I currently work on a personal writing project which has ended up with me maintaining a few different versions due to the differences of the relevant platforms and output formats I want to support that are not trivially solved. After several instances of me glancing at pandoc and the sheer forest that it represents, I have concluded mere templates don't do what I need, and worse, that I seem to need a combination of a custom filter and writer... suffice to say: messing with the AST is where I feel way out of my depth. Enough so that, rather than asking specific questions of 'how do I do X' here, this is a question of 'is X the right way to go about it, or what is the proper way to do it, and can you give an example of how it ties together?'... so if this question is rather lengthy: my apologies.
My current goal is to have custom markup like the following which is supposed to 'track' which character says something:
<paul|"Hi there">
If I convert to HTML, I'd want something similar to:
<span class="speech paul">"Hi there"</span>
to pop out (and perhaps the <p> tags), whereas if it is just pure markdown / plain text, I'd want it to silently disappear:
"Hi there"
Looking at the JSON AST structures I've studied, it would make sense that I'd want a new structure type similar to the 'Emph' tag called 'Speech' which allows whole blobs of text to be put inside of it with a bit of extra information attached (the person speaking). So something like this:
{"t":"Speech","speaker":"paul","c":[ ... ] }
Problem #1: At the point a lua-filter sees the document, it is obviously already distilled to an AST. This means replacing the items in a manner similar to what most macro expander samples do cannot really work since it would require reading forward. With this method, I just replace bits and pieces in place (<NAME| becomes a StartSpeech and the first solitary > that follows becomes an EndSpeech, but that would make malformed input a bigger potential problem because of silent-ish failures. Additionally, these tags would be completely out of sorts with how an AST is supposed to look.
To complicate matters even further, some of my characters end up learning a secondary language throughout the story, for which I apply a different format that contains a simplified understanding of the spoken text with perspective-characters understanding of what was said. Example:
<paul|"Heb je goed geslapen?"|"Did you ?????">
I could probably add a third 'UnderstoodSpeech' group to my filter, but (problem #2) at this point, the relationship between the speaker, the original speech, and the understood translation is completely gone. As long as the final documents need these values in these respective orders and only in these orders, it is fine... but what if I want my HTML version to look like
"Did you?????"
with a tool-tip / hover-over effect containing the original speech? That would be near impossible to achieve because the AST does not contain that kind of relational detail.
Whatever kind of AST I create in the filter is what I need to understand in my custom writer. Ideally, I want to re-use as much stock functionality of pandoc as possible for the writer, but I don't even know if that is feasible at this point.
So now my question: could someone with great pandoc understanding please give me an example on how to keep relevant data-bits together and apply them in the correct manner? By this I mean show a basic example of what needs to be put in the lua-filter and lua-writer scripts in the following toolchain
[CUSTOMIZED MARKDOWN INPUT] -> lua-filter -> lua-writer -> [CUSTOMIZED HTML5 OUTPUT]
Related
For those who are not familiar with what a homophone is, I provide the following examples:
our & are
hi & high
to & too & two
While using the Speech API included with iOS, I am encountering situations where a user may say one of these words, but it will not always return the word I want.
I looked into the [alternativeSubstrings] (link) property wondering if this would help, but in my testing of the above words, it always comes back empty.
I also looked into the Natural Language API, but could not find anything in there that looked useful.
I understand that as a user adds more words, the Speech API can begin to infer context and correct for these, but my use case will not work well with this since it will often only want one or two words at most, limiting the effectiveness of context.
An example of contextual processing:
Using the words above on their own, I get these results:
are
hi
to
However, if I put together the following sentence, you can see they are all wrong:
I am too high for our ladder
Ideally, I would either get a list back containing [are, our], [to, too, two], [hi, high] for each transcription segment, or would have a way to compare a string against a function that supports homophones.
An example of this would be:
if myDetectedWord == "to" then { ... }
Where myDetectedWord can be [to, too, two], and this function would return true for each of these.
This is a common NLP dilemma, and I'm not so sure what might be your desired output in this application. However, you may want to bypass this problem in your design/architecture process, if possible and if you could. Otherwise, this problem is to turn into a challenge.
Being said that, if you wish to really get into it, I like this idea of yours:
string against a function
This might be more efficient and performance friendly.
One way, I'd be liking to solve this problem would be though RegEx processing, instead of using endless loops and arrays. You could maybe prototype loops and arrays to begin with and see how it works, then you might want to use regular expression for gaining performance.
You could for instance define fixed arrays in regular expressions and quickly check against your string (word by word, maybe using back-referencing) and you can add many boundaries in your expressions for string processing, as you wish.
