100 language processing knocks (2020): 44
"""
## 44.Visualization of dependent trees[Permalink](https://nlp100.github.io/ja/ch05.html#44-Visualizationofdependenttrees)
Visualize the dependency tree of a given sentence as a directed graph. For visualization,[Graphviz](http://www.graphviz.org/)Etc. should be used.
"""
from collections import defaultdict
from typing import List, Tuple
import pydot
def read_file(fpath: str) -> List[List[str]]:
"""Get clear format of parsed sentences.
Args:
fpath (str): File path.
Returns:
List[List[str]]: List of sentences, and each sentence contains a word list.
e.g. result[1]:
['* 0 2D 0/0 -0.764522',
'\u3000\t sign,Blank,*,*,*,*,\u3000,\u3000,\u3000',
'* 1 2D 0/1 -0.764522',
'I\t noun,Pronoun,General,*,*,*,I,Wagamama,Wagamama',
'Is\t particle,Particle,*,*,*,*,Is,C,Wow',
'* 2 -1D 0/2 0.000000',
'Cat\t noun,General,*,*,*,*,Cat,cat,cat',
'so\t auxiliary verb,*,*,*,Special,Continuous form,Is,De,De',
'is there\t auxiliary verb,*,*,*,Five steps, La line Al,Uninflected word,is there,Al,Al',
'。\t sign,Kuten,*,*,*,*,。,。,。']
"""
with open(fpath, mode="rt", encoding="utf-8") as f:
sentences = f.read().split("EOS\n")
return [sent.strip().split("\n") for sent in sentences if sent.strip() != ""]
class Morph:
"""Morph information for each token.
Args:
data (dict): A dictionary contains necessary information.
Attributes:
surface (str):Surface
base (str):Uninflected word (base)
pos (str):Part of speech (base)
pos1 (str):Part of speech subcategory 1 (pos1
"""
def __init__(self, data):
self.surface = data["surface"]
self.base = data["base"]
self.pos = data["pos"]
self.pos1 = data["pos1"]
def __repr__(self):
return f"Morph({self.surface})"
def __str__(self):
return "surface[{}]\tbase[{}]\tpos[{}]\tpos1[{}]".format(
self.surface, self.base, self.pos, self.pos1
)
class Chunk:
"""Containing information for Clause/phrase.
Args:
data (dict): A dictionary contains necessary information.
Attributes:
chunk_id (str): The number of clause chunk (Phrase number).
morphs List[Morph]: Morph (morpheme) list.
dst (str): The index of dependency target (Contact clause index number).
srcs (List[str]): The index list of dependency source. (Original clause index number).
"""
def __init__(self, chunk_id, dst):
self.id = chunk_id
self.morphs = []
self.dst = dst
self.srcs = []
def __repr__(self):
return "Chunk( id: {}, dst: {}, srcs: {}, morphs: {} )".format(
self.id, self.dst, self.srcs, self.morphs
)
def get_surface(self) -> str:
"""Concatenate morph surfaces in a chink.
Args:
chunk (Chunk): e.g. Chunk( id: 0, dst: 5, srcs: [], morphs: [Morph(I), Morph(Is)]
Return:
e.g. 'I am'
"""
morphs = self.morphs
res = ""
for morph in morphs:
if morph.pos != "symbol":
res += morph.surface
return res
def validate_pos(self, pos: str) -> bool:
"""Return Ture if 'noun' or 'verb' in chunk's morphs. Otherwise, return False."""
morphs = self.morphs
return any([morph.pos == pos for morph in morphs])
def convert_sent_to_chunks(sent: List[str]) -> List[Morph]:
"""Extract word and convert to morph.
