!date
!python --version

Thu Jun  6 02:11:57 AM UTC 2024
Python 3.10.12

22. 係り受けを用いた目的語抽出＋ネットワーク描画の例#

自然言語処理における代表的な情報抽出には、(1) 表面的な文字そのものを指定する方法、(2) 品詞を指定する方法、(3) 係り受け関係を指定する方法、(4) 分散表現による距離を参照する方法がある。

ここでは係り受け関係の例として「目的語 => 係り受け先」という関係を抽出してみよう。例えば「例題を取り入れて理解しやすくしてほしい。」という文においては「例題 => 取り入れ」がこの関係に相当する。この例では「例題」が目的語(UDではtoken.dep_ == obj)となり、「取り入れ」が係り受け先(UDではtoken.head)に相当する。

  • NOTE

    • ネットワーク描画のために networkx, pyviz を利用している。これらは pip install でインストール可能。

    • networkx はネットワーク描画や分析等に使われるライブラリ。本来ならこれだけで済むことが多いが、日本語には未対応のため pyviz (グラフ描画ライブラリ）も利用している。

    • pyvis注意点。

      • Google Colab ではやや取り扱いに難がある。具体的には pip install 後に一旦カーネルに再接続し直す必要がある。

      • Pythonスクリプトとして実行するか、ノートブック(ipynb)として実行するかによりコードがやや異なる。具体的にはコード内コメントを参照。

  • 参考

# spacy, ginzaインストール
!pip install -U ginza ja_ginza

# matplotlib で日本語フォントを使うための環境構築
!pip install japanize-matplotlib
!apt-get -y install fonts-ipafont-gothic # 日本語フォント

# ネットワーク分析
!pip install networkx
!pip install pyvis    # グラフ描画ライブラリ。networkxと組み合わせて日本語化対応用。

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22.1. 利用ライブラリの用意、データセット準備#

事前に、load_r_assesment.ipynb でデータセットを作成し、pkl形式でファイル保存(r_assesment.pkl)しておく。今回は作成済みファイルをダウンロードして利用することにする。

r_assesment.pklは授業評価アンケートの自由記述欄をpd.DataFrame形式で保存したもので、授業名(title)、学年(grade)、必修か否か(required)、質問番号(q_id)、コメント(comment)で構成される。

!curl -O https://ie.u-ryukyu.ac.jp/~tnal/2022/dm/static/r_assesment.pkl

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
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100 34834  100 34834    0     0  11591      0  0:00:03  0:00:03 --:--:-- 11595

import collections
import numpy as np
import pandas as pd
import spacy

import networkx as nx
import matplotlib.pyplot as plt
import japanize_matplotlib

nlp = spacy.load("ja_ginza")

assesment_df = pd.read_pickle('r_assesment.pkl')
assesment_df.head()
title grade required q_id comment
0 工業数学Ⅰ 1 True Q21 (1) 特になし
1 工業数学Ⅰ 1 True Q21 (2) 正直わかりずらい。むだに間があるし。
2 工業数学Ⅰ 1 True Q21 (2) 例題を取り入れて理解しやすくしてほしい。
3 工業数学Ⅰ 1 True Q21 (2) 特になし
4 工業数学Ⅰ 1 True Q21 (2) スライドに書く文字をもう少しわかりやすくして欲しいです。 
from spacy import displacy

sample = assesment_df['comment'][2]
print('sample = ', sample)

doc = nlp(sample)
displacy.render(doc, style="dep", options={"compact":True})

sample =  例題を取り入れて理解しやすくしてほしい。
例題 NOUN を ADP 取り入れ VERB て SCONJ 理解 NOUN し AUX やすく AUX し VERB て SCONJ ほしい。 AUX obj case advcl mark obl aux aux mark fixed 
from spacy.symbols import obj

for token in doc:
    if token.dep == obj:
        print(token.text, token.head.text)