Your fixed arrays also can be designed based on probabilities of occurring certain words in certain part of a string. For instance,
^I
vs
^eye
The probability of I being the first word is much higher than that of eye.
The probability of I in any part of a string is higher than that of eye, also.
You might want to weight words based on that.
I'd say the key would be that you'd narrow down your desired outputs as focused as possible and increase accuracy, [maybe even with 100 words if possible], if you wish to have a good/working application.
Good project though, I hope you like/enjoy the challenge.
I know that PO / MO files are meant to be used for small strings like button names, labels, etc. Not long text like an About page, etc.
But lately I am encountering a lot of situations that are in the middle. For example, a two sentence call to action. Or a short paragraph.
Is there best practice or "rule of thumb" for when a string is too long to put in a PO file?
update
For "long" text I use partials and include the correct language version. My question is WHEN is it optimal to use one vs the other. I've heard that PO files are "inefficient" for "long" pieces of text. But what does that mean and when is it too "long"? Or is this not a concern?
Use one entry for a self-contained chunk of text; e.g. a sentence as you say.
Two sentences that belong together and don't make sense without each other should be one entry. Why? Because otherwise the translator wouldn't have the context necessary to translate it well. Same goes for a short paragraph, e.g. explaining a setting: if it's inseparable in the code, it should be one entry.
If you encounter a situation where you have lots of long texts regularly (e.g. entire pages or paragraphs of pages), that's usually a sign that you are using an ill-fitting tool. Some people do it, using Gettext for entire articles, but you're better off having separate documents in such cases. But that doesn't seem to be the case here.
I am looking to write a basic profanity filter in a Rails based application. This will use a simply search and replace mechanism whenever the appropriate attribute gets submitted by a user. My question is, for those who have written these before, is there a CSV file or some database out there where a list of profanity words can be imported into my database? We are submitting the words that we will replace the profanities with on our own. We more or less need a database of profanities, racial slurs and anything that's not exactly rated PG-13 to get triggered.
As the Tin Man suggested, this problem is difficult, but it isn't impossible. I've built a commercial profanity filter named CleanSpeak that handles everything mentioned above (leet speak, phonetics, language rules, whitelisting, etc). CleanSpeak is capable of filtering 20,000 messages per second on a low end server, so it is possible to build something that works well and performs well. I will mention that CleanSpeak is the result of about 3 years of on-going development though.
There are a few things I tell everyone that is looking to try and tackle a language filter.
Don't use regular expressions unless you have a small list and don't mind a lot of things getting through. Regular expressions are relatively slow overall and hard to manage.
Determine if you want to handle conjugations, inflections and other language rules. These often add a considerable amount of time to the project.
Decide what type of performance you need and whether or not you can make multiple passes on the String. The more passes you make the slow your filter will be.
Understand the scunthrope and clbuttic problems and determine how you will handle these. This usually requires some form of language intelligence and whitelisting.
Realize that whitespace has a different meaning now. You can't use it as a word delimiter any more (b e c a u s e of this)
Be careful with your handling of punctuation because it can be used to get around the filter (l.i.k.e th---is)
Understand how people use ascii art and unicode to replace characters (/ = v - those are slashes). There are a lot of unicode characters that look like English characters and you will want to handle those appropriately.
Understand that people make up new profanity all the time by smashing words together (likethis) and figure out if you want to handle that.
You can search around StackOverflow for my comments on other threads as I might have more information on those threads that I've forgotten here.
Here's one you could use: Offensive/Profane Word List from CMU site
Based on personal experience, you do understand that it's an exercise in futility?
If someone wants to inject profanity, there's a slew of words that are innocent in one context, and profane in another so you'll have to write a context parser to avoid black-listing clean words. A quick glance at CMU's list shows words I'd never consider rude/crude/socially unacceptable. You'll see there are many words that could be proper names or nouns, countries, terms of endearment, etc. And, there are myriads of ways to throw your algorithm off using L33T speak and such. Search Wikipedia and the internets and you can build tables of variations of letters.
Look at CMU's list and imagine how long the list would be if, in addition to the correct letter, every a could also be 4, o could be 0 or p, e could be 3, s could be 5. And, that's a very, very, short example.
I was asked to do a similar task and wrote code to generate L33T variations of the words, and generated a hit-list of words based on several profanity/offensive lists available on the internet. After running the generator, and being a little over 1/4 of the way through the file, I had over one million entries in my DB. I pulled the plug on the project at that point, because the time spent searching, even using Perl's Regex::Assemble, was going to be ridiculous, especially since it'd still be so easy to fool.
