I'm having a hard time figuring out how to recognize some text only if it is preceded and followed by certain things. The task is to recognize AND, OR, and NOT, but not if they're part of a word:
They should be recognized here:
x AND y
(x)AND(y)
NOT x
NOT(x)
but not here:
xANDy
abcNOTdef
AND gets recognized if it is surrounded by spaces or parentheses. NOT gets recognized if it is at the beginning of the input, preceded by a space, and followed by a space or parenthesis.
The trouble is that if I include parentheses as part of the definition of AND or NOT, they get consumed, and I need them to be separate tokens.
Is there some kind of lookahead/lookbehind syntax I can use?
EDIT:
Per the comments, here's some context. The problem is related to this problem: Antlr: how to match everything between the other recognized tokens? My working solution there is just to recognize AND, OR, etc. and skip everything else. Then, in a second pass over the text, I manually grab the characters not otherwise covered, and run a totally different tokenizer on it. The reason is that I need a custom, human-language-specific tokenizer for this content, which means that I can't, in advance, describe what is an ID. Each human language is different. I want to combine, in stages, a single query-language tokenizer, and then apply a human-language tokenizer to what's left.
ANTLR is not the right tool for this task. A normal parser is designed for a specific language, that is, a set of sentences consisting of elements that are known at parser creation time. There are ways to make this more flexible, e.g. by using a runtime function in a predicate to recognize words not defined in the grammar, but this has other (negative) implications.
What you should consider is NLP for a different approach to process natural language. It's more than just skipping things between two known tokens.
Related
I am trying to create a simple programming language from scratch (interpreter) but I wonder why I should use a lexer.
For me, it looks like it would be easier to create a parser that directly parses the code. what am I overlooking?
I think you'll agree that most languages (likely including the one you are implementing) have conceptual tokens:
operators, e.g * (usually multiply), '(', ')', ;
keywords, e.g., "IF", "GOTO"
identifiers, e.g. FOO, count, ...
numbers, e.g. 0, -527.23E-41
comments, e.g., /* this text is ignored in your file */
whitespace, e.g., sequences of blanks, tabs and newlines, that are ignored
As a practical matter, it takes a specific chunk of code to scan for/collect the characters that make each individual token. You'll need such a code chunk for each type of token your language has.
If you write a parser without a lexer, at each point where your parser is trying to decide what comes next, you'll have to have ALL the code that recognize the tokens that might occur at that point in the parse. At the next parser point, you'll need all the code to recognize the tokens that are possible there. This gives you an immense amount of code duplication; how many times do you want the code for blanks to occur in your parser?
If you think that's not a good way, the obvious cure to is remove all the duplication: place the code for each token in a subroutine for that token, and at each parser place, call the subroutines for the tokens. At this point, in some sense, you already have a lexer: an isolated collection of code to recognize tokens. You can code perfectly fine recursive descent parsers this way.
The next thing you'll discover is that you call the token subroutines for many of the tokens at each parser point. Even that seems like a lot of work and duplication. So, replace all the calls with a single "GetNextToken" call, that itself invokes the token recognizing code for all tokens, and returns a enum that identifies the specific token encountered. Now your parser starts to look reasonable: at each parser point, it makes one call on GetNextToken, and then branches on enum returned. This is basically the interface that people have standardized on as a "lexer".
One thing you will discover is the token-lexers sometimes have trouble with overlaps; keywords and identifiers usually have this trouble. It is actually easier to merge all the token recognizers into a single finite state machine, which can then distinguish the tokens more easily. This also turns out to be spectacularly fast when processing the programming language source text. Your toy language may never parse more than 100 lines, but real compilers process millions of lines of code a day, and most of that time is spent doing token recognition ("lexing") esp. white space suppression.
You can code this state machine by hand. This isn't hard, but it is rather tedious. Or, you can use a tool like FLEX to do it for you, that's just a matter of convenience. As the number of different kinds of tokens in your language grows, the FLEX solution gets more and more attractive.
TLDR: Your parser is easier to write, and less bulky, if you use a lexer. In addition, if you compile the individual lexemes into a state machine (by hand or using a "lexer generator"), it will run faster and that's important.
