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Comment by messel | Hacker News Reader

Thanks I'll check it out. I've been using a simple approach of allowing folks like Zemanta to extract meta tags from content (natural language text), weighting them by confidence levels or reuse and then finding similar combinations/weights. Link by content.

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I'm not sure exactly what you mean, are you talking about entity extraction with confidence levels ala Calais? That used to be clearforest before Reuters bought them. I think entity extraction in general is so closely related to natural language quantification that it is one of those generally tough problems. In general, I feel work in this area suffers scalability issues and highly optimized point solutions will do better for a while yet. I guess we'll have to see how Bing ne Powerset does in mainstreaming this whole thing or if it'll be just a bunch of hype like twine.

Yup, Calais & Zemanta have similar APIs. Input text and get back tags.

It's much more than tags - in-text exact entities, deep categorization, related news, etc :)

The exact disambiguation of mentioned concepts and entities is what is exciting and underused by web developers right now.

Andraz Tori, CTO at Zemanta

I think sense disambiguation is certainly one of the most interesting applications of semantic technologies. Here at TQ, the focus tends to be on the exploitation of referential semantics and structured data to establish master data models, merge data models and provide a platform for model driven application development and analytics. However, the focus here tends to be more towards corporate IT than mainstream web dev.

How best do you think this can be leveraged by folks that are interested in associating commonality within text (clustering by topic)?

If you need any more help from us (Zemanta), let us know!

we know a lot about these problems & solutions, so send us an email at info@zemanta.com

Andraz Tori, CTO at Zemanta

Thanks Andraz, appreciate your presence here and continued support of your business.

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