Method survey 2016 about 2016 HOT 23 OPEN

The list of categories is tentative (and is loosely based on https://habrahabr.ru/post/313862/). If you feel that there is no good fit for your project, please list it under a new category in the comments. Contributions of brief definitions for any category are also welcome.

from 2016.

I, #128, was recursive text templates.

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#6 is text templates plus simulation

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Literary text post-processing: #139 #135 #12
Non-literary text post-processing: #49
Text templates: #113 #12 #8

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Text templates (with a small amount of recursion): #125
Markov chain: #143

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#131 Markov Chains

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#155 text templates and simulation

from 2016.

@dranorter, @serin-delaunay Do you think that the "recursive" part in "recursive text template" is important?

NB: That's a serious question, not trolling. I am not sure yet about how to approach the classification.

from 2016.

I updated the list. Many thanks for contributions! Please feel free to comment on the format and the content — and to classify more entries! :)

from 2016.

#149: non-literary text (tweets), post-processing

from 2016.

Aha! About the recursion: I just noted that there is a separate "recursion" method. I guess we do need to work out definitions here.

In https://habrahabr.ru/post/313862/ "recursion" section actually describes two separate approaches.

First is where the text consists of many shorter stories, with the end of the previous story naturally leads to the beginning of the next one. #136 uses this approach. I propose to call this method iterative text templates, as there is no "depth" in the recursion, it is used more as a literary device than as a method of text generation.

The second approach is true recursion, where the content is of a fractal nature, so to say. See Transorbital anaphase provine biforn the pure-bred synostosis as an example, which is basically a set of nested dictionary definitions, or Redwreath and Goldstar Have Traveled to Deathsgate, where two characters have a discussion about their discussion, ending up with «You want to know whether I am asking whether you are asking whether you shall tell me whether you want to know whether I believe I can answer that?»

from 2016.

Right; my #128 is making a point of having several types of "true recursion;" sub-quests, locations within locations, and stories within stories.

[...] or Redwreath and Goldstar Have Traveled to Deathsgate, where two characters have a discussion about their discussion, ending up with «You want to know whether I am asking whether you are asking whether you shall tell me whether you want to know whether I believe I can answer that?»

Nice! I wish I had thought to include that!

from 2016.

#133 and #147 used text templates and recursion.

from 2016.

I didn't mean to distinguish recursive and non-recursive text templates as methods; recursion was one of the methods listed, and I found it unclear what it meant as a method. Since a few of the templates used in #125 were recursive (primarily object lists, usernames, and passwords), and since fortunes are told for children, grandchildren, and great-grandchildren using recursion, I thought it might be appropriate to list #125 under recursion as well as under text templates.

from 2016.

It would look cleaner to just list the recursive template examples under "recursion". Though I'd like to know what other sorts of recursion might be used. A train, generate, train, generate, train loop? Repeated OCR of printouts of Gutenberg texts?

from 2016.

#130 "Text templates with recursion and simulation"

I have a tree of possible events in the story and then a recursive template that says which events
to choose when creating the story. Recursion because the story follows a "goal>subquest>subsubquest" model and simulation because different events in the story affect the "world state" which can be accessed later on to generate text.

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#114 used neural networks, simulation and text templates.

from 2016.

I used a few of these ( #45 ). "Filler" scenes were generated with Tracery or by filling in simple templates. Combat scenes were done with a simulation. Markov Chains (seeded with a large list of cities) to generate names for people and places. Didn't reference other works, unless you count the covers, which mixed and matched a few templates and PD or creative commons artwork.

from 2016.

#24 uses sentence similarity derived from WordNet and Corpus statistics in addition to aleatory page selection from a given set of documents; so it's two different uses/methods approaching corpora—there's probably a way better way to describe this.

from 2016.

#15 uses an approach inspired by the "Story Compiler".

I suppose the "Story Compiler" and its derivatives could all belong to the "literary-text post-processing" category (the actual literary-text is kept mostly unmodified, but the story compiler decides when it shows up in the story). But you could also argue that it's a "template/simulation" hybrid as well (simulating an author's mind in coming up with a plot and then plugging in paragraphs to fit that plot). I think this just goes to show how arbitrary the process of categorization can be.

Personally, I'd support listing #15 under "literary-text post-processing though", especially as no templates were actually being used.

from 2016.

Many thanks for the submissions, feedback and your work on the NaNoGenMo, all! I updated the issue text. Any further comments, critique and other feedback are appreciated — not to mention more submissions to this list!

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• #61 is "non-literary text or data post-processing (time)"
• #138 is "non-literary text or data post-processing (tweets)"

from 2016.

Updated, thank you! Any more submissions? :)

from 2016.