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.
Related
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]
If my title is incorrect/could be better, please let me know.
I've been trying to find an existing paper/article describing the problem that I'm having: I'm trying to create vectors for words so that they are equal to the sum of their parts.
For example: Cardinal(the bird) would be equal to the vectors of: red, bird, and ONLY that.
In order to train such a model, the input might be something like a dictionary, where each word is defined by it's attributes.
Something like:
Cardinal: bird, red, ....
Bluebird: blue, bird,....
Bird: warm-blooded, wings, beak, two eyes, claws....
Wings: Bone, feather....
So in this instance, each word-vector is equal to the sum of the word-vector of its parts, and so on.
I understand that in the original word2vec, semantic distance was preserved, such that Vec(Madrid)-Vec(Spain)+Vec(Paris) = approx Vec(Paris).
Thanks!
PS: Also, if it's possible, new words should be able to be added later on.
If you're going to be building a dictionary of the components you want, you don't really need word2vec at all. You've already defined the dimensions you want specified: just use them, e.g. in Python:
kb = {"wings": {"bone", "feather"},
"bird": {"wings", "warm-blooded", ...}, ...}
Since the values are sets, you can do set intersection:
kb["bird"] | kb["reptile"]
You'll need to do find some ways decompose the elements recursively for comparisons, simplifications, etc. These are decisions you'll have to make based on what you expect to happen during such operations.
This sort of manual dictionary development is quite an old fashioned approach. Folks like Schank and Abelson used to do stuff like this in the 1970's. The problem is, as these dictionaries get more complex, they become intractable to maintain and more inaccurate in their approximations. You're welcome to try as an exercise---it can be kind of fun!---but keep your expectations low.
You'll also find aspects of meaning lost in these sorts of decompositions. One of word2vec's remarkable properties is its sensitives to the gestalt of words---words may have meaning that is composed of parts, but there's a piece in that composition that makes the whole greater than the sum of the parts. In a decomposition, the gestalt is lost.
Rather than trying to build a dictionary, you might be best off exploring what W2V gives you anyway, from a large corpus, and seeing how you can leverage that information to your advantage. The linguistics of what exactly W2V renders from text aren't wholly understood, but in trying to do something specific with the embeddings, you might learn something new about language.
I'm relatively new to Ruby, so this is a pretty general question. I have found through the Ruby Docs page a lot of methods that seem to do the exact same thing or very similar. For example chars vs split(' ') and each vs map vs collect. Sometimes there are small differences and other times I see no difference at all.
My question here is how do I know which is best practice, or is it just personal preference? I'm sure this varies from instance to instance, so if I can learn some of the more important ones to be cognizant of I would really appreciate that because I would like to develop good habits early.
I am a bit confused by your specific examples:
map and collect are aliases. They don't "do the exact same thing", they are the exact same thing. They are just two names for the same method. You can use whatever name you wish, or what reads best in context, or what your team has decided as a Coding Standard. The Community seems to have settled on map.
each and map/collect are completely different, there is no similarity there, apart from the general fact that they both operate on collections. map transform a collection by mapping every element to a new element using a transformation operation. It returns a new collection (an Array, actually) with the transformed elements. each performs a side-effect for every element of the collection. Since it is only used for its side-effect, the return value is irrelevant (it might just as well return nil like Kernel#puts does, in languages like C, C++, Java, C♯, it would return void), but it is specified to always return its receiver.
split splits a String into an Array of Strings based on a delimiter that can be either a Regexp (in which case you can also influence whether or not the delimiter itself gets captured in the output or ignored) or a String, or nil (in which case the global default separator gets used). chars returns an Array with the individual characters (represented as Strings of length 1, since Ruby doesn't have an specific Character type). chars belongs together in a family with bytes and codepoints which do the same thing for bytes and codepoints, respectively. split can only be used as a replacement for one of the methods in this family (chars) and split is much more general than that.
So, in the examples you gave, there really isn't much similarity at all, and I cannot imagine any situation where it would be unclear which one to choose.
In general, you have a problem and you look for the method (or combination of methods) that solve it. You don't look at a bunch of methods and look for the problem they solve.
There'll typically be only one method that fits a specific problem. Larger problems can be broken down into different subproblems in different ways, so it is indeed possible that you may end up with different combinations of methods to solve the same larger problem, but for each individual subproblem, there will generally be only one applicable method.
When documentation states that 2 methods do the same, it's just matter of preference. To learn the details, you should always start with Ruby API documentation
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
In an experimental project I am playing with I want to be able to look at textual data and detect whether it contains data in a tabular format. Of course there are a lot of cases that could look like tabular data, so I was wondering what sort of algorithm I'd need to research to look for common features.
My first thought was to write a long switch/case statement that checked for data seperated by tabs, and then another case for data separated by pipe symbols and then yet another case for data separated in another way etc etc. Now of course I realize that I would have to come up with a list of different things to detect - but I wondered if there was a more intelligent way of detecting these features than doing a relatively slow search for each type.
I realize this question isn't especially eloquently put so I hope it makes some sense!
Any ideas?
(no idea how to tag this either - so help there is welcomed!)
The only reliable scheme would be to use machine-learning. You could, for example, train a perceptron classifier on a stack of examples of tabular and non-tabular materials.
A mixed solution might be appropriate, i.e. one whereby you handled the most common/obvious cases with simple heuristics (handled in "switch-like" manner) as you suggested, and to leave the harder cases, for automated-learning and other types of classifier-logic.
This assumes that you do not already have a defined types stored in the TSV.
A TSV file is typically
[Value1]\t[Value..N]\n
My suggestion would be to:
Count up all the tabs
Count up all of new lines
Count the total tabs in the first row
Divide the total number of tabs by the tabs in the first row
With the result of 4, if you get a remainder of 0 then you have a candidate of TSV files. From there you may either want to do the following things:
You can continue reading the data and ignoring the error of lines with less or more than the predicted tabs per line
You can scan each line before reading to make sure all are consistent
You can read up to the line that does not fit the format and then throw an error
Once you have a good prediction of the amount of tab separated values you can use a regular expression to parse out the values [as a group].