言語処理100本ノック(2020): 43

```py
"""
43. 名詞を含む文節が動詞を含む文節に係るものを抽出
名詞を含む文節が，動詞を含む文節に係るとき，これらをタブ区切り形式で抽出せよ．ただし，句読点などの記号は出力しないようにせよ．
"""
from collections import defaultdict
from typing import List


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記号,空白,*,*,*,*,\u3000,\u3000,\u3000',
                             '* 1 2D 0/1 -0.764522',
                             '吾輩\t名詞,代名詞,一般,*,*,*,吾輩,ワガハイ,ワガハイ',
                             'は\t助詞,係助詞,*,*,*,*,は,ハ,ワ',
                             '* 2 -1D 0/2 0.000000',
                             '猫\t名詞,一般,*,*,*,*,猫,ネコ,ネコ',
                             'で\t助動詞,*,*,*,特殊・ダ,連用形,だ,デ,デ',
                             'ある\t助動詞,*,*,*,五段・ラ行アル,基本形,ある,アル,アル',
                             '。\t記号,句点,*,*,*,*,。,。,。']
    """
    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): 表層形（surface）
        base (str): 基本形（base）
        pos (str): 品詞（base）
        pos1 (str): 品詞細分類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 (文節番号).
        morphs List[Morph]: Morph (形態素) list.
        dst (str): The index of dependency target (係り先文節インデックス番号).
        srcs (List[str]): The index list of dependency source. (係り元文節インデックス番号).
    """

    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 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',
                                '吾輩\t名詞,代名詞,一般,*,*,*,吾輩,ワガハイ,ワガハイ',
                                'は\t助詞,係助詞,*,*,*,*,は,ハ,ワ',
                                '* 1 -1D 0/2 0.000000',
                                '猫\t名詞,一般,*,*,*,*,猫,ネコ,ネコ',
                                'で\t助動詞,*,*,*,特殊・ダ,連用形,だ,デ,デ',
                                'ある\t助動詞,*,*,*,五段・ラ行アル,基本形,ある,アル,アル',
                                '。\t記号,句点,*,*,*,*,。,。,。']

    Parsing format:
        e.g. "* 0 1D 0/1 0.000000"
        | カラム | 意味                                                         |
        | :----: | :----------------------------------------------------------- |
        |   1    | 先頭カラムは`*`。係り受け解析結果であることを示す。          |
        |   2    | 文節番号（0から始まる整数）                                  |
        |   3    | 係り先番号＋`D`                                              |
        |   4    | 主辞/機能語の位置と任意の個数の素性列                        |
        |   5    | 係り関係のスコア。係りやすさの度合で、一般に大きな値ほど係りやすい。 |

    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 concat_morphs_surface(chunk: Chunk) -> str:
    """Concatenate morph surfaces in a chink.

    Args:
        chunk (Chunk): e.g. Chunk( id: 0, dst: 5, srcs: [], morphs: [Morph(吾輩), Morph(は)]
    Return:
        e.g. '吾輩は'
    """
    res = ""
    for morph in chunk.morphs:
        if morph.pos != "記号":
            res += morph.surface
    return res


# ans43
def validate_pos_in_chunk(chunk: Chunk, pos: str) -> bool:
    """Return Ture if '名詞' or '動詞' in chunk's morphs. Otherwise, return False."""
    return any([morph.pos == pos for morph in chunk.morphs])


def concat_chunks_surface(chunks: List[Chunk]):
    """Concatenate surface of dependency source and target between chunks.

    Args:
        chunks (List[Chunk]): chunks represent a sentences.
            e.g. [Chunk( id: 0, dst: 5, srcs: [], morphs: [Morph(吾輩), Morph(は)] ),
                  Chunk( id: 1, dst: 2, srcs: [], morphs: [Morph(ここ), Morph(で)] ),
                  Chunk( id: 2, dst: 3, srcs: ['1'], morphs: [Morph(始め), Morph(て)] ),
                  Chunk( id: 3, dst: 4, srcs: ['2'], morphs: [Morph(人間), Morph(という)] ),
                  Chunk( id: 4, dst: 5, srcs: ['3'], morphs: [Morph(もの), Morph(を)] ),
                  Chunk( id: 5, dst: -1, srcs: ['0', '4'], morphs: [Morph(見), Morph(た), Morph(。)] )]
    """
    chunks_surface = []
    for chunk in chunks:
        if len(chunk.srcs) == 0:
            continue
        else:
            if validate_pos_in_chunk(chunk, "動詞"):
                current_chunk_surface = concat_morphs_surface(chunk)
                for src in chunk.srcs:
                    src_chunk = chunks[int(src)]
                    if validate_pos_in_chunk(src_chunk, "名詞"):
                        src_chunk_surface = concat_morphs_surface(src_chunk)
                        chunks_surface.append(
                            "{}  {}".format(src_chunk_surface, current_chunk_surface)
                        )
    return chunks_surface


fpath = "neko.txt.cabocha"
sentences = read_file(fpath)
chunks = [convert_sent_to_chunks(sent) for sent in sentences]  # ans41
result = [concat_chunks_surface(sent) for sent in chunks]  # ans43
result = list(filter(lambda x: len(x) != 0, result))  # filtering the empty list

for sent in result[:3]:
    print(sent)

# ['どこで  生れたか', '見当が  つかぬ']
# ['所で  泣いて', 'ニャーニャー  泣いて', 'いた事だけは  記憶している']
# ['ここで  始めて', '吾輩は  見た', 'ものを  見た']

```