I recommend you have a long talk with whoever requested that, and ask if they understand the programming issues involved, and low-likelihood of accuracy and success, especially over the long-term, or the possible customer backlash when they realize you're censoring them.
I have one that I've added to (obfuscated a bit) but here it is: https://github.com/rdp/sensible-cinema/blob/master/lib/subtitle_profanity_finder.rb
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I'm building an application that receives source code as input and analyzes several aspects of the code. It can accept code from many common languages, e.g. C/C++, C#, Java, Python, PHP, Pascal, SQL, and more (however many languages are unsupported, e.g. Ada, Cobol, Fortran). Once the language is known, my application knows what to do (I have different handlers for different languages).
Currently I'm asking the user to input the programming language the code is written in, and this is error-prone: although users know the programming languages, a small percentage of them (on rare occasions) click the wrong option just due to recklessness, and that breaks the system (i.e. my analysis fails).
It seems to me like there should be a way to figure out (in most cases) what the language is, from the input text itself. Several notes:
I'm receiving pure text and not file names, so I can't use the extension as a hint.
The user is not required to input complete source codes, and can also input code snippets (i.e. the include/import part may not be included).
it's clear to me that any algorithm I choose will not be 100% proof, certainly for very short input codes (e.g. that could be accepted by both Python and Ruby), in which cases I will still need the user's assistance, however I would like to minimize user involvement in the process to minimize mistakes.
Examples:
If the text contains "x->y()", I may know for sure it's C++ (?)
If the text contains "public static void main", I may know for sure it's Java (?)
If the text contains "for x := y to z do begin", I may know for sure it's Pascal (?)
My question:
Are you familiar with any standard library/method for figuring out automatically what the language of an input source code is?
What are the unique code "tokens" with which I could certainly differentiate one language from another?
I'm writing my code in Python but I believe the question to be language agnostic.
Thanks
Vim has a autodetect filetype feature. If you download vim sourcecode you will find a /vim/runtime/filetype.vim file.
For each language it checks the extension of the file and also, for some of them (most common), it has a function that can get the filetype from the source code. You can check that out. The code is pretty easy to understand and there are some very useful comments there.
build a generic tokenizer and then use a Bayesian filter on them. Use the existing "user checks a box" system to train it.
Here is a simple way to do it. Just run the parser on every language. Whatever language gets the farthest without encountering any errors (or has the fewest errors) wins.
This technique has the following advantages:
You already have most of the code necessary to do this.
The analysis can be done in parallel on multi-core machines.
Most languages can be eliminated very quickly.
This technique is very robust. Languages that might appear very similar when using a fuzzy analysis (baysian for example), would likely have many errors when the actual parser is run.
If a program is parsed correctly in two different languages, then there was never any hope of distinguishing them in the first place.
I think the problem is impossible. The best you can do is to come up with some probability that a program is in a particular language, and even then I would guess producing a solid probability is very hard. Problems that come to mind at once:
use of features like the C pre-processor can effectively mask the underlyuing language altogether
looking for keywords is not sufficient as the keywords can be used in other languages as identifiers
looking for actual language constructs requires you to parse the code, but to do that you need to know the language
what do you do about malformed code?
Those seem enough problems to solve to be going on with.
One program I know which even can distinguish several different languages within the same file is ohcount. You might get some ideas there, although I don't really know how they do it.
In general you can look for distinctive patterns:
Operators might be an indicator, such as := for Pascal/Modula/Oberon, => or the whole of LINQ in C#
Keywords would be another one as probably no two languages have the same set of keywords
Casing rules for identifiers, assuming the piece of code was writting conforming to best practices. Probably a very weak rule
Standard library functions or types. Especially for languages that usually rely heavily on them, such as PHP you might just use a long list of standard library functions.
You may create a set of rules, each of which indicates a possible set of languages if it matches. Intersecting the resulting lists will hopefully get you only one language.
The problem with this approach however, is that you need to do tokenizing and compare tokens (otherwise you can't really know what operators are or whether something you found was inside a comment or string). Tokenizing rules are different for each language as well, though; just splitting everything at whitespace and punctuation will probably not yield a very useful sequence of tokens. You can try several different tokenizing rules (each of which would indicate a certain set of languages as well) and have your rules match to a specified tokenization. For example, trying to find a single-quoted string (for trying out Pascal) in a VB snippet with one comment will probably fail, but another tokenizer might have more luck.