Well, for intelligently simplified programing language you can get away without either lexer or parser :-) Not kidding. Look up Forth. You can start with tags here on SO (gforth is GNU's) and then go to the Standard's site which has pointers to a few interpreters, sites and its Glossary.
Then you can check out Win32Forth and that should keep you busy for quite a while :-)
Interpreter also compiles (when you invoke words that switch system to compilation context). All without a distinct parser. Lookahead is actually lookbehind :-) - not kidding. It rarely absorbs one following word (== lookahead is max 1). The "words" (aka tokens) are at the same time keywords and variable names and they all live in a Dictionary. There's a whole online book at that site (plus pdf).
Control structures are also just words (they compile a few addresses and jumps on the fly).
You can find old Journals there as well, covering a wide spectrum from machine code generation to object oriented extensions. Yes still without parser - believe it or not.
There used to be more sophisticated (commercial) Forth systems which were reducing words to machine call instructions with immediate addressing (makes the engine run 2-4 times faster) but even plain interpreters were always considered to be fast. One is apparently still active - SwiftForth, but don't expect any freebies there.
There's one Forth on GitHub CiForth which is quite spartanic but has builds and releases for Win, Linux and Mac, 32 and 64 so you can just download and run. Claims to have a 16-bit build as well :-) For embedded systems I suppose.
I was thinking to make a Pug parser but besides the indents are well-known to be context-sensitive (that can be trivially hacked with a lexer feedback loop to make it almost context-free which is adopted by Python), what otherwise makes it not context-free?
XML tags are definitely not context-free, that each starting tag needs to match an end tag, but Pug does not have such restriction, that makes me wonder if we could just parse each starting identifier as a production for a tag root.
The main thing that Pug seems to be missing, at least from a casual scan of its website, is a formal description of its syntax. Or even an informal description. Perhaps I wasn't looking in right places.
Still, based on the examples, it doesn't look awful. There will be some challenges; in particular, it does not have a uniform tokenisation context, so the scanner is going to be complicated, not just because of the indentation issue. (I got the impression from the section on whitespace that the indentation rule is much stricter than Python's, but I didn't find a specification of what it is exactly. It appeared to me that leading whitespace after the two-character indent is significant whitespace. But that doesn't complicate things much; it might even simplify the task.)
What will prove interesting is handling embedded JavaScript. You will at least need to tokenise the embedded JS, and the corner cases in the JS spec make it non-trivial to tokenise without parsing. Anyway, just tokenising isn't sufficient to know where the embedded code terminates. (For the lexical challenge, consider the correct identification of regular expression literals. /= might be the start of a regex or it might be a divide-and-assign operator; how a subsequent { is tokenised will depend on that decision.) Template strings present another challenge (recursive embedding). However, JavaScript parsers do exist, so you might be able to leverage one.
In other words, recognising tag nesting is not going to be the most challenging part of your project. Once you've identified that a given token is a tag, the nesting part is trivial (and context-free) because it is precisely defined by the indentation, so a DEDENT token will terminate the tag.
However, it is worth noting that tag parsing is not particularly challenging for XML (or XML-like HTML variants). If you adopt the XML rule that close tags cannot be omitted (except for self-closing tags), then the tagname in a close tag does not influence the parse of a correct input. (If the tagname in the close tag does not match the close tag in the corresponding open tag, then the input is invalid. But the correspondence between open and close tags doesn't change.) Even if you adopt the HTML-5 rule that close tags cannot be omitted except in the case of a finite list of special-case tagnames, then you could theoretically do the parse with a CFG. (However, the various error recovery rules in HTML-5 are far from context free, so that would only work for input which did not require rematching of close tags.)
Ira Baxter makes precisely this point in the cross-linked post he references in a comment: you can often implement context-sensitive aspects of a language by ignoring them during the parse and detecting them in a subsequent analysis, or even in a semantic predicate during the parse. Correct matching of open- and close tagnames would fall into this category, as would the "declare-before-use" rule in languages where the declaration of an identifier does not influence the parse. (Not true of C or C++, but true in many other languages.)
