Logical fallacy detection and/or identification with natural-language-processing

Question

Is there a package or methodology in existence for the detection of flawed logical arguments in text?

I was hoping for something that would work for text that is not written in an academic setting (such as a logic class). It might be a stretch but I would like something that can identify where logic is trying to be used and identify the logical error. A possible use for this would be marking errors in editorial articles.

I don't need anything that is polished. I wouldn't mind working to develop something either so I'm really looking for what's out there in the wild now.

Arnold

Answer 1

That's a difficult problem, because you'll have to map natural language to some logical representation, and deal with ambiguity in the process.

Attempto Project may be interesting for you. It has several tools that you can try online. In particular, RACE may be doing something you wanted to do. It checks for consistency on the given assertions. But the bigger issue here is in transforming them to logical forms.

Malleus

Answer 2

For an onology of logical axioms, OpenCyc and the commercial full Cyc ontologies might be worth investigating as well. CycML is used as a language to model the logical assertions, and the Cyc engine is capable of logical inference. The source for OpenCyc can be found in the OpenCyc SourceForge project. The Cyc Wikipedia page also has great information.

Benedetto

Answer 3

Yes, this is a very nasty problem. I would suggest you try to focus in on a narrow domain. For example, if you are looking for logic errors in cancer determination, you have to focus on which type of cancer as well as what are you trying to resolve eg: correct treatment plans, correct observations, correct procedures, correct stage determination, etc. Then you have to find the taxonomy or ontology for that specific cancer, eg: Medline. So for example, you will likely have to focus in on ONLY lung cancer and then only a subset of lung cancer types and only observations indicating lung cancer. Then you will have identify your corpus, knowledge trees, entity relationships and then worry about negation detection, hypotheticals and subject detection. If Healthcare doesn float your boat, I hear another challenging domain for logic errors is the legal/law industry.

Revareval

How does this answer the OP question? It is merely a suggestion to focus more on some point, but the OP asks for package or methodology. I don't think you provide a methodology for "natural-language processing". I think you should revise your answer to better deal with what he's asking – Englut