But since you want to perform analysis anyway you probably have parsers for the languages you support, so you can just try running the snippet through each parser and take that as indicator which language it would be (as suggested by OregonGhost as well).
Some thoughts:
$x->y() would be valid in PHP, so ensure that there's no $ symbol if you think C++ (though I think you can store function pointers in a C struct, so this could also be C).
public static void main is Java if it is cased properly - write Main and it's C#. This gets complicated if you take case-insensitive languages like many scripting languages or Pascal into account. The [] attribute syntax in C# on the other hand seems to be rather unique.
You can also try to use the keywords of a language - for example, Option Strict or End Sub are typical for VB and the like, while yield is likely C# and initialization/implementation are Object Pascal / Delphi.
If your application is analyzing the source code anyway, you code try to throw your analysis code at it for every language and if it fails really bad, it was the wrong language :)
My approach would be:
Create a list of strings or regexes (with and without case sensitivity), where each element has assigned a list of languages that the element is an indicator for:
class => C++, C#, Java
interface => C#, Java
implements => Java
[attribute] => C#
procedure => Pascal, Modula
create table / insert / ... => SQL
etc. Then parse the file line-by-line, match each element of the list, and count the hits.
The language with the most hits wins ;)
How about word frequency analysis (with a twist)? Parse the source code and categorise it much like a spam filter does. This way when a code snippet is entered into your app which cannot be 100% identified you can have it show the closest matches which the user can pick from - this can then be fed into your database.
Here's an idea for you. For each of your N languages, find some files in the language, something like 10-20 per language would be enough, each one not too short. Concatenate all files in one language together. Call this lang1.txt. GZip it to lang1.txt.gz. You will have a set of N langX.txt and langX.txt.gz files.
Now, take the file in question and append to each of he langX.txt files, producing langXapp.txt, and corresponding gzipped langXapp.txt.gz. For each X, find the difference between the size of langXapp.gz and langX.gz. The smallest difference will correspond to the language of your file.
Disclaimer: this will work reasonably well only for longer files. Also, it's not very efficient. But on the plus side you don't need to know anything about the language, it's completely automatic. And it can detect natural languages and tell between French or Chinese as well. Just in case you need it :) But the main reason, I just think it's interesting thing to try :)
The most bulletproof but also most work intensive way is to write a parser for each language and just run them in sequence to see which one would accept the code. This won't work well if code has syntax errors though and you most probably would have to deal with code like that, people do make mistakes. One of the fast ways to implement this is to get common compilers for every language you support and just run them and check how many errors they produce.
Heuristics works up to a certain point and the more languages you will support the less help you would get from them. But for first few versions it's a good start, mostly because it's fast to implement and works good enough in most cases. You could check for specific keywords, function/class names in API that is used often, some language constructions etc. Best way is to check how many of these specific stuff a file have for each possible language, this will help with some syntax errors, user defined functions with names like this() in languages that doesn't have such keywords, stuff written in comments and string literals.
Anyhow you most likely would fail sometimes so some mechanism for user to override language choice is still necessary.
I think you never should rely on one single feature, since the absence in a fragment (e.g. somebody systematically using WHILE instead of for) might confuse you.
Also try to stay away from global identifiers like "IMPORT" or "MODULE" or "UNIT" or INITIALIZATION/FINALIZATION, since they might not always exist, be optional in complete sources, and totally absent in fragments.
Dialects and similar languages (e.g. Modula2 and Pascal) are dangerous too.
I would create simple lexers for a bunch of languages that keep track of key tokens, and then simply calculate a key tokens to "other" identifiers ratio. Give each token a weight, since some might be a key indicator to disambiguate between dialects or versions.
Note that this is also a convenient way to allow users to plugin "known" keywords to increase the detection ratio, by e.g. providing identifiers of runtime library routines or types.
Very interesting question, I don't know if it is possible to be able to distinguish languages by code snippets, but here are some ideas:
One simple way is to watch out for single-quotes: In some languages, it is used as character wrapper, whereas in the others it can contain a whole string
A unary asterisk or a unary ampersand operator is a certain indication that it's either of C/C++/C#.
Pascal is the only language (of the ones given) to use two characters for assignments :=. Pascal has many unique keywords, too (begin, sub, end, ...)
The class initialization with a function could be a nice hint for Java.
Functions that do not belong to a class eliminates java (there is no max(), for example)
Naming of basic types (bool vs boolean)
Which reminds me: C++ can look very differently across projects (#define boolean int) So you can never guarantee, that you found the correct language.
