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Deciphering variable information while debugging Java
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The "#" seems to be everywhere when I debug. They are always preceded by some instance/variable name and followed by a (usually three digits) number. What does it mean? I have an image below
Taken from https://medium.com/#andrey_cheptsov/intellij-idea-pro-tips-6da48acafdb7 .
#730 means the 730th object created since the application started.
It is not the hashcode. Length of this can be more or less than 3 digits.
It's totally depends upon which IDE you are using, may eclipse will give something else instead of #730 and in different format also, so it is the way of intellij to maintaining the debugging.
This is Intellij debugger's way of displaying a "unique identifier" for an object. It consists of the short classname and a unique number. The unique number seems to be generated using a simple counter, so the "meaning" of 729 in Owner#729 is (presumably) "this is the 729th object that the debugger has allocated an identifier for". However, you probably shouldn't rely on that.
There is no overt relationship between these numbers and Java identity hashcode values, though I expect Intellij maintains a mapping behind the scenes.
The Owner#5f9d02cb in the screenshot is reminiscent of the result of Object::toString ... when it hasn't been overridden. If that it is what it is, then the 5f9d02cb will be the object's identity hashcode.
This question has already been asked, but the answers seem to be incomplete. What does the first colon in the following context mean?
import hudson.model.SCMS;
(...)
SCMS: for (SCM scm : scmTriggerItem.getSCMs()) {
(...)
Additionally, the colon has some new uses in Java 8.
This question (which has originally been asked two years ago) is different from loop-in-java-code, because it is wider. While the answers of the original question do not mention the use of the colon as label, which is answered in question "loop-in-java-code", the latter question doesn't ask for the use of the colon within for loops nor in Java 8.
As the answer from biziclop shows, there are colon usages in the Java syntax that are easily forgotten and not mentioned in the other two questions.
There are four six uses of the : character in the Java language.
To denote a label. Labels can be used to break or continue to in loops.
In an enhanced for statement (also called for-each statement), which allows easy iteration across collections and arrays.
As one half of the ?: conditional operator.
And since Java 8, as part of the :: method reference operator.
In a switch statement, after case or default.
And you can also use it in an assert statement to specify an error message when the assertion fails.
In your case, SCMS: is a label, while for (SCM scm : scmTriggerItem.getSCMs()) is an enhanced for statement.
You can always look up the full syntax reference of Java here. It is amazingly dull but without it I easily missed two of the six cases.
I am using poi api for my development.Let me explain my process.
1.Compare two sentences.
Example :
A1 : Arun is well.
A2 : Is aruni well.
Here i need to find newly added word in A2 and newly added letter in arun*i* and highlight it with some colours.
How is it possible by using java .?
Thanks..!
This is not specifically java question. It is more of algorithm question. So once you understand algorithm, it will be trivial to implement solution in java in your case.
See this so question: How to Check for Deleted Words Between 2 Sentences in Java
and read about Longest common subsequence"
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What is the best tool that can do text simplification using Java?
Here is an example of text simplification:
John, who was the CEO of a company, played golf.
↓
John played golf. John was the CEO of a company.
I see your problem as a task of converting complex or compound sentence into simple sentences.
Based on literature Sentence Types, a simple sentence is built from one independent clause. A compound and complex sentence is built from at least two clauses. Also, clause must have subject and verb.
So your task is to split sentence into clauses that form your sentence.
Dependency parsing from Stanford CoreNLP is a perfect tools to split compound and complex sentence into simple sentence. You can try the demo online.
From your sample sentence, we will get parse result in Stanford typed dependency (SD) notation as shown below:
nsubj(CEO-6, John-1)
nsubj(played-11, John-1)
cop(CEO-6, was-4)
det(CEO-6, the-5)
rcmod(John-1, CEO-6)
det(company-9, a-8)
prep_of(CEO-6, company-9)
root(ROOT-0, played-11)
dobj(played-11, golf-12)
A clause can be identified from relation (in SD) which category is subject, e.g. nsubj, nsubjpass. See Stanford Dependency Manual
Basic clause can be extracted from head as verb part and dependent as subject part. From SD above, there are two basic clause i.e.
John CEO
John played
After you get basic clause, you can add another part to make your clause a complete and meaningful sentence. To do so, please consult Stanford Dependency Manual.
By the way, your question might be related with Finding meaningful sub-sentences from a sentence
Answer to 3rd comment:
Once you got the pair of subject an verb, i.e. nsubj(CEO-6, John-1), get all dependencies that have link to that dependency, except any dependency which category is subject, then extract unique word from these dependencies.
Based on example, nsubj(CEO-6, John-1), if you start traversing from John-1, you'll get nsubj(played-11, John-1) but you should ignore it since its category is subject.
Next step is traversing from CEO-6 part. You'll get
cop(CEO-6, was-4)
det(CEO-6, the-5)
rcmod(John-1, CEO-6)
prep_of(CEO-6, company-9)
From result above, you got new dependencies to traverse (i.e. find another dependencies that have was-4, the-5, company-9 in either head or dependent).
Now your dependencies are
cop(CEO-6, was-4)
det(CEO-6, the-5)
rcmod(John-1, CEO-6)
prep_of(CEO-6, company-9)
det(company-9, a-8)
In this step, you've finished traversing all dependecies linked to nsubj(CEO-6, John-1). Next, extract words from all head and dependent, then arrange the word in ascending order based on number appended to these words. This number indicating word order in original sentence.
