Modelling the connectivity between terms in the neuroscience literature

We describe a method to model connectivity patterns between words in a document collection. These connectivity patterns may be helpful to gain more insight in the meaning of the document collection as a whole, in the semantics of the field, or they may be used in other applications like information retrieval, query-refinement, question-answering, etc. Structural equation modelling (SEM) has been used as a statistical technique for modelling the connectivities between terms. Furthermore, in order to validate the goodness-of-fit of the models, we adopt a bootstrapping approach since the data encountered in text mining applications are likely to violate the underlying assumptions of SEM and the calculated test statistics often does follow the theoretical distributions. We applied the described method on a corpus of journal articles taken from the neuroscience literature.