Even if these aspects cannot be ignored -- as with C typedefs, for example -- the simplest solution might be to use an ambiguous CFG and a parsing technology which produces all possible parses. After the parse forest is generated, you could walk the alternatives and reject the ones which are inconsistent. (In the case of C, that would include an alternative parse in which a name was typedef'd and then used in a context where a typename is not valid.)
I found the powerful RegexNER and it's superset TokensRegex from Stanford CoreNLP.
There are some rules that should give me fine results, like the pattern for PERSONs with titles:
"g. Meho Mehic" or "gdin. N. Neko" (g. and gdin. are abbrevs in Bosnian for mr.).
I'm having some trouble with existing tokenizer. It splits some strings on two tokens and some leaves as one, for example, token "g." is left as word <word>g.</word> and token "gdin." is split on 2 tokens: <word>gdin</word> and <word>.</word>.
That causes trouble with my regex, I have to deal with one-token and multi-token cases (note the two "maybe-dot"s), RegexNER example:
( /g\.?|gdin\.?/ /\./? ([{ word:/[A-Z][a-z]*\.?/ }]+) ) PERSON
Also, this causes another issue, with sentence splitting, some sentences are not well recognized so regex fails... For example, when a sentence contains "gdin." it will split it on two, so a dot will end the (non-existing) sentence. I managed to bypass this with ssplit.isOneSentence = true for now.
Questions:
Do I have to make my own tokenizer, and how? (to merge some tokens like "gdin.")
Are there any settings I missed that could help me with this?
Ok I thought about this for a bit and can actually think of something pretty straight forward for your case. One thing you could do is add "gdin" to the list of titles in the tokenizer.
The tokenizer rules are in edu.stanford.nlp.process.PTBLexer.flex (look at line 741)
I do not really understand the tokenizer that well, but clearly there are a list of job titles in there, so they must be cases where it will not split off the period.
This will of course require you to work with a custom build of Stanford CoreNLP.
You can get the full code at our GitHub:https://github.com/stanfordnlp/CoreNLP
There are instructions on the main page for building a jar with all of the main Stanford CoreNLP classes. I think if you just run the ant process it will automatically generate the new PTBLexer.java based on PTBLexer.flex.
I'm currently in the process of creating a programming language. I've laid out my entire design and am in progress of creating the Lexer for it. I have created numerous lexers and lexer generators in the past, but have never come to adopt the "standard", if one exists.
Is there a specific way a lexer should be created to maximise capability to use it with as many parsers as possible?
Because the way I design mine, they look like the following:
Code:
int main() {
printf("Hello, World!");
}
Lexer:
[
KEYWORD:INT, IDENTIFIER:"main", LEFT_ROUND_BRACKET, RIGHT_ROUNDBRACKET, LEFT_CURLY_BRACKET,
IDENTIFIER:"printf", LEFT_ROUND_BRACKET, STRING:"Hello, World!", RIGHT_ROUND_BRACKET, COLON,
RIGHT_CURLY_BRACKET
]
Is this the way Lexer's should be made? Also as a side-note, what should my next step be after creating a Lexer? I don't really want to use something such as ANTLR or Lex+Yacc or Flex+Bison, etc. I'm doing it from scratch.
If you don't want to use a parser generator [Note 1], then it is absolutely up to you how your lexer provides information to your parser.
Even if you do use a parser generator, there are many details which are going to be project-dependent. Sometimes it is convenient for the lexer to call the parser with each token; other times is is easier if the parser calls the lexer; in some cases, you'll want to have a driver which interacts separately with each component. And clearly, the precise datatype(s) of your tokens will vary from project to project, which can have an impact on how you communicate as well.
Personally, I would avoid use of global variables (as in the original yacc/lex protocol), but that's a general style issue.
Most lexers work in streaming mode, rather than tokenizing the entire input and then handing the vector of tokens to some higher power. Tokenizing one token at a time has a number of advantages, particularly if the tokenization is context-dependent, and, let's face it, almost all languages have some impurity somewhere in their syntax. But, again, that's entirely up to you.
Good luck with your project.
Notes:
Do you also forgo the use of compilers and write all your code from scratch in assembler or even binary?
