This won’t help you do it on a computer, but you might find Stefanie Posavec’s work inspiring:

http://www.stefanieposavec.co.uk/personal/#/the-longest-sent...

Your sentence got passed through Stanford's NLP service and here the result is visualized with d3.js[1]. Your use of the verb to find is quite interesting. Depending on structure it means different things. According to this graph it is a verb phrase (VP) followed by a noun phrase (NP:DObj) and a gerund phrase behaving as an adjective phrase object compliment. The verb to find can function in couple different ways. It can take a noun phrase direct object (NP:DObj) only without an object compliment such as the noun cat, as in to find a cat, with a semantic meaning of discovering something that wasn't there earlier, or it can function like the verb consider which equates a noun direct object (NP:DObj) with an object compliment (ObjComp) which requires, unlike the first use case discussed with only one grammatical structure following it, two grammatical structures following it. What semantic meaning can we get from parsing whether there is one grammatical structure following the verb to find or two grammatical structures. In the first case, a dog can find the cat, but in the second case, a dog can't consider which is a higher order cognitive function that requires reasoning. If I was building AI, I can already using Stanford's NPL service determine programmatically how many grammatical structures follow to find and I can also deduce that the noun before the verb to find in this case you, or me, is something capable of higher level logical reasoning, most likely a person, or, perhaps, a dolphin, a parrot, or IBM's Watson in very rare specific cases.

Any sentence can be parsed into its constituent parts. "You might find Stefanie Posavex's work inspiring" is a free standing grammatical sentence which is used as part of a larger sentence. It can be broken further conceptually into you might find something and Stefanie Posavex's work is inspiring. What I want to do is help students learn to take these last two sentences and mix them together with you and find into a meaningful larger sentence using interactive graphical visualizations.

A verb functioning as consider requiring two grammatical structures following it is one of a small number of ways a verb can behave. There are only a small handful of verbs that function as consider such as to think, to find, and to nickname.

Graphical visualization helps understanding this so much. The visualization I linked to is only proof of concept. If I used more specific labels on the nodes of the graph you would understand the concepts more clearly than me trying to write them. I'm working day and night on this. I'm learning about closures, prototypes, and the difference between pseudo class inheritance and delegation in Javascript. I've only minimally looked at how to work with tree structures. I'm so friggen' far away, I'm just a cook.

[1] http://i.imgur.com/5CHdoOO.png

> I'm not interested in natural language processing with machines and machine learning.

I'm confused, why not? "English grammar visualization" is inherently an NLP problem. Both handwritten and statistically learned grammars are very large and therefore hard to visualize. I think it would be better to focus on a manageable language fragment. Some kind of interactive second language learning environment--but that probably already exists. What is it exactly that you want to do?

BTW: I wonder for how many people "graphing sentences" is an effective approach to language learning.

When I first got to college realizing that I was having a very difficult time with writing, I went to the grammar section in the school's library. After failing to work through a few of the more popular books on grammar that I tried, I discovered an small obscure grammar book. The way the book worked is to first explain a handful of grammar concepts and then have the reader parse several sentences before moving onto more complex concepts. It was so systematic and methodical.

I reviewed the book a couple years ago and out of curiosity searched for one of the concepts online. There were only a small handful of search results and I think the author made up the word, the book is obscure. One of the results linked to an article on the IBM website written by one of the Watson engineers. I wrote him an email because I would imagine their engineers were using much more complicated concepts in linguistic research that a layman like my self would ever understand.

We were interested in two very different things. He is interested in helping a machine understand semantic meaning through grammatical structure while I'm interested in helping people understand semantic meaning through grammatical structure. Sure we are both using the same grammar book to parse a sentence. But how we are doing it is completely different. He is writing a program to do it and I'm writing a program to hold a child's hand while the child does it.

A sentence is a pattern of patterns. For the same reason Mike Bostock's work in visualization helps people understand patterns whether it is about the road to the White House or the road to the Super Bowl based on team salaries, visualization helps students with pattern recognition in their own writing.

I'm curious about this book, what was it?

Doing Grammar by Max Morenberg [1]For me, isolating the concepts spared me a lot of confusion.

[1] http://www.amazon.com/Doing-Grammar-Edition-Max-Morenberg/dp...

Thanks!

we should work together! i'm working on grmmr (green-bridge.org) and we have a solid system for identifying grammar & visualizing them well. this system is built upon years and years of actual classroom usage. we're looking to build an app / a game that will let the user to identify, analyze the grammar and get applauded for that

Unfortunately my coding skills are far from adequate, when I get proficient I'll send you an email to see what you are up to.