Why we use parsing

This term also refers to the study of these rules, and this file includes morphology, phonology, and syntax. It is capable of describing many, of the syntax of programming languages. Notational conventions symbol may be indicated by enclosing the element in square brackets. It is a choice of the alternative which may use the symbol within the single rule.

It may be enclosed by parenthesis [,] when needed. A CFG is a left-recursive grammar that has at least one production of the type. The rules in a context-free grammar are mainly recursive.

A syntax analyser checks that specific program satisfies all the rules of Context-free grammar or not. If it does meet, these rules syntax analysers may create a parse tree for that programme. Grammar derivation is a sequence of grammar rule which transforms the start symbol into the string.

When the sentential form of input is scanned and replaced in left to right sequence, it is known as left-most derivation. The sentential form which is derived by the left-most derivation is called the left-sentential form.

Rightmost derivation scan and replace the input with production rules, from right to left, sequence. But this parser has the restriction of training with a limited set of training data. This is another way we can do dependency parsing with NLTK. Stanford parser is a state-of-the-art dependency parser. NLTK has a wrapper around it. Language model for desired language. For example, English language model.

Natural Language Toolkit - Parsing Advertisements. But how does this process work in general? This is described in more detail in your assigned reading, but here's an overview of the steps involved:. We looked at some examples of this process in class. See your book for more details and examples. Parsing is the process of turning a stream of tokens into a parse tree, according to the rules of some grammar.

This is the second, and more significant, part of syntax analysis after scanning. We are going to see how to write grammars to describe programming languages. You already did this a bunch in Theory class. But in this class, the concern is deeper: we not only need the grammar to properly define the language in a strict mathematical sense; it also needs to make the parser be fast.

How fast? Well, scanning is going to be linear-time; just take one transition for every character in the input. We want our parsers to work in linear-time in the size of the input as well. This is important: if your code gets 10 times as long, you're OK if it takes 10 times as long to compile.

But if it suddenly takes times longer to compile, that's going to be a problem! In fact, that's what the case would be if we allowed any old grammar for our languages; the fastest algorithms for parsing any language have complexity O n 3. What this means is, we have to talk about parsers and grammars together. The grammar determines how fast the parser can go, and the choice of parser also affects what kind of grammar we will write!

Parsers are classified into two general categories, and we will look at both kinds in some detail. Here's a quick overview. We also saw some preliminary examples in class of how to parse a string top-down or bottom-up. Top-down parsers construct the parse tree by starting at the root the start symbol. The basic algorithm for top-down parsing is:.

The big question that top-down parsers have to answer is on step 2 : which right-hand side do we take? This is why top-down parsers are also called predictive parsers ; they have to predict what is coming next, every time they see a non-terminal symbol. Top-down parsers work well with a kind of grammar called LL. In fact, sometimes top-down parsers are called LL parsers. We'll mostly focus on a special case called LL 1 grammars, which will be defined in the next section.

Bottom-up parsers construct the parse tree by starting at the leaves of the tree the tokens , and building up the higher constructs the non-terminals , until the leaves all form together into a single parse tree. The basic bottom-up parsing algorithm is:. The big decision for bottom-up parsers is whether to do 1 or 2 every step along the way; that is, whether to shift or to reduce and how to reduce, if there is more than one option.

For this reason, bottom-up parsers are also called shift-reduce parsers. Bottom-up parsers work well with LR grammars, so they're sometimes called LR parsers. All of 2. A grammar is called LL 1 if it can be parsed by a top-down parser that only requires a single token of "look-ahead".

Remember that top-down parsers use look-ahead to predict which right-hand side of a non-terminal's production rules to take.

So with an LL 1 grammar, we can always tell which right-hand side to take just by looking at whatever the next token is. There are two common issues that make a grammar not be LL 1 : common prefixes and left recursion. Fortunately, both these issues have somewhat standard fixes. Let's see what they are. Do you see what the problem is? If we are trying to expand an instance of X in the top-down parse tree, we need to determine which of the two possible rules to apply, based on the next token of look-ahead.

But that next token will always be an a , which doesn't give us enough information to distinguish the rules! The standard fix is to "factor out" the common prefix. First, we make a "tail rule" that has every part of each right-hand side except the common prefix. This should be a new non-terminal in the language, like:. Here, Y gets the part of each right-hand side from X , but without the common prefix of a. Once we have this tail rule, we can combine all the productions of the original nonterminal into a single rule with the common prefix, followed by the new non-terminal.

So the whole grammar becomes:. Having clarified the role of regular expressions, we can look at the general structure of a parser.

A complete parser is usually composed of two parts: a lexer , also known as scanner or tokenizer , and the proper parser. The parser needs the lexer because it does not work directly on the text but on the output produced by the lexer.

Not all parsers adopt this two-step schema; some parsers do not depend on a separate lexer and they combine the two steps. They are called scannerless parsers. A lexer and a parser work in sequence: the lexer scans the input and produces the matching tokens; the parser then scans the tokens and produces the parsing result. The lexer scans the text and finds 4 , 3 , and 7 , and then a space.

The job of the lexer is to recognize that the characters constitute one token of type NUM. The definitions used by lexers and parsers are called rules or productions. It is now typical to find suites that can generate both a lexer and parser.

In the past, it was instead more common to combine two different tools: one to produce the lexer and one to produce the parser. For example, this was the case of the venerable lex and yacc couple: using lex, it was possible to generate a lexer, while using yacc, it was possible to generate a parser. Scannerless parsers operate differently because they process directly the original text instead of processing a list of tokens produced by a lexer. That is to say, a scannerless parser works as a lexer and a parser combined.

While it is certainly important to know for debugging purposes if a particular parsing tool is a scannerless parser, in many cases, it is not relevant to define a grammar. That is because you usually still define the pattern that group sequences of characters to create virtual tokens; then, you combine them to obtain the final result. This is simply because it is more convenient to do so. In other words, the grammar of a scannerless parser looks very similar to one for a tool with separate steps.

Again, you should not confuse how things are defined for your convenience and how things work behind the scenes.