Is there a specific way a lexer should be created to maximise capability to use it with as many parsers as possible?
In the lexers I've looked at, the canonical API is pretty minimal. It's basically:
Token readNextToken();
The lexer maintains a reference to the source text and its internal pointers into where it is currently looking. Then, every time you call that, it scans and returns the next token.
The Token type usually has:
A "type" enum for which kind of token it is: string, operator, identifier, etc. There are usually special kinds for "EOF", meaning a special terminator token that is produced after the end of the input, and "ERROR" for the rare cases where a syntax error comes from the lexical grammar. This is mainly just unterminated string literals or totally unknown characters in the source.
The source text of the token.
Sometimes literals are converted to their proper value representation during lexing in which case you'll have that value too. So a number token would have "123" as text but also have the numeric value 123. Or you can do that during parsing/compilation.
Location within the source file of the token. This is for error reporting. Usually 1-based line and column, but can also just be start and end byte offsets. The latter is a little faster to produce and can be converted to line and column lazily if needed.
Depending on your grammar, you may need to be able to rewind the lexer too.
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I am a college student getting my Computer Science degree. A lot of my fellow students really haven't done a lot of programming. They've done their class assignments, but let's be honest here those questions don't really teach you how to program.
I have had several other students ask me questions about how to parse things, and I'm never quite sure how to explain it to them. Is it best to start just going line by line looking for substrings, or just give them the more complicated lecture about using proper lexical analysis, etc. to create tokens, use BNF, and all of that other stuff? They never quite understand it when I try to explain it.
What's the best approach to explain this without confusing them or discouraging them from actually trying.
I'd explain parsing as the process of turning some kind of data into another kind of data.
In practice, for me this is almost always turning a string, or binary data, into a data structure inside my Program.
For example, turning
":Nick!User#Host PRIVMSG #channel :Hello!"
into (C)
struct irc_line {
char *nick;
char *user;
char *host;
char *command;
char **arguments;
char *message;
} sample = { "Nick", "User", "Host", "PRIVMSG", { "#channel" }, "Hello!" }
Parsing is the process of analyzing text made of a sequence of tokens to determine its grammatical structure with respect to a given (more or less) formal grammar.
The parser then builds a data structure based on the tokens. This data structure can then be used by a compiler, interpreter or translator to create an executable program or library.
(source: wikimedia.org)
If I gave you an english sentence, and asked you to break down the sentence into its parts of speech (nouns, verbs, etc.), you would be parsing the sentence.
That's the simplest explanation of parsing I can think of.
That said, parsing is a non-trivial computational problem. You have to start with simple examples, and work your way up to the more complex.
What is parsing?
In computer science, parsing is the process of analysing text to determine if it belongs to a specific language or not (i.e. is syntactically valid for that language's grammar). It is an informal name for the syntactic analysis process.
For example, suppose the language a^n b^n (which means same number of characters A followed by the same number of characters B). A parser for that language would accept AABB input and reject the AAAB input. That is what a parser does.
In addition, during this process a data structure could be created for further processing. In my previous example, it could, for instance, to store the AA and BB in two separate stacks.
Anything that happens after it, like giving meaning to AA or BB, or transform it in something else, is not parsing. Giving meaning to parts of an input sequence of tokens is called semantic analysis.
What isn't parsing?
Parsing is not transform one thing into another. Transforming A into B, is, in essence, what a compiler does. Compiling takes several steps, parsing is only one of them.
Parsing is not extracting meaning from a text. That is semantic analysis, a step of the compiling process.
What is the simplest way to understand it?
I think the best way for understanding the parsing concept is to begin with the simpler concepts. The simplest one in language processing subject is the finite automaton. It is a formalism to parsing regular languages, such as regular expressions.
It is very simple, you have an input, a set of states and a set of transitions. Consider the following language built over the alphabet { A, B }, L = { w | w starts with 'AA' or 'BB' as substring }. The automaton below represents a possible parser for that language whose all valid words starts with 'AA' or 'BB'.
