consider the use of geometries specified manually by an expert, geometries
derived automatically from corpus statistics, and geometries computed from
linguistic resources.Comment: 13 pages, 15 figure
Non-Standard Words as Features for Text Categorization
This paper presents categorization of Croatian texts using Non-Standard Words
(NSW) as features. Non-Standard Words are: numbers, dates, acronyms,
abbreviations, currency, etc. NSWs in Croatian language are determined
according to Croatian NSW taxonomy. For the purpose of this research, 390 text
documents were collected and formed the SKIPEZ collection with 6 classes:
official, literary, informative, popular, educational and scientific. Text
categorization experiment was conducted on three different representations of
the SKIPEZ collection: in the first representation, the frequencies of NSWs are
used as features; in the second representation, the statistic measures of NSWs
(variance, coefficient of variation, standard deviation, etc.) are used as
features; while the third representation combines the first two feature sets.
Naive Bayes, CN2, C4.5, kNN, Classification Trees and Random Forest algorithms
were used in text categorization experiments. The best categorization results
are achieved using the first feature set (NSW frequencies) with the
categorization accuracy of 87%. This suggests that the NSWs should be
considered as features in highly inflectional languages, such as Croatian. NSW
based features reduce the dimensionality of the feature space without standard
lemmatization procedures, and therefore the bag-of-NSWs should be considered
for further Croatian texts categorization experiments.Comment: IEEE 37th International Convention on Information and Communication
Technology, Electronics and Microelectronics (MIPRO 2014), pp. 1415-1419,
201
Evaluation of linear classifiers on articles containing pharmacokinetic evidence of drug-drug interactions
Background. Drug-drug interaction (DDI) is a major cause of morbidity and
mortality. [...] Biomedical literature mining can aid DDI research by
extracting relevant DDI signals from either the published literature or large
clinical databases. However, though drug interaction is an ideal area for
translational research, the inclusion of literature mining methodologies in DDI
workflows is still very preliminary. One area that can benefit from literature
mining is the automatic identification of a large number of potential DDIs,
whose pharmacological mechanisms and clinical significance can then be studied
via in vitro pharmacology and in populo pharmaco-epidemiology. Experiments. We
implemented a set of classifiers for identifying published articles relevant to
experimental pharmacokinetic DDI evidence. These documents are important for
identifying causal mechanisms behind putative drug-drug interactions, an
important step in the extraction of large numbers of potential DDIs. We
evaluate performance of several linear classifiers on PubMed abstracts, under
different feature transformation and dimensionality reduction methods. In
addition, we investigate the performance benefits of including various
publicly-available named entity recognition features, as well as a set of
internally-developed pharmacokinetic dictionaries. Results. We found that
several classifiers performed well in distinguishing relevant and irrelevant
abstracts. We found that the combination of unigram and bigram textual features
gave better performance than unigram features alone, and also that
normalization transforms that adjusted for feature frequency and document
length improved classification. For some classifiers, such as linear
discriminant analysis (LDA), proper dimensionality reduction had a large impact
on performance. Finally, the inclusion of NER features and dictionaries was
found not to help classification.Comment: Pacific Symposium on Biocomputing, 201
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