it looks pretty basic, but for the last half hour I couldnt find out why we say {zero} or {one} in the following productions what does the {} indicate?
bit
= {zero} zero
| {one} one
;
I have also searched far and wide for this answer, but what i have found is that the words in the curly braces of your production simply name that particular variation.
For example, when your production is run there are two methods that will be created for it when it is parsed, AZeroBit and AOneBit, thus the user can take specific actions for those two different variatons in his/her java working classes code.
Forgive me if i am way off point here, as there is not much to go by in terms of good beginner tutorials. Try reading the sableCC thesis chapter 3.
Related
My Question
What are the best practices for creating a customized report based on a user form input? Specifically, how do I create an easy to maintain system which takes user input which is collected in a form and generate multiple paragraphs that explains the results of analysis.
Background
I am working on a very large multiyear project with a startup (who is my client). My job is to program analysis and generate reports to users. The pipeline for data looks like this:
Users enter information into a form -> results are calculated based on user input -> reports are displayed to users that share analysis.
It is really important to my client that some of the analysis results are displayed in paragraphs in a non-formal user friendly tone. The challenge is that the form and analysis are quite complex and will only get more complex over time. An example of the type of template for the paragraphs looks something like this:
resultsParagraphText=`Hi ${userName}. We found that the best ice cream flavour for you is ${bestIceCreamFlavor}. These other flavors ${otherFlavors} might be good for you. Here are the reasons why you might enjoy these flavors: ${reasonsWhyGoodFlavors}.
However we would not recommend these other flavors ${badFlavors}. Here are the reasons you should avoid this bad flavors: ${reasonsWhyBadFlavors}.`
These results paragraphs, of which there of many, have several minor problems which combined are significant:
If there is a bug in the code, minor visual errors would be visible to end users (capitalization errors, missing/extra commas, and so on).
A lot of string comparisons (e.g. if answers.previousFlavors.includes("Vanilla")) are required to generate the results paragraphs. Minor errors in the forms (e.g. vanilla in the form is not capitalized so answers.previousFlavors.includes("Vanilla") returns false even when user enters vanilla.) can cause errors in the results paragraph.
Changes in different parts of the project (form, analysis) directly effect how the results paragraph is made. Bad types, differences in string values, null or undefined values not being caught directly have an impact on how the results paragraph is made.
There are many edge cases (e.g. What if the user has no other suitable good flavors for them? The the sentence These other flavors ${otherFlavors} might be good for you. needs to be excluded).
It is hard to write paragraphs that use templates and have a non-formal tone.
and so on.
I have charts and other types of ways to display results and have explained to the client the challenges of sharing the information in paragraph form.
What I am looking for
I need examples, how tos, best practices on how to build a maintainable system for generating customized paragraphs based on user input. I know how to solve each of the individual issues (as they are fairly simple) but in a large project this will become very hard to maintain.
Notes
I have no clue what tags to use for the post. Feel free to edit/add tags if you know more appropriate ones.
The project is planning to use machine learning in the future other parts of the project. If there is a ML/AI solution that is useful please tell me.
I am working primarily in JavaScript, Python, C, and R, but if there is a library or tool in any other language please tell me. Finding a solution is very important to me and I would be willing to learn a lot find a best solution.
To avoid this question being removed because I have rephrased it to avoid asking for personal opinion, instead asking for existing examples or how tos. I can also imagine that others might find a solution fairly useful. If you can edit it to make the question less subjective please do so.
If you have any questions or need clarification feel free to ask. Any help is appreciated.
I am a graduate student focusing on ML and NLP. I have a lot of data (8 million lines) and the text is usually badly written and contains so many spelling mistakes.
So i must go through some text cleaning and vectorizing. To do so, i considered two approaches:
First one:
cleaning text by replacing bad words using hunspell package which is a spell checker and morphological analyzer
+
tokenization
+
convert sentences to vectors using tf-idf
The problem here is that sometimes, Hunspell fails to provide the correct word and changes the misspelled word with another word that don't have the same meaning. Furthermore, hunspell does not reconize acronyms or abbreviation (which are very important in my case) and tends to replace them.
Second approache:
tokenization
+
using some embeddings methode (like word2vec) to convert words into vectors without cleaning text
I need to know if there is some (theoretical or empirical) way to compare this two approaches :)
Please do not hesitate to respond If you have any ideas to share, I'd love to discuss them with you.
Thank you in advance
I post this here just to summarise the comments in a longer form and give you a bit more commentary. No sure it will answer your question. If anything, it should show you why you should reconsider it.
Points about your question
Before I talk about your question, let me point a few things about your approaches. Word embeddings are essentially mathematical representations of meaning based on word distribution. They are the epitome of the phrase "You shall know a word by the company it keeps". In this sense, you will need very regular misspellings in order to get something useful out of a vector space approach. Something that could work out, for example, is US vs. UK spelling or shorthands like w8 vs. full forms like wait.