John was the CEO a company
Our new sentence is missing one part, i.e of. This part is hidden in prep_of(CEO-6, company-9). If you read Stanford Dependency Manual, there are two kinds of SD, collapsed and non-collapsed. Please read them to understand why this of is hidden and how to get the word order of this hidden part.
With same approach, you'll get second sentence
John played golf
I think one can design a very simple algorithm for the basic cases of this situation, while real world cases may be too many, that such an approach will become unruly :)
Still I thought I should
think aloud and write my approach and maybe add some python code. My basic idea is that derive a solution from first principles,
mostly by explicitly exposing our model of what is really happening. And not to rely on other theories, models, libraries BEFORE we do one by HAND and from SCRATCH.
Goal: given a sentence, extract subsentences from it.
Example: John, who was the ceo of the company, played Golf.
Expected output: John was the CEO of the company. John played Golf.
Here is my model of what is happening here written out in the form of model assumptions:
(axioms?)
MA1. Simple sentences can be expanded by inserting subsentences.
MA2. A subsentence is a qualification/modification(additional information) on one or more of the entities.
MA3. To insert a subsentence, we put a comma right next to the entity we want to expand on (provide more information on) and attach the subsentence, I am going to call it an extension - and place another comma when the extension ends.
Given this model, the algorithm can be straightforward at least to address the simple cases first.
DETECT: Given a sentence, detect if it has an extension clause, by looking for a pair of commas in the sentence.
EXTRACT: If you find two commas, generate two sentences:
2.1 EXTRACT-BASE: base sentence:
delete everything out between the two commas, You get the base sentence.
2.2 EXTRACT-EXTENSION: extension sentence:
take everything inside the extension sentence, replace 'who' with the word right before it.
That is your second sentence.
PRINT: In fact you should print the extension sentence first, because the base sentence depends on it.
Well, that is our algorithm. Yes it sounds like a hack. It is. But something I am learning now, is that, if you use a trick in one program it is a hack, if it can handle more stuff, it is a technique.
So let us expand and complicate the situation a bit.
Compounding cases:
Example 2. John, who was the CEO of the company, played Golf with Ram, the CFO.
As I am writing it, I noticed that I had omitted the 'who was' phrase for the CFO!
That brings us to the complicating case that our algorithm will fail. Before going there,
let me create a simpler version of 2 that WILL work.
Example 3. John, who was the CEO of the company, played Golf with Ram, who was the CFO.
Example 4. John, the CEO of the company, played Golf with Ram, the CFO.
Wait we are not done yet!
Example 5. John, who is the CEO and Ram, who was the CFO at that time, played Golf, which is an engaging game.
To allow for this I need to extend my model assumptions:
MA4. More than one entities may be expanded likewise, but should not cause confusion because the
extension clause occurs right next to the entity being informed about. (accounts for example 3)
MA5. The 'who was' phrase may be omitted since it can be inferred by the listener. (accounts for example 4)
MA6. Some entities are persons, they will be extended using a 'who' and some entities are things, extended using a 'which'. Either of these extension heads may be omitted.
Now how do we handle these complications in our algorithm?
Try this:
SPLIT-SENTENCE-INTO-BASE-AND-EXTENSIONS:
If sentence contains a comma, look for the following comma, and extract whatever is in between into extension sentence. Continue until you find no more closing comma or opening comma left.
At this point you should have list with base sentence and one or more extension sentences.
PROCESS_EXTENSIONS:
For each extension, if it has 'who is' or 'which is', replace it by name before the extension headword.
If extension does not have a 'who is' or 'which is', place the leading word and and an is.
PRINT: all extension sentences first and then the base sentences.
Not scary.
When I get some time in the next few days, I will add a python implementation.
Thank you
Ravi Annaswamy
You are unlikely to solve this problem using any known algorithm in the general case - this is getting into strong AI territory. Even humans can't parse grammar very well!
Note that the problem is quite ambiguous regarding how far you simplify and what assumptions you are willing to make. You could take your example further and say:
John is assumed to be the name of a being. The race of John is unknown. John played
golf at some point in the past. Golf is assumed to refer to the ball
game called golf, but the variant of golf that John played is unknown.
At some point in the past John was the CEO of a company. CEO is assumed to
mean "Chief Executive Officer" in the context of a company but this is
not specified. The company is unknown.
In case the lesson is not obvious: the more you try to determine the exact meaning of words, the more cans of worms you start to open up...... it takes human-like levels of judgement and interpretation to know when to stop.
You may be able to solve some simpler cases using various Java-based NLP tools: see Is there a good natural language processing library
I believe AlchemyApi is your best option. Still it will require a lot of work on your side to do exactly what you need, and how the most commentators have alredy told you, most probably you'll not get 100% quality results.
The following list contains 1 correct word called "disastrous" and other incorrect words which sound like the correct word?
A. disastrus
B. disasstrous
C. desastrous
D. desastrus
E. disastrous
F. disasstrous
Is it possible to automate generation of wrong choices given a correct word, through some kind of java dictionary API?
No, there is nothing related in java API. You can make a simple algorithm which will do the job.
Just make up some rules about letters permutations and doubling and add generated words to the Set until you get enough words.
There are a number of algorithms for matching words by sound - 'soundex' is the one that springs to mind, but I remember uncovering a few when I did some research on this a couple of years ago. I expect the problem you would find is that they take a word and return a value that represents how the word sounds so you can see if two spellings sound similar (so the words in the question should generate similar values); but I expect doing the reverse, i.e. taking the value and generating similar sounding spellings, would be quite hard.