A-->(q1)--A-->(qf)
/
(q0)
\
B-->(q2)--B-->(qf)
It is a very simple parser for that language. You start at (q0), the initial state, then you read a symbol from the input, if it is A then you move to (q1) state, otherwise (it is a B, remember the remember the alphabet is only A and B) you move to (q2) state and so on. If you reach (qf) state, then the input was accepted.
As it is visual, you only need a pencil and a piece of paper to explain what a parser is to anyone, including a child. I think the simplicity is what makes the automata the most suitable way to teaching language processing concepts, such as parsing.
Finally, being a Computer Science student, you will study such concepts in-deep at theoretical computer science classes such as Formal Languages and Theory of Computation.
Have them try to write a program that can evaluate arbitrary simple arithmetic expressions. This is a simple problem to understand but as you start getting deeper into it a lot of basic parsing starts to make sense.
Parsing is about READING data in one format, so that you can use it to your needs.
I think you need to teach them to think like this. So, this is the simplest way I can think of to explain parsing for someone new to this concept.
Generally, we try to parse data one line at a time because generally it is easier for humans to think this way, dividing and conquering, and also easier to code.
We call field to every minimum undivisible data. Name is field, Age is another field, and Surname is another field. For example.
In a line, we can have various fields. In order to distinguish them, we can delimit fields by separators or by the maximum length assign to each field.
For example:
By separating fields by comma
Paul,20,Jones
Or by space (Name can have 20 letters max, age up to 3 digits, Jones up to 20 letters)
Paul 020Jones
Any of the before set of fields is called a record.
To separate between a delimited field record we need to delimit record. A dot will be enough (though you know you can apply CR/LF).
A list could be:
Michael,39,Jordan.Shaquille,40,O'neal.Lebron,24,James.
or with CR/LF
Michael,39,Jordan
Shaquille,40,O'neal
Lebron,24,James
You can say them to list 10 nba (or nlf) players they like. Then, they should type them according to a format. Then make a program to parse it and display each record. One group, can make list in a comma-separated format and a program to parse a list in a fixed size format, and viceversa.
Parsing to me is breaking down something into meaningful parts... using a definable or predefined known, common set of part "definitions".
For programming languages there would be keyword parts, usable punctuation sequences...
For pumpkin pie it might be something like the crust, filling and toppings.
For written languages there might be what a word is, a sentence, what a verb is...
For spoken languages it might be tone, volume, mood, implication, emotion, context
Syntax analysis (as well as common sense after all) would tell if what your are parsing is a pumpkinpie or a programming language. Does it have crust? well maybe it's pumpkin pudding or perhaps a spoken language !
One thing to note about parsing stuff is there are usually many ways to break things into parts.
For example you could break up a pumpkin pie by cutting it from the center to the edge or from the bottom to the top or with a scoop to get the filling out or by using a sledge hammer or eating it.
And how you parse things would determine if doing something with those parts will be easy or hard.
In the "computer languages" world, there are common ways to parse text source code. These common methods (algorithims) have titles or names. Search the Internet for common methods/names for ways to parse languages. Wikipedia can help in this regard.
In linguistics, to divide language into small components that can be analyzed. For example, parsing this sentence would involve dividing it into words and phrases and identifying the type of each component (e.g.,verb, adjective, or noun).
Parsing is a very important part of many computer science disciplines. For example, compilers must parse source code to be able to translate it into object code. Likewise, any application that processes complex commands must be able to parse the commands. This includes virtually all end-user applications.
Parsing is often divided into lexical analysis and semantic parsing. Lexical analysis concentrates on dividing strings into components, called tokens, based on punctuationand other keys. Semantic parsing then attempts to determine the meaning of the string.
http://www.webopedia.com/TERM/P/parse.html
Simple explanation: Parsing is breaking a block of data into smaller pieces (tokens) by following a set of rules (using delimiters for example),
so that this data could be processes piece by piece (managed, analysed, interpreted, transmitted, ets).
Examples: Many applications (like Spreadsheet programs) use CSV (Comma Separated Values) file format to import and export data. CSV format makes it possible for the applications to process this data with a help of a special parser.
Web browsers have special parsers for HTML and CSS files. JSON parsers exist. All special file formats must have some parsers designed specifically for them.