If you run the source code through a hashing algorithm and it looks the same, you're most likely analyzing Perl
Indentation is a good hint for Python
You could use functions provided by the languages themselves - like token_get_all() for PHP - or third-party tools - like pychecker for python - to check the syntax
Summing it up: This project would make an interesting research paper (IMHO) and if you want it to work well, be prepared to put a lot of effort into it.
There is no way of making this foolproof, but I would personally start with operators, since they are in most cases "set in stone" (I can't say this holds true to every language since I know only a limited set). This would narrow it down quite considerably, but not nearly enough. For instance "->" is used in many languages (at least C, C++ and Perl).
I would go for something like this:
Create a list of features for each language, these could be operators, commenting style (since most use some sort of easily detectable character or character combination).
For instance:
Some languages have lines that start with the character "#", these include C, C++ and Perl. Do others than the first two use #include and #define in their vocabulary? If you detect this character at the beginning of line, the language is probably one of those. If the character is in the middle of the line, the language is most likely Perl.
Also, if you find the pattern := this would narrow it down to some likely languages.
Etc.
I would have a two-dimensional table with languages and patterns found and after analysis I would simply count which language had most "hits". If I wanted it to be really clever I would give each feature a weight which would signify how likely or unlikely it is that this feature is included in a snippet of this language. For instance if you can find a snippet that starts with /* and ends with */ it is more than likely that this is either C or C++.
The problem with keywords is someone might use it as a normal variable or even inside comments. They can be used as a decider (e.g. the word "class" is much more likely in C++ than C if everything else is equal), but you can't rely on them.
After the analysis I would offer the most likely language as the choice for the user with the rest ordered which would also be selectable. So the user would accept your guess by simply clicking a button, or he can switch it easily.
In answer to 2: if there's a "#!" and the name of an interpreter at the very beginning, then you definitely know which language it is. (Can't believe this wasn't mentioned by anyone else.)
Before I dive into ANTLR (because it is apparently not for the faint of heart), I just want to make sure I have made the right decision regarding its usage.
I want to create a grammar that will parse in a text file with predefined tags so that I can populate values within my application. (The text file is generated by another application.) So, essentially, I want to be able to parse something like this:
Name: TheFileName
Values: 5 3 1 6 1 3
Other Values: 5 3 1 5 1
In my application, TheFileName is stored as a String, and both sets of values are stored to an array. (This is just a sample, the file is much more complicated.) Anyway, am I at least going down the right path with ANTLR? Any other suggestions?
Edit
The files are created by the user and they define the areas via tags. So, it might look something like this.
Name: <string>TheFileName</string>
Values: <array>5 3 1 6 1 3</array>
Important Value: <double>3.45</double>
Something along those lines.
The basic question is how is the file more complicated? Is it basically more of the same, with a tag, a colon and one or more values, or is the basic structure of the other lines more complex? If it's basically just more of the same, code to recognize and read the data is pretty trivial, and a parser generator isn't likely to gain much. If the other lines have substantially different structure, it'll depend primarily on how they differ.
Edit: Based on what you've added, I'd go one (tiny) step further, and format your file as XML. You can then use existing XML parsers (and such) to read the files, extract data, verify that they fit a specified format, etc.
It depends on what control you have over the format of the file you are parsing. If you have no control then a parser-generator such as ANTLR may be valuable. (We do this ourselves for FORTRAN output files over which we have no control). It's quite a bit of work but we have now mastered the basic ANTLR lexer/parser strategy and it's starting to work well.
If, however, you have some or complete control over the format then create it with as much markup as necessary. I would always create such a file in XML as there are so many tools for processing it (not only the parsing, but also XPath, databases, etc.) In general we use ANTLR to parse semi-structured information into XML.
If you don't need for the format to be custom-built, then you should look into using an existing format such as JSON or XML, for which there are parsers available.
Even if you do need a custom format, you may be better off designing one that is dirt simple so that you don't need a full-blown grammar to parse it. Designing your own scripting grammar from scratch and doing a good job of it is a lot of work.
Writing grammar parsers can also be really fun, so if you're curious then you should go for it. But I don't recommend carelessly mixing learning exercises with practical work code.
Well, if it's "much more complicated", then, yes, a parser generator would be helpful. But, since you don't show the actual format of your file, how could anybody know what might be the right tool for the job?
I use the free GOLD Parser Builder, which is incredibly easy to use, and can generate the parser itself in many different languages. There are samples for parsing such expressions also.
If the format of the file is up to the user can you even define a grammar for it?
Seems like you just want a lexer at best. Using ANTLR just for the lexer part is possible, but would seem like overkill.