class documentation

A bottom-up PCFG parser that uses dynamic programming to find the single most likely parse for a text. The ViterbiParser parser parses texts by filling in a "most likely constituent table". This table records the most probable tree representation for any given span and node value. In particular, it has an entry for every start index, end index, and node value, recording the most likely subtree that spans from the start index to the end index, and has the given node value.

The ViterbiParser parser fills in this table incrementally. It starts by filling in all entries for constituents that span one element of text (i.e., entries where the end index is one greater than the start index). After it has filled in all table entries for constituents that span one element of text, it fills in the entries for constitutants that span two elements of text. It continues filling in the entries for constituents spanning larger and larger portions of the text, until the entire table has been filled. Finally, it returns the table entry for a constituent spanning the entire text, whose node value is the grammar's start symbol.

In order to find the most likely constituent with a given span and node value, the ViterbiParser parser considers all productions that could produce that node value. For each production, it finds all children that collectively cover the span and have the node values specified by the production's right hand side. If the probability of the tree formed by applying the production to the children is greater than the probability of the current entry in the table, then the table is updated with this new tree.

A pseudo-code description of the algorithm used by ViterbiParser is:

Create an empty most likely constituent table, MLC.
For width in 1...len(text):
For start in 1...len(text)-width:
For prod in grammar.productions:
For each sequence of subtrees [t[1], t[2], ..., t[n]] in MLC,
where t[i].label()==prod.rhs[i],
and the sequence covers [start:start+width]:
old_p = MLC[start, start+width, prod.lhs]
new_p = P(t[1])P(t[1])...P(t[n])P(prod)
if new_p > old_p:
new_tree = Tree(prod.lhs, t[1], t[2], ..., t[n])
MLC[start, start+width, prod.lhs] = new_tree
Return MLC[0, len(text), start_symbol]
Method __init__ Create a new ViterbiParser parser, that uses grammar to parse texts.
Method __repr__ Undocumented
Method grammar No summary
Method parse When possible this list is sorted from most likely to least likely.
Method trace Set the level of tracing output that should be generated when parsing a text.
Method _add_constituents_spanning Find any constituents that might cover span, and add them to the most likely constituents table.
Method _find_instantiations No summary
Method _match_rhs No summary
Method _trace_lexical_insertion Undocumented
Method _trace_production Print trace output indicating that a given production has been applied at a given location.
Instance Variable _grammar The grammar used to parse sentences.
Instance Variable _trace The level of tracing output that should be generated when parsing a text.

Inherited from ParserI:

Method parse_all No summary
Method parse_one No summary
Method parse_sents Apply self.parse() to each element of sents. :rtype: iter(iter(Tree))
def __init__(self, grammar, trace=0): (source)

Create a new ViterbiParser parser, that uses grammar to parse texts.

Parameters
grammar:PCFGThe grammar used to parse texts.
trace:intThe level of tracing that should be used when parsing a text. 0 will generate no tracing output; and higher numbers will produce more verbose tracing output.
def __repr__(self): (source)

Undocumented

def grammar(self): (source)
Returns
The grammar used by this parser.
def parse(self, tokens): (source)

When possible this list is sorted from most likely to least likely.

Parameters
tokensUndocumented
sent:list(str)The sentence to be parsed
Returns
iter(Tree)An iterator that generates parse trees for the sentence.
def trace(self, trace=2): (source)

Set the level of tracing output that should be generated when parsing a text.

Parameters
trace:intThe trace level. A trace level of 0 will generate no tracing output; and higher trace levels will produce more verbose tracing output.
Returns
NoneUndocumented
def _add_constituents_spanning(self, span, constituents, tokens): (source)

Find any constituents that might cover span, and add them to the most likely constituents table.

Parameters
span:tuple(int, int)The section of the text for which we are trying to find possible constituents. The span is specified as a pair of integers, where the first integer is the index of the first token that should be included in the constituent; and the second integer is the index of the first token that should not be included in the constituent. I.e., the constituent should cover text[span[0]:span[1]], where text is the text that we are parsing.
constituents:dict(tuple(int,int,Nonterminal) -> ProbabilisticToken or ProbabilisticTree)The most likely constituents table. This table records the most probable tree representation for any given span and node value. In particular, constituents(s,e,nv) is the most likely ProbabilisticTree that covers text[s:e] and has a node value nv.symbol(), where text is the text that we are parsing. When _add_constituents_spanning is called, constituents should contain all possible constituents that are shorter than span.
tokens:list of tokensThe text we are parsing. This is only used for trace output.
Returns
NoneUndocumented
def _find_instantiations(self, span, constituents): (source)
Parameters
span:tuple(int, int)The section of the text for which we are trying to find production instantiations. The span is specified as a pair of integers, where the first integer is the index of the first token that should be covered by the production instantiation; and the second integer is the index of the first token that should not be covered by the production instantiation.
constituents:dict(tuple(int,int,Nonterminal) -> ProbabilisticToken or ProbabilisticTree)The most likely constituents table. This table records the most probable tree representation for any given span and node value. See the module documentation for more information.
Returns
a list of the production instantiations that cover a given span of the text. A "production instantiation" is a tuple containing a production and a list of children, where the production's right hand side matches the list of children; and the children cover span. :rtype: list of pair of Production, (list of (ProbabilisticTree or token.
def _match_rhs(self, rhs, span, constituents): (source)
Parameters
rhs:list(Nonterminal or any)The list specifying what kinds of children need to cover span. Each nonterminal in rhs specifies that the corresponding child should be a tree whose node value is that nonterminal's symbol. Each terminal in rhs specifies that the corresponding child should be a token whose type is that terminal.
span:tuple(int, int)The section of the text for which we are trying to find child lists. The span is specified as a pair of integers, where the first integer is the index of the first token that should be covered by the child list; and the second integer is the index of the first token that should not be covered by the child list.
constituents:dict(tuple(int,int,Nonterminal) -> ProbabilisticToken or ProbabilisticTree)The most likely constituents table. This table records the most probable tree representation for any given span and node value. See the module documentation for more information.
Returns
list(list(ProbabilisticTree or token)a set of all the lists of children that cover span and that match rhs.
def _trace_lexical_insertion(self, token, index, width): (source)

Undocumented

def _trace_production(self, production, p, span, width): (source)

Print trace output indicating that a given production has been applied at a given location.

Parameters
production:ProductionThe production that has been applied
p:floatThe probability of the tree produced by the production.
span:tupleThe span of the production
widthUndocumented
Returns
NoneUndocumented
_grammar: PCFG = (source)

The grammar used to parse sentences.

_trace: int = (source)

The level of tracing output that should be generated when parsing a text.