Another point I want to make clear (or perhaps you should do that) is that you are not looking to build a machine learning model here. You could consider the word embeddings that you could generate, a sort of a machine learning model but it's not. It's just a way of representing words with numbers.
You already have the answer to your question
You yourself have pointed out that using hunspell introduces new mistakes. It will be no doubt also the case with your other approach. If this is just a preprocessing step, I suggest you leave it at that. It is not something you need to prove. If for some reason you do want to dig into the problem, you could evaluate the effects of your methods through an external task as #lenz suggested.
How does external evaluation work?
When a task is too difficult to evaluate directly we use another task which is dependent on its output to draw conclusions about its success. In your case, it seems that you should pick a task that depends on individual words like document classification. Let's say that you have some sort of labels associated with your documents, say topics or types of news. Predicting these labels could be a legitimate way of evaluating the efficiency of your approaches. It is also a chance for you to see if they do more harm than good by comparing to the baseline of "dirty" data. Remember that it's about relative differences and the actual performance of the task is of no importance.
There is a questionnaire that we use to evaluate the student knowledge level (we do this manually, as in a test paper). It consists of the following parts:
Multiple choice
Comprehension Questions (I.e: Is a spider an insect?)
Now I have been given a task to make an expert system that will automate this. So basically we have a proper answer for this. But my problem is the "comprehension questions". I need to compare the context of their answer to the context of the correct answer.
I already initially searched for the answer, but it seems like it's really a big task to do. What I have search so far is I can do this through NLP which is really new to me. Also, if I'm not mistaken, it seems like that I have to find a dictionary of all words that is possible for the examiner to answer.
Am I on the right track? If no, please suggest of what should I do (study what?) or give me some links to the materials that I need. Also, should I make my own dictionary? Because the words that I will be using are in the Filipino language.
Update: Comprehension question
The comprehension section of the questionnaire contains one paragraph explaining a certain scenario. The questions are fairly simple. Here is an example:
Bonnie's uncle told her to pick apples from the tree. Picking up a stick, she poked the fruits so they would fall. In the middle of doing this, a strong gust of wind blew. Due to her fear of the fruits falling on top of her head, she stopped what she was doing. After this, though, she noticed that the wind had caused apples to fall from the tree. These fallen apples were what she brought home to her uncle.
The questions are:
What did Bonnie's uncle tell her to do?
What caused Bonnie to stop picking apples from the tree?
Is Bonnie a good fruit picker? Please explain your answer.
The possible answers that the answer key states are:
For number 1:
1.1 Bonnie's uncle told her to pick apples from the tree
1.2 Get apples
For number 2:
2.1 A strong gust of wind blew
2.2 She might get hit in the head by the fruits
For number 3:
3.1 No, because the apples she got were already on the ground
3.2 No, because the wind was what caused the fruits to fall
3.3 Yes, because it is difficult to pick fruits when it's windy.
3.4 Yes, because at least she tried
Now there are answers that were given to me. The job that the system shall be able to do is to compare the context of the student's answer to the context of the right answer in order for the system to successfully be able to grade the student's answer.
One simplistic way of doing this that I can think of (off the top of my head) is to use a string similarity metric like cosine or jaccard to identify whether certain keywords appear in a test answer and the known correct answer.
Extracting these keywords automatically could be done with part of speech tagging using NLP. For example, you could extract all nouns (and possibly verbs). Then, representing each answer as a vector of keywords, you could compare the test vector with the known correct vector.
For example, in the second question, the vector for the two possible answers could be
gust, wind, blew
hit, head, fruits
An answer like "she picked up a stick" with the keywords: picked, stick would have a very low score as compared to something like "afraid of fruit falling on her head" with keywords: fruit, falling, head.
Notes:
This can detect only wildly wrong answers. Wrong answers containing the right keywords would not be detected by this technique. :)
I'm not sure about non-english sentences. If that is the case, you might want to take every word in the answer as a keyword (removing stopwords). This question might help as well.
Help by editing my question title and tags is greatly appreciated!
Sometimes one participant in my corpus of "conversations" will refer to another participant using a nickname, usually an abbreviation or misspelling, but hereafter I'll just say "nicknames". Let's say I'm willing to manually tell my software whether or not I think various possible nicknames are in fact nicknames, but I want software to come up with a list of possible matches between the handle's that identify people, and the potential nicknames. How would I go about doing that?
Background on me and then my corpus: I have no experience doing natural language processing but I'm a competent data analyst with R. My data is produced by 70 teams, each forecasting the likelihood of 100 distinct events occurring some time in the future. The result that I have 70 x 100 = 7000 text files, containing the stream of forecasts participants make and the comments they include with their forecasts. I'll paste a very short snip of one of these text files below, this one had to do with whether the Malian government would enter talks with the MNLA:
02/12/2013 20:10: past_returns answered Yes: (50%)
I hadn't done a lot of research when I put in my previous
placeholder... I'm bumping up a lot due to DougL's forecast
02/12/2013 19:31: DougL answered Yes: (60%)
Weak President Traore wants talks if MNLA drops territorial claims.
