I smell a $30 million acquisition in the near future..
By those standards, many companies will be worth billions ;). (Chunking is not exactly new and PCFG parsing is pretty fast these days.)
An Efficient Way to Extract the Main Topics from a Sentence
61–70 of 78 posts
Re: An Efficient Way to Extract the Main Topics from a Sentence
#62Re: An Efficient Way to Extract the Main Topics from a Sentence
#63Earlier quoted context omitted.
I think people underestimate how explicitly-programmed human language is in humans. I'm starting to think that this might be the central problem in NLP right now. Humans have good natural pattern-matching engines in their heads, but the entire body of syntax and vocabulary available to a person is the result of the memorization of a huge amount of text. I suspect the majority of people rarely ever develop truly novel…
>> I recently started work on an attempt to improve the classification of English vocabulary by grade level. I would be interested in this. Let me know if you plan to open this. What data sources are you using?
The data sources aren't that interesting. After trying for a while to find something already pre-compiled, I quit and resorted to Googling for phrases like, "9th grade spelling list", and aggregating the data from the results by hand. There are a bunch of sites for teachers and home educators and the like that include tables of vocabulary for various grades. It's tedious, but it works.
Re: An Efficient Way to Extract the Main Topics from a Sentence
#64This is neat. Shlomib, you might be interested in SHRDLU [1][2] if you are not aware of it. It was developed by Terry Winograd [3] for his dissertation [4] at MIT. It is a natural language understanding [5] parser that allows you to interact with a small world of 3D solids. I think you will find the paper interesting, because it goes into detail on sentence structure and associated parsing. Here is a sample dialogue…
Re: An Efficient Way to Extract the Main Topics from a Sentence
#65------
- "math final today 6-17-09 piece of cake hopefully i should do well since i m a math nerd amp english amp social"
- math, nerd, studies, piece, cake, english, amp, final, hopefully, social, since, should, well, today, do, of,
------
- "anyone want an incredibly designed unique limited edition tee for the summer check out www artcotic com"
- tee, designed, incredibly, unique, edition, limited, summer, anyone, check, www, want, com, an, out, for, the,
Re: An Efficient Way to Extract the Main Topics from a Sentence
#66Re: An Efficient Way to Extract the Main Topics from a Sentence
#67Earlier quoted context omitted.
>> I recently started work on an attempt to improve the classification of English vocabulary by grade level. I would be interested in this. Let me know if you plan to open this. What data sources are you using?
I'll provide an API for it, won't be ready for months though. It's not a big priority yet -- part of a larger project. The data sources aren't that interesting. After trying for a while to find something already pre-compiled, I quit and resorted to Googling for phrases like, "9th grade spelling list", and aggregating the data from the results by hand. There are a bunch of sites for teachers and home educators and the…
Re: An Efficient Way to Extract the Main Topics from a Sentence
#68Without having Brown and NLTK in Node.js, I'm not sure how well I can add this to my port of shlomibs original code. For those who haven't seen yet, I wrote a port of the first part of this here https://github.com/jbrooksuk/node-summary Maybe later I'll give it a crack :)
https://github.com/NaturalNode/natural
Re: An Efficient Way to Extract the Main Topics from a Sentence
#69Earlier quoted context omitted.
SHRDLU is definitely amazing, especially given its age, but one's amazement is tempered a little bit (or maybe enhanced, depending on perspective) when you realize that it achieved what it did primarily through really great engineering rather that some fundamental insight about language. Since SHRDLU's world is so limited, Winograd was able to explicitly program every facet of its language understanding. Unsurprising…
I think people underestimate how explicitly-programmed human language is in humans. I'm starting to think that this might be the central problem in NLP right now. Humans have good natural pattern-matching engines in their heads, but the entire body of syntax and vocabulary available to a person is the result of the memorization of a huge amount of text. I suspect the majority of people rarely ever develop truly novel…
Re: An Efficient Way to Extract the Main Topics from a Sentence
#70The sentence subject is one thing, the sentence topic might be quite another. Consider sentences like: "He joined the not-yet-famous Liverpool band in early 1958." To many human beings the topic is quickly obvious. Parsing won't do the trick.
The only reason that sentence is "obvious" to many people is because we have a reference to a famous band from Liverpool that got its start in the late 50's/early 60's that is already embedded in our brain's library of facts. Removed from that context human beings see that sentence as equally meaningless as a parser, because it is. I'd imagine many young people (who don't have the "correct" reference points) wouldn't…
http://en.wikipedia.org/wiki/Topic%E2%80%93comment#Definitio... e.g. in the sentence (3) As for the little girl, the dog bit her "the dog" is the subject NP, "the little girl" is the topic. That toy example is perfectly parsable without semantics or even probabilities (though take any real-world sentence and I'm betting you'll need more than just grammar).