16,123 research outputs found
What Works Better? A Study of Classifying Requirements
Classifying requirements into functional requirements (FR) and non-functional
ones (NFR) is an important task in requirements engineering. However, automated
classification of requirements written in natural language is not
straightforward, due to the variability of natural language and the absence of
a controlled vocabulary. This paper investigates how automated classification
of requirements into FR and NFR can be improved and how well several machine
learning approaches work in this context. We contribute an approach for
preprocessing requirements that standardizes and normalizes requirements before
applying classification algorithms. Further, we report on how well several
existing machine learning methods perform for automated classification of NFRs
into sub-categories such as usability, availability, or performance. Our study
is performed on 625 requirements provided by the OpenScience tera-PROMISE
repository. We found that our preprocessing improved the performance of an
existing classification method. We further found significant differences in the
performance of approaches such as Latent Dirichlet Allocation, Biterm Topic
Modeling, or Naive Bayes for the sub-classification of NFRs.Comment: 7 pages, the 25th IEEE International Conference on Requirements
Engineering (RE'17
From Frequency to Meaning: Vector Space Models of Semantics
Computers understand very little of the meaning of human language. This
profoundly limits our ability to give instructions to computers, the ability of
computers to explain their actions to us, and the ability of computers to
analyse and process text. Vector space models (VSMs) of semantics are beginning
to address these limits. This paper surveys the use of VSMs for semantic
processing of text. We organize the literature on VSMs according to the
structure of the matrix in a VSM. There are currently three broad classes of
VSMs, based on term-document, word-context, and pair-pattern matrices, yielding
three classes of applications. We survey a broad range of applications in these
three categories and we take a detailed look at a specific open source project
in each category. Our goal in this survey is to show the breadth of
applications of VSMs for semantics, to provide a new perspective on VSMs for
those who are already familiar with the area, and to provide pointers into the
literature for those who are less familiar with the field
Using distributional similarity to organise biomedical terminology
We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy
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