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Most modern NLP methods are built on top of word embeddings, things like Neural Machine Translation, text classification and etc all convert input words into wo
by m_ke 8y ago
Most modern NLP methods are built on top of word embeddings, things like Neural Machine Translation, text classification and etc all convert input words into word embeddings then stack a neural networks on top of it.
- PaulHoule 8y agoPeople write papers about these things. "Do they make commercial applications?" is the question. The paradigm here is "Does method X get better answers than method Y?" as opposed to "Will we embarass ourselves if we put method Z into production?"
- m_ke 8y agoAll of those things are used daily by billions of people around the world. Google sells their translation system through Google cloud, so does AWS and a bunch of other NLP startups.
- PaulHoule 8y agoCan you point me to a text analysis API that is not embarrassingly bad? I have tried products from Amazon, IBM, Google and other companies and the one thing they have in common is they've never passed an acceptance test for me. Language translation is a particular bad example of something where a "Clever Hans" effect can happen, where the reader's desire and capability for closure will fill in for mistakes the system makes and make it look like it performs better than it really does.
- nl 8y agoI don’t use off the shelf APIs, but I’d be surprised if Google’s named entity recogniser isn’t pretty good in the news or financial domain. OpenCalais is pretty good in the financial domain. http://www.opencalais.com/opencalais-demo/ http://www.opencalais.com/opencalais-demo/ IBM isn’t great. Don’t know about Amazon.
- singhrac 8y agoThat's true, but specifically for translation I think all SOTA methods use task-specific embeddings (i.e., learn their own instead of using BERT or Elmo, etc.). See https://arxiv.org/pdf/1808.09381.pdf https://arxiv.org/pdf/1808.09381.pdf and note that the BERT paper doesn't mention translation as a metric.