Mali's military may not want talks. France wants talks. MNLA sugggests
it just needs autonomy. But in 7 weeks?
02/12/2013 10:59: past_returns answered No: (75%)
placeholder forecast...
http://www.irinnews.org/Report/97456/What-s-the-way-forward-for-Mali
My initial thoughts: Obviously I can start by providing the names I'm looking to match things up with... in the above example they would be past_returns and DougL (though there is no use of nicknames in the above). I wouldn't think it'd be that hard to get a computer to guess at minor misspellings (though I wouldn't personally know where to start). I can imagine that other tricks could be used, like assuming that a string is more likely to be a nickname if it is used much much more by one team, than by other teams. A nickname is more likely to refer to someone who spoke recently than someone who spoke long ago, or not at all on regarding this question. And they should be used in sentences in a manner similar to the way the full name/screenname is typically used in the corpus. But I'm interested to hear about simple approaches, as well as ones that try to consider more sophisticated techniques.
This could get about as complicated as you want to make it. From the semi-linguistic side of things, research topics would include Levenshtein Distance (for detecting minor misspellings of known names/nicknames) and Named Entity Recognition (for the task of detecting names/nicknames in the first place). Actually, NER's worth reading about, but existing systems might not help you much in your domain of forum handles and nicknames.
The first rough idea that comes to mind is that you could run a tokenized version of your corpus against an English dictionary (perhaps a dataset compiled from Wiktionary or something like WordNet) to find words that are candidates for names, then filter those through some heuristics (do they start with the same letters as known full names? Do they have a low Levenshtein distance from known names? Are they used more than once?).
You could also try some clustering or supervised ML algorithms against the non-word tokens. That might reveal some non-"word" tokens that often occur in the same threads as a given username; again, heuristics could help rule out some false positives.
Good luck; sounds like a fun problem - hope I mentioned at least one thing you hadn't already thought of.
I'm writing a bridge between the user and a search engine, not a search engine. Part of my value added will be inferring the intent of a query. The intent of a tracking number, stock symbol, or address is fairly obvious. If I can categorise a query, then I can decide if the user even needs to see search results. Of course, if I cannot, then they will see search results. I am currently designing this inference engine.
I'm writing a parser; it should take any given token and assign it a category. Here are some theoretical English examples:
"denver" is a USCITY and a PLACENAME
"aapl" is a NASDAQSYMBOL and a STOCKTICKERSYMBOL
"555 555 5555" is a USPHONENUMBER
I know that each of these cases will most likely require specific handling, however I'm not sure where to start.
Ideally I'd end up with something simple like:
queryCategory = magicCategoryFinder( query )
>print queryCategory
>"SOMECATEGORY or a list"
Natural language parsing is a complicated topic. One of the problems here is that determining what a word is depends on context and implied knowledge. Also, you're not so much interested in words as you are in groups of words. Consider, "New York City" is a place but its three words, two of which (new and city) have other meanings.
also you have to consider ambiguity, which is once again where context and implied knowledge comes in. For example, JAVA is (or was) a stock symbol for Sun Microsystems. It's also a programming language, a place and has meaning associated with coffee. How do you classify it? You'd need to know the context in which it was used.
And if you can solve that problem reliably you can make yourself very wealthy.
What's all this in aid of anyway?
To learn about "tagging" (the term of art for what you're trying to do), I suggest playing around with NLTK's tag module. More generally, NLTK, the Natural Language ToolKit, is an excellent toolkit (based on the Python programming language) for experimentation and learning in the field of Natural Language Processing (whether it's suitable for a given production application may be a different issue, esp. if said application requires very high speed processing on large volumes of data -- but, you have to walk before you can run!-).
You're bumping up against one of the hardest problems in computer science today... determining semantics from english context. This is the classic text mining problem and get into some very advanced topics. I thiink I would suggest thinking more about you're problem and see if you can a) go without categorization or b) perhaps utilize structural info such as document position or something to give you a hint (is either a city or placename or an undetermined) and maybe some lookup tables to help. ie stock symbols are pretty easy to create a pretty full lookup for. You might consider downloading CIA world factbook for a lookup of cities... etc.
As others have already pointed out, this is an exceptionally difficult task. The classic test is a pair of sentences:Time flies like an arrow.Fruit flies like a bananna.
In the first sentence, "flies" is a verb. In the second, it's part of a noun. In the first, "like" is an adverb, but in the second it's a verb. The context doesn't make this particularly easy to sort out either -- there's no obvious difference between "Time" and "Fruit" (both normally nouns). Likewise, "arrow" and "bananna" are both normally nouns.
It can be done -- but it really is decidedly non-trivial.
Although it might not help you much with disambiguation, you could use Cyc. It's a huge database of what things are that's intended to be used in AI applications (though I haven't heard any success stories).