Knowledge Extraction from Unstructured Texts (2016)
Interesting! I use a similar technique on <a href="https://hnprofile.com" rel="nofollow">https://hnprofile.com</a><p>It differs in a few key ways, largely because I’m trying to solve a different problem.<p><a href="https://hnprofile.com/learn-more" rel="nofollow">https://hnprofile.com/learn-more</a><p>I’m curious how this solution for knowledge extraction, vs the goal of summarization holds up in practice. I suspect (as they mention on the conclusion), even though data is extracted, timing is everything. Language and information changes over time and I’m curious how multiple sets of the similar, but different data in the corpus would be represented. For instance, “I live in Japan”, vs “I lived in Japan” vs “we live in Japan” but “I visit New York”<p>Where do I live, who are “we”, etc are still very difficult problems. Summarizations are a bit easier in that for the most part you’re just removing superfluous information.