Rley

A Ruby library for constructing general parsers for any context-free language.

What is Rley?

Rley uses the Earley algorithm which is a general parsing algorithm that can handle any context-free grammar. Earley parsers can literally swallow anything that can be described by a context-free grammar. That's why Earley parsers find their place in so many NLP (Natural Language Processing) libraries/toolkits.

In addition, Rley goes beyond most Earley parser implementations by providing support for ambiguous parses. Indeed, it delivers the results of a parse as a Shared Packed Parse Forest (SPPF). A SPPF is a data structure that allows to encode efficiently all the possible parse trees that result from an ambiguous grammar.

As another distinctive mark, Rley is also the first Ruby implementation of a parsing library based on the new Grammar Flow Graph approach .

What it can do?

Maybe parsing algorithms and internal implementation details are of lesser interest to you and the good question to ask is "what Rley can really do?".

In a nutshell:

Rley can parse context-free languages that other well-known libraries cannot handle
Built-in support for ambiguous grammars that typically occur in NLP

In short, the foundations of Rley are strong enough to be useful in a large application range such as:

computer languages -e.g. Simple Regex Language - ,
artificial intelligence and
Natural Language Processing.

Features

Simple API for context-free grammar definition,
Allows ambiguous grammars,
Generates shared packed parse forests,
Accepts left-recursive rules/productions,
Provides syntax error detection and reporting.

Compatibility

Rley supports the following Ruby implementations:

MRI 2.3
MRI 2.4
MRI 2.5
MRI 2.6
JRuby 9.1+

Getting Started

Installation

Installing the latest stable version is simple:

$ gem install rley

A whirlwind tour of Rley

The purpose of this section is show how to create a parser for a minimalistic English language subset. The tour is organized as follows:

Creating facade object of Rley library
Defining the language grammar
Creating a lexicon
Creating a tokenizer
Parsing some input
Generating the parse tree

The complete source code of the example used in this tour can be found in the examples directory

Creating facade object of Rley library

    require 'rley' # Load Rley library

    # Let's create a facade object called 'engine'
    # It provides a unified, higher-level interface
    engine = Rley.Engine.new

Defining the language grammar

The subset of English grammar is based on an example from the NLTK book.

    engine.build_grammar do
      # Terminal symbols (= word categories in lexicon)
      add_terminals('Noun', 'Proper-Noun', 'Verb')
      add_terminals('Determiner', 'Preposition')

      # Here we define the productions (= grammar rules)
      rule 'S' => 'NP VP'
      rule 'NP' => 'Proper-Noun'
      rule 'NP' => 'Determiner Noun'
      rule 'NP' => 'Determiner Noun PP'
      rule 'VP' => 'Verb NP'
      rule 'VP' => 'Verb NP PP'
      rule 'PP' => 'Preposition NP'
    end

Creating a lexicon

    # To simplify things, lexicon is implemented as a Hash with pairs of the form:
    # word => terminal symbol name
    Lexicon = {
      'man' => 'Noun',
      'dog' => 'Noun',
      'cat' => 'Noun',
      'telescope' => 'Noun',
      'park' => 'Noun',  
      'saw' => 'Verb',
      'ate' => 'Verb',
      'walked' => 'Verb',
      'John' => 'Proper-Noun',
      'Mary' => 'Proper-Noun',
      'Bob' => 'Proper-Noun',
      'a' => 'Determiner',
      'an' => 'Determiner',
      'the' => 'Determiner',
      'my' => 'Determiner',
      'in' => 'Preposition',
      'on' => 'Preposition',
      'by' => 'Preposition',
      'with' => 'Preposition'
    }.freeze

Creating a tokenizer

  require 'strscan'

    # A tokenizer reads the input string and converts it into a sequence of tokens.
    # Remark: Rley doesn't provide tokenizer functionality.
    # Highly simplified tokenizer implementation
    def tokenizer(aTextToParse)
      scanner = StringScanner.new(aTextToParse)
      tokens = []

      loop do
        scanner.skip(/\s+/)
        curr_pos = scanner.pos
        word = scanner.scan(/\S+/)
        break unless word

        term_name = Lexicon[word]
        raise StandardError, "Word '#{word}' not found in lexicon" if term_name.nil?
        pos = Rley::Lexical::Position.new(1, curr_pos + 1)
        tokens << Rley::Lexical::Token.new(word, term_name, pos)
      end

      return tokens
    end

More ambitious NLP applications will surely rely on a Part-of-Speech tagger instead of creating a lexicon and tokenizer from scratch. Here are a few Ruby Part-of-Speech gems:

Parsing some input

    input_to_parse = 'John saw Mary with a telescope'
    # Convert input text into a sequence of token objects...
    tokens = tokenizer(input_to_parse)
    result = engine.parse(tokens)

    puts "Parsing successful? #{result.success?}" # => Parsing successful? true

At this stage, we're done with parsing. What we need next are convenient means to exploit the parse result. As it is, the result variable in the last code snippet above is a data structure ("Earley item sets") that is highly depending on the intricate details of the Earley's parsing algorithm. Obviously, it contains all the necessary data to exploit the parsing results but it is rather low-level and inconvenient from a programming viewpoint. Therefore, Rley provides out of the box two convenient data structures for representing the parse outcome:

Parse tree (optimal when the parse is unambiguous)
Parse forest (a more sophisticated data structure that copes with ambiguity)

For our whirlwind tour, we will opt for parse trees.

Generating the parse tree

    ptree = engine.convert(result)

OK. Now that we have the parse tree, what we can do with it? One option is to manipulate the parse tree and its node directly. For instance, one could write code to customize and transform the parse tree. This approach gives most the of flexibility needed for advanced applications. The other, more common option is to use an Rley::ParseTreeVisitor instance. Such a visitor walks over the parse tree nodes and generates visit events that are dispatched to subscribed event listeners. All this may, at first, sound complicated but the coming code snippets show it otherwise.

Let's do it by:

Creating a parse tree visitor
Using one of the built-in visit subscribers specifically created to render the parse tree in a given output format.

Creating a parse tree visitor

Good news: creating a parse tree visitor for the parse tree ptree is just an one-liner:

    # Let's create a parse tree visitor
    visitor = engine.ptree_visitor(ptree)

Visiting the parse tree

Unsurprisingly, to start the parse tree visit, one calls the #start method:

    visitor.start

If you try the above line, no particular result will be visible and for a good reason: no object was specified as a visit event subscriber. As a convenience, Rley bundles a number of formatter classes that were designed to listen to the visit event and then render the parse tree in a specific format. To begin with, we'll use the simple formatter Rley::Formatter::Debug class. Its purpose is just to print out the visit event name.

Remove the line with the call to the #start method and replace it with the two statements:

    # Let's create a formatter (i.e. visit event listener)
    renderer = Rley::Formatter::Debug.new($stdout)

    # Subscribe the formatter to the visitor's event and launch the visit
    renderer.render(visitor)

These two lines will generate the following output:

before_ptree
  before_non_terminal
    before_subnodes
      before_non_terminal
        before_subnodes
          before_terminal
          after_terminal
        after_subnodes
      after_non_terminal
      before_non_terminal
        before_subnodes
          before_terminal
          after_terminal
          before_non_terminal
            before_subnodes
              before_terminal
              after_terminal
            after_subnodes
          after_non_terminal
          before_non_terminal
            before_subnodes
              before_terminal
              after_terminal
              before_non_terminal
                before_subnodes
                  before_terminal
                  after_terminal
                  before_terminal
                  after_terminal
                after_subnodes
              after_non_terminal
            after_subnodes
          after_non_terminal
        after_subnodes
      after_non_terminal
    after_subnodes
  after_non_terminal
after_ptree

At least is something visible: these are the parse tree visit events. Note that the indentation of event names depends on the nesting level of the tree node being visited.

Not really impressive? So let's use another formatter...

Visualizing the parse tree structure

If one replaces the previous formatter by an instance of Rley::Formatter::Asciitree the output now shows the parse tree structure.

    # Let's create a formatter that will render the parse tree with characters
    renderer = Rley::Formatter::Asciitree.new($stdout)

    # Subscribe the formatter to the visitor's event and launch the visit
    renderer.render(visitor)

The outputs looks like this:

S
+-- NP
|   +-- Proper-Noun: 'John'
+-- VP
    +-- Verb: 'saw'
    +-- NP
    |   +-- Proper-Noun: 'Mary'
    +-- PP
        +-- Preposition: 'with'
        +-- NP
            +-- Determiner: 'a'
            +-- Noun: 'telescope'

If you are more inclined for graphical representation, then replace the last formatter by yet another one:

    # Let's create a formatter that will render the parse tree in labelled bracket notation
    renderer = Rley::Formatter::BracketNotation.new($stdout)

    # Subscribe the formatter to the visitor's event and launch the visit
    renderer.render(visitor)

This results in the strange-looking output:

[S [NP [Proper-Noun John]][VP [Verb saw][NP [Proper-Noun Mary]][PP [Preposition with][NP [Determiner a][Noun telescope]]]]]

This output is in a format that is recognized by many NLP softwares. The next diagram was created by copy-pasting the output above in the online tool RSyntaxTree. By the way, this tool is also a Ruby gem, rsyntaxtree.

