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06-Jul-2017 08:30

A nice side-effect of the top-down approach is that it’s easy to build parse trees, without much extra overhead; since the tokenizer is creating a new object for each token anyway, we can reuse these objects as nodes in the parse tree.

I'm currently studying LR parsing and in need of an online LR(1) parser to be able to verify my outcome.

The “nud” function (for null denotation) is used when a token appears at the beginning of a language construct, and the “led” function (left denotation) when it appears inside the construct (to the left of the rest of the construct, that is).

The “lbp” value is a binding power, and it controls operator precedence; the higher the value, the tighter a token binds to the tokens that follow.

To see how this scales, let’s add support for a few more math operations.

We need a few more token classes: Note that “mul” and “div” uses a higher binding power than the other operators; this guarantees that when the “mul” operator is invoked in the expression “1 * 2 3”, it only gets the literal “2”, instead of treating “2 3” as a subexpression.

For example, here’s an excerpt from Python’s expression grammar.

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Could anyone provide me with a link of such a tool?This makes sure that the expression parser stops when it reaches the end of the program. To use it, we need a tokenizer that can generate the right kind of token objects for a given source program.