Args:
sent (List[str]): A sentence contains a word list.
e.g. sent:
['* 0 1D 0/1 0.000000',
'I\t noun,Pronoun,General,*,*,*,I,Wagamama,Wagamama',
'Is\t particle,Particle,*,*,*,*,Is,C,Wow',
'* 1 -1D 0/2 0.000000',
'Cat\t noun,General,*,*,*,*,Cat,cat,cat',
'so\t auxiliary verb,*,*,*,Special,Continuous form,Is,De,De',
'is there\t auxiliary verb,*,*,*,Five steps, La line Al,Uninflected word,is there,Al,Al',
'。\t sign,Kuten,*,*,*,*,。,。,。']
Parsing format:
e.g. "* 0 1D 0/1 0.000000"
|column|meaning|
| :----: | :----------------------------------------------------------- |
| 1 |The first column is`*`.. Indicates that this is a dependency analysis result.|
| 2 |Phrase number (integer starting from 0)|
| 3 |Contact number +`D` |
| 4 |Head/Function word positions and any number of feature sequences|
| 5 |Engagement score. In general, the larger the value, the easier it is to engage.|
Returns:
List[Chunk]: List of chunks.
"""
chunks = []
chunk = None
srcs = defaultdict(list)
for i, word in enumerate(sent):
if word[0] == "*":
# Add chunk to chunks
if chunk is not None:
chunks.append(chunk)
# eNw Chunk beggin
chunk_id = word.split(" ")[1]
dst = word.split(" ")[2].rstrip("D")
chunk = Chunk(chunk_id, dst)
srcs[dst].append(chunk_id) # Add target->source to mapping list
else: # Add Morch to chunk.morphs
features = word.split(",")
dic = {
"surface": features[0].split("\t")[0],
"base": features[6],
"pos": features[0].split("\t")[1],
"pos1": features[1],
}
chunk.morphs.append(Morph(dic))
if i == len(sent) - 1: # Add the last chunk
chunks.append(chunk)
# Add srcs to each chunk
for chunk in chunks:
chunk.srcs = list(srcs[chunk.id])
return chunks
def get_edges(chunks: List[Chunk]) -> List[Tuple[str, str]]:
"""Get edges from sentence chunks.
Args:
chunks (List[Chunk]): A sentence contains many chunks.
e.g. [Chunk( id: 0, dst: 5, srcs: [], morphs: [Morph(I), Morph(Is)] ),
Chunk( id: 1, dst: 2, srcs: [], morphs: [Morph(here), Morph(so)] ),
Chunk( id: 2, dst: 3, srcs: ['1'], morphs: [Morph(start), Morph(hand)] ),
Chunk( id: 3, dst: 4, srcs: ['2'], morphs: [Morph(Human), Morph(That)] ),
Chunk( id: 4, dst: 5, srcs: ['3'], morphs: [Morph(thing), Morph(To)] ),
Chunk( id: 5, dst: -1, srcs: ['0', '4'], morphs: [Morph(You see), Morph(Ta), Morph(。)] )]
Returns:
List[Tuple[str, str]]: Edges.
e.g. [('here', 'Start with'),
('Start with', 'Human'),
('Human', 'Things'),
('I am', 'saw'),
('Things', 'saw')]
"""
edges = []
for chunk in chunks:
if len(chunk.srcs) == 0:
continue
post_node = chunk.get_surface()
for src in chunk.srcs:
src_chunk = chunks[int(src)]
pre_node = src_chunk.get_surface()
edges.append((pre_node, post_node))
return edges
def draw_graph(edges):
graph = pydot.Dot(graph_type="digraph")
for edge in edges:
graph.add_edge(pydot.Edge(edge[0], edge[1]))
graph.write_png("demo_graph.png ")
fpath = "neko.txt.cabocha"
sentences = read_file(fpath)
sentences = [convert_sent_to_chunks(sent) for sent in sentences] # ans41
# ans44
edges = get_edges(sentences[5]) # May return empty list
draw_graph(edges)
# edges:
# [('here', 'Start with'),
# ('Start with', 'Human'),
# ('Human', 'Things'),
# ('I am', 'saw'),
# ('Things', 'saw')]
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