言語処理100本ノック 2020「42. 係り元と係り先の文節の表示」
問題文
問題の概要
「41. 係り受け解析結果の読み込み(文節・係り受け)」を活用し、全ての係り受け関係を洗い出します。結合時は、品詞が記号の際には空文字列に置換しています。
class Morph: def __init__(self, dc): self.surface = dc['surface'] self.base = dc['base'] self.pos = dc['pos'] self.pos1 = dc['pos1'] class Chunk: def __init__(self, morphs, dst): self.morphs = morphs # 形態素(Morphオブジェクト)のリスト self.dst = dst # 係り先文節インデックス番号 self.srcs = [] # 係り元文節インデックス番号のリスト def parse_cabocha(block): def check_create_chunk(tmp): if len(tmp) > 0: c = Chunk(tmp, dst) res.append(c) tmp = [] return tmp res = [] tmp = [] dst = None for line in block.split('\n'): if line == '': tmp = check_create_chunk(tmp) elif line[0] == '*': dst = line.split(' ')[2].rstrip('D') tmp = check_create_chunk(tmp) else: (surface, attr) = line.split('\t') attr = attr.split(',') lineDict = { 'surface': surface, 'base': attr[6], 'pos': attr[0], 'pos1': attr[1] } tmp.append(Morph(lineDict)) for i, r in enumerate(res): res[int(r.dst)].srcs.append(i) return res filename = 'ch05/ai.ja.txt.cabocha' with open(filename, mode='rt', encoding='utf-8') as f: blocks = f.read().split('EOS\n') blocks = list(filter(lambda x: x != '', blocks)) blocks = [parse_cabocha(block) for block in blocks] for b in blocks: for m in b: if int(m.dst) > -1: print(''.join([mo.surface if mo.pos != '記号' else '' for mo in m.morphs]), ''.join([mo.surface if mo.pos != '記号' else '' for mo in b[int(m.dst)].morphs]), sep='\t')