What Happened to Old School NLP?
languagengine.co
What Happened to Old School NLP?
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Re: What Happened to Old School NLP?
#2NetBase (http://www.netbase.com) uses this kind of "old-school" NLP (with a large team of full-time linguistics PhDs) augmented with statistical tools and increasingly sophisticated forms of automation.
The end product is more accurate and quicker to adapt than the industry is used to.
Disclosure: I work in the engineering team at NetBase.
Re: What Happened to Old School NLP?
#3NetBase ( http://www.netbase.com ) uses this kind of "old-school" NLP (with a large team of full-time linguistics PhDs) augmented with statistical tools and increasingly sophisticated forms of automation. The end product is more accurate and quicker to adapt than the industry is used to. Disclosure: I work in the engineering team at NetBase.
awesome! what sorts of things does NetBase do with old school NLP?
Re: What Happened to Old School NLP?
#4NetBase ( http://www.netbase.com ) uses this kind of "old-school" NLP (with a large team of full-time linguistics PhDs) augmented with statistical tools and increasingly sophisticated forms of automation. The end product is more accurate and quicker to adapt than the industry is used to. Disclosure: I work in the engineering team at NetBase.
Cool! The website says netbase supports 42 different languages, but not which languages.
Re: What Happened to Old School NLP?
#5NetBase ( http://www.netbase.com ) uses this kind of "old-school" NLP (with a large team of full-time linguistics PhDs) augmented with statistical tools and increasingly sophisticated forms of automation. The end product is more accurate and quicker to adapt than the industry is used to. Disclosure: I work in the engineering team at NetBase.
Cool! The website says netbase supports 42 different languages, but not which languages.
to find out which 42, we'll need to build a computer the size of a planet
Re: What Happened to Old School NLP?
#6NetBase ( http://www.netbase.com ) uses this kind of "old-school" NLP (with a large team of full-time linguistics PhDs) augmented with statistical tools and increasingly sophisticated forms of automation. The end product is more accurate and quicker to adapt than the industry is used to. Disclosure: I work in the engineering team at NetBase.
awesome! what sorts of things does NetBase do with old school NLP?
Social media analysis of many kinds is our full-time focus right now. It's a rapidly changing field. The demands of customers shift as they become more educated about what to expect from social media and how to push the boundaries of technology.
Re: What Happened to Old School NLP?
#7We'll start to see the re-emergence of tools from old-school NLP, but now augmented with the powerful statistical tools and data-oriented automation of new-school NLP. IBM's Watson already does this to some extent.
This is not a new trend. As early as 1997, Steven Abney augmented [1] attribute-value grammars with discriminative modelling (maximum entropy models) in this case to form 'stochastic attribute-value grammars'. There is a lot of work on efficiently extracting the best parse from packed forests, etc. Most systems that rely on unification grammars (e.g. HPSG grammars) already use stochastic models.
In the early to mid 2000s when the modelling of association strengths using structured or unstructured text became popular, old-school parsers have been adopting such techniques to learn selectional preferences that cannot be learnt from the usually small hand-annotated treebanks. E.g. in languages that normally have SVO (subject-verb-object) for main clauses but also permit OVS order, parsers trained on small hand-annotated treebanks would often be set on the wrong path when the direct object is fronted (analyzing the direct object as subject). Techniques from association strength modelling were used to learn selectional preferences such as 'bread is usually the subject of eat' from automatically annotated text [2].
In recent years, learning word vector representations using neural networks has become popular. Again, not surprisingly, people have been integrating vectors as features in the disambiguation components of old-school NLP parsers. In some cases with great success.
tl;dr, the flow of ideas and tools from new-school NLP to old-school NLP has been going on ever since the statistical NLP revolution started.
Re: What Happened to Old School NLP?
#8The subtle ideas form the original "Structure Of Magic" books about how we construct out internal representations of reality depending of wording we use has been replaced by industry of coaches and consultants.
The ideas, by the way, had been studied by mainstream psychology as the framing effect and the priming effect.
In short, our minds do lexical analysis and decomposition sub-consciously, so we could be influenced by specially crafted sentences. We also leak details of our internal representation of some aspects of reality in the way we unconsciously construct language sentences.
Re: What Happened to Old School NLP?
#9We'll start to see the re-emergence of tools from old-school NLP, but now augmented with the powerful statistical tools and data-oriented automation of new-school NLP. IBM's Watson already does this to some extent. This is not a new trend. As early as 1997, Steven Abney augmented [1] attribute-value grammars with discriminative modelling (maximum entropy models) in this case to form 'stochastic attribute-value gramma…
Yep, it's certainly true that these techniques never went away fully, but they've been eclipsed by the simpler techniques. Academic work, especially under the guise of "computational linguistics" as opposed to "NLP", works a lot on this. But the public face of NLP has been pure new-school stuff for a while now.
I was more trying to just talk about why the richly structured old school fell from grace, not claim that it's dead and buried.
Re: What Happened to Old School NLP?
#10We'll start to see the re-emergence of tools from old-school NLP, but now augmented with the powerful statistical tools and data-oriented automation of new-school NLP. IBM's Watson already does this to some extent. This is not a new trend. As early as 1997, Steven Abney augmented [1] attribute-value grammars with discriminative modelling (maximum entropy models) in this case to form 'stochastic attribute-value gramma…
Yep, it's certainly true that these techniques never went away fully, but they've been eclipsed by the simpler techniques. Academic work, especially under the guise of "computational linguistics" as opposed to "NLP", works a lot on this. But the public face of NLP has been pure new-school stuff for a while now. I was more trying to just talk about why the richly structured old school fell from grace, not claim that i…
The rest of the analysis seemed very much to the point. It was an interesting read, thanks!