AutoMap: Extract, Analyze and Represent Relational Data from Texts (according to its webpage).
From the webpage:
AutoMap is a text mining tool that enables the extraction of network data from texts. AutoMap can extract content analytic data (words and frequencies), semantic networks, and meta-networks from unstructured texts developed by CASOS at Carnegie Mellon. Pre-processors for handling pdf’s and other text formats exist. Post-processors for linking to gazateers and belief inference also exist. The main functions of AutoMap are to extract, analyze, and compare texts in terms of concepts, themes, sentiment, semantic networks and the meta-networks extracted from the texts. AutoMap exports data in DyNetML and can be used interoperably with *ORA.
AutoMap uses parts of speech tagging and proximity analysis to do computer-assisted Network Text Analysis (NTA). NTA encodes the links among words in a text and constructs a network of the linked words.
AutoMap subsumes classical Content Analysis by analyzing the existence, frequencies, and covariance of terms and themes.
For a rough cut at a topic map from a text, AutoMap looks like a useful tool.
In addition to the software, training material and other information is available.
My primary interest is the application of such a tool to legislative debates, legislation and court decisions.
None of those occur in a vacuum and topic maps could help provide a context for understand such material.