Python DependencyGraph.get_by_address方法代码示例

# 需要导入模块: from nltk.parse.dependencygraph import DependencyGraph [as 别名]
# 或者: from nltk.parse.dependencygraph.DependencyGraph import get_by_address [as 别名]
def make_dep_tree(sent, deps):
    adj = merge_with(cons, [], *[{x:[m]} for x,m,_ in deps])
    heads = dict([(m,h) for h,m,_ in deps])
    rel = dict([(m,rel) for _,m,rel in deps])
    n = len(sent["x"])
    pos = sent["pos"]
    x = sent["x"]
    nodelist = defaultdict(lambda: {"address": -1, "head": -1, "deps": [], "rel": "", "tag": "", "word": None})
    
    for i in range(1, n):
        node = nodelist[i]
        node["address"] = i
        node["head"] = heads[i]
        node["deps"] = adj[i] if adj.has_key(i) else []
        node["tag"] = pos[i]
        node["word"] = x[i]
        node["rel"] = rel[i]
    
    g = DependencyGraph()
    g.get_by_address(0)["deps"] = adj[0] if adj.has_key(0) else []
    [g.add_node(node) for node in nodelist.values()]
    g.root = nodelist[adj[0][0]]
    
    return g

# 需要导入模块: from nltk.parse.dependencygraph import DependencyGraph [as 别名]
# 或者: from nltk.parse.dependencygraph.DependencyGraph import get_by_address [as 别名]
    def parse(self, tokens, tags):
        """
        Parses a list of tokens in accordance to the MST parsing algorithm
        for non-projective dependency parses.  Assumes that the tokens to
        be parsed have already been tagged and those tags are provided.  Various
        scoring methods can be used by implementing the ``DependencyScorerI``
        interface and passing it to the training algorithm.

        :type tokens: list(str)
        :param tokens: A list of words or punctuation to be parsed.
        :type tags: list(str)
        :param tags: A list of tags corresponding by index to the words in the tokens list.
        :return: An iterator of non-projective parses.
        :rtype: iter(DependencyGraph)
        """
        self.inner_nodes = {}

        # Initialize g_graph
        g_graph = DependencyGraph()
        for index, token in enumerate(tokens):
            g_graph.nodes[index + 1].update(
                {
                    'word': token,
                    'tag': tags[index],
                    'rel': 'NTOP',
                    'address': index + 1,
                }
            )
        #print (g_graph.nodes)


        # Fully connect non-root nodes in g_graph
        g_graph.connect_graph()
        original_graph = DependencyGraph()
        for index, token in enumerate(tokens):
            original_graph.nodes[index + 1].update(
                {
                    'word': token,
                    'tag': tags[index],
                    'rel': 'NTOP',
                    'address': index+1,
                }
            )

        b_graph = DependencyGraph()
        c_graph = DependencyGraph()

        for index, token in enumerate(tokens):
            c_graph.nodes[index + 1].update(
                {
                    'word': token,
                    'tag': tags[index],
                    'rel': 'NTOP',
                    'address': index + 1,
                }
            )

        # Assign initial scores to g_graph edges
        self.initialize_edge_scores(g_graph)
        logger.debug(self.scores)
        # Initialize a list of unvisited vertices (by node address)
        unvisited_vertices = [
            vertex['address'] for vertex in c_graph.nodes.values()
        ]
        # Iterate over unvisited vertices
        nr_vertices = len(tokens)
        betas = {}
        while unvisited_vertices:
            # Mark current node as visited
            current_vertex = unvisited_vertices.pop(0)
            logger.debug('current_vertex: %s', current_vertex)
            # Get corresponding node n_i to vertex v_i
            current_node = g_graph.get_by_address(current_vertex)
            logger.debug('current_node: %s', current_node)
            # Get best in-edge node b for current node
            best_in_edge = self.best_incoming_arc(current_vertex)
            betas[current_vertex] = self.original_best_arc(current_vertex)
            logger.debug('best in arc: %s --> %s', best_in_edge, current_vertex)
            # b_graph = Union(b_graph, b)
            for new_vertex in [current_vertex, best_in_edge]:
                b_graph.nodes[new_vertex].update(
                    {
                        'word': 'TEMP',
                        'rel': 'NTOP',
                        'address': new_vertex,
                    }
                )
            b_graph.add_arc(best_in_edge, current_vertex)
            # Beta(current node) = b  - stored for parse recovery
            # If b_graph contains a cycle, collapse it
            cycle_path = b_graph.contains_cycle()
            if cycle_path:
                # Create a new node v_n+1 with address = len(nodes) + 1
                new_node = {
                    'word': 'NONE',
                    'rel': 'NTOP',
                    'address': nr_vertices + 1,
                }
                # c_graph = Union(c_graph, v_n+1)
                c_graph.add_node(new_node)
#.........这里部分代码省略.........