Sample parse tree diagram

Error reporting

Rley is a non-violent parser, that is, it won't throw an exception when it detects a syntax error. Instead, the parse result will be marked as non-successful. The parse error can then be identified by calling the GFGParsing#failure_reason method. This method returns an error reason object which can help to produce an error message.

Consider the example from the Parsing some input section above and, as an error, we delete the verb saw in the sentence to parse.

    # Verb has been removed from the sentence on next line
    input_to_parse = 'John Mary with a telescope'
    # Convert input text into a sequence of token objects...
    tokens = tokenizer(input_to_parse)
    result = engine.parse(tokens)

    puts "Parsing successful? #{result.success?}" # => Parsing successful? false
    exit(1)

As expected, the parse is now failing.
To get an error message, one just need to retrieve the error reason and ask it to generate a message.

    # Show error message if parse fails...
    puts result.failure_reason.message unless result.success?

Re-running the example with the error, results in the error message:

  Syntax error at or near token line 1, column 6 >>>Mary<<<
  Expected one 'Verb', found a 'Proper-Noun' instead.

The standard Rley message not only inform about the location of the mistake, it also provides some hint by disclosing its expectations.

Let's experiment again with the original sentence but without the word telescope.

    # Last word has been removed from the sentence on next line
    input_to_parse = 'John saw Mary with a '
    # Convert input text into a sequence of token objects...
    tokens = tokenizer(input_to_parse)
    result = engine.parse(tokens)

    puts "Parsing successful? #{result.success?}" # => Parsing successful? false
    unless result.success?
      puts result.failure_reason.message
      exit(1)
    end

This time, the following output is displayed:

  Parsing successful? false
  Premature end of input after 'a' at position line 1, column 20
  Expected one 'Noun'.

Again, the resulting error message is user-friendly.

Examples

The project source directory contains several example scripts that demonstrate how grammars are to be constructed and used.

Other similar Ruby projects

Rley isn't the sole implementation of the Earley parser algorithm in Ruby.
Here are a few other ones:

Kanocc gem -- Advertised as a Ruby based parsing and translation framework.
Although the gem dates from 2009, the author still maintains its in a public repository in Github
The grammar symbols (tokens and non-terminals) must be represented as (sub)classes. Grammar rules are methods of the non-terminal classes. A rule can have a block code argument that specifies the semantic action when that rule is applied.
lc1 project -- Advertised as a combination of Earley and Viterbi algorithms for [Probabilistic] Context Free Grammars
Aimed in parsing brazilian portuguese.
earley project -- An Earley parser (grammar rules are specified in JSON format).
The code doesn't seem to be maintained: latest commit dates from Nov. 2011.
linguist project -- Advertised as a library for parsing context-free languages.
It is a recognizer not a parser. In other words it can only tell whether a given input conforms to the grammar rules or not. As such it cannot build parse trees.
The code doesn't seem to be maintained: latest commit dates from Oct. 2011.

Other interesting Ruby resources

The extensive resource list not to miss: Awesome NLP with Ruby actively curated by Andrei Beliankou (aka arbox).

Thanks to:

Professor Keshav Pingali, one of the creators of the Grammar Flow Graph parsing approach for his encouraging e-mail exchange.
Arjun Menon for his NLP example that uses engtagger gem.

Grammar Flow Graph

Since the Grammar Flow Graph parsing approach is quite new, it has not yet taken a place in standard parser textbooks. Here are a few references (and links) of papers on GFG:

K. Pingali, G. Bilardi. Parsing with Pictures
K. Pingali, G. Bilardi. A Graphical Model for Context-Free Grammar Parsing. In : International Conference on Compiler Construction. Springer Berlin Heidelberg, 2015. p. 3-27.
M. Fulbright. An Evaluation of Two Approaches to Parsing