Deriving Meaning: A Conceptual Analysis

Deriving Meaning From the Data

Conceptual Meaning of Clusters

The interpretation of cluster analysis solutions in the case of objectattribute data can be supported by methods of Formal Concept Analysis leading to a conceptual understanding of the "meaning" of clusters, partitions and dendrograms. The central idea is the embedding of a given cluster in a conceptual scale which represents the user's granularity with respect to the values of attributes in th...

Deriving Meaning Vectors from Large Corpora

A neural network model for deriving meaning vectors for words from information retrieval based document vectors is described. A small scale experiment is performed using the model and results compared with an actual thesaurus. Although the results are not good, they show that a full scale experiment may be worth investigating to determine if the poor results are due to the model itself or the v...

Deriving Verb-Meaning Clusters From Syntactic Structure

This paper presents a methodology for using the argument structure of sentences, as encoded by the PropBank project, to develop clusters of verbs with similar meaning and usage. These clusters can be favorably compared to the classes developed by the VerbNet project. The most interesting cases are those where the clustering methodology suggests new members for VerbNet classes which will then be...

Deriving Design Aspects from Conceptual Models

Two fundamental issues in aspect orientation are the identification and the composition of aspects. We argue that aspects must be identified at the requirement and the domain analysis phases. We also propose a mechanism for gradually composing aspects throughout the software development process. We illustrate our ideas for the design of a transaction framework.