例題 取り入れ

22.2. 目的語とその係り受け先を抽出してみよう#

{‘例題_取り入れ’:1], ,,,}のように obj と token.head をアンダースコアで結んだ文字列を作り、その文字列が出現した回数も同時にカウントしてみる。

from spacy.symbols import obj

result = {}
for text in assesment_df['comment']:
    doc = nlp(text)
    for token in doc:
        if token.dep == obj:
            target = token.lemma_ + '_' + token.head.lemma_
            if target not in result:
                result[target] = 1
            else:
                result[target] += 1

print(collections.Counter(result))

Counter({'課題_出す': 3, '講義_受ける': 3, 'こと_学ぶ': 2, '講義_通す': 2, '話_聞く': 2, '予習_する': 2, '解答_出す': 2, '単位_落とす': 2, '知識_獲得': 2, '仮説_立てる': 2, '手法_使う': 2, '例題_取り入れる': 1, '文字_する': 1, '行列_扱う': 1, '演習_出す': 1, '時間_おく': 1, '何_話す': 1, '道徳_学ぶ': 1, '想い_考える': 1, '品性_伸ばす': 1, 'こと_学べる': 1, 'こと_知れる': 1, '考え方_持てる': 1, '興味_持つ': 1, '心_学べる': 1, '側面_見れる': 1, 'これ_考える': 1, '計画_立てる': 1, '将来_想像': 1, 'アドバイス_もらえる': 1, 'マナー_学べる': 1, 'ある_知る': 1, 'ニーズ_聞ける': 1, '友達_作る': 1, '作成_通す': 1, '将来_見通す': 1, '機会_設ける': 1, '対面_増やす': 1, '課題_変更': 1, 'プログラミング_学べる': 1, 'それ_完成': 1, '復習_こなせる': 1, '基礎_学ぶ': 1, '課題_見る': 1, 'コード_良い': 1, 'プログラミング_触る': 1, '予想_上回る': 1, '復習_する': 1, '言語_学ぶ': 1, '課題_やる': 1, 'プログラミング_学ぶ': 1, 'こと_覚える': 1, '知識_生かす': 1, '演習_通す': 1, '基礎_定着': 1, 'デンチュウ_動かす': 1, 'denchu_通す': 1, '方_知る': 1, 'コード_書く': 1, 'denchu_動かす': 1, 'プログラミング_楽しい': 1, 'こと_実践': 1, '事_落とし込む': 1, 'メモ書き_消す': 1, 'こと_書く': 1, 'こと_聞く': 1, '環境_作る': 1, '点数_教える': 1, '提出_行う': 1, 'コマンド_打つ': 1, '方_招待': 1, '資料_理解': 1, '授業_理解': 1, 'テスト_延期': 1, '試験_続ける': 1, 'まとめ方_する': 1, '内容_補強': 1, '不便_感ずる': 1, 'メール_読む': 1, 'チャンネル_開設': 1, '内容_アウトプット': 1, 'ペン_使う': 1, '説明_聞く': 1, '何_言う': 1, '点数_公開': 1, '点_送る': 1, '採点_願う': 1, '試験_実施': 1, '試験_行なう': 1, 'ら_用いる': 1, '勉強_する': 1, 'ところ_調べる': 1, '採点_使う': 1, 'プログラム_作る': 1, '内容_網羅': 1, '提出期間_過ぎる': 1, '試験_する': 1, '解決策_考える': 1, '問題_解決': 1, '試験_受ける': 1, '環境_整える': 1, '回答_出す': 1, '理解_測る': 1, 'こと_する': 1, '試験_もつ': 1, '間違い_指摘': 1, '授業_受ける': 1, '説明_書く': 1, '難易度_する': 1, '過去問_配布': 1, '要点_押さえる': 1, '不満_書く': 1, '部分_ミス': 1, 'こと_望む': 1, '自学_増やす': 1, '質問_する': 1, '説明_する': 1, '動き_理解': 1, '欲_言う': 1, '緊張感_持てる': 1, '課題_再提出': 1, '間隔_空ける': 1, '時間_作る': 1, '知識_利用': 1, '知識_使う': 1, '研究_踏まえる': 1, '講義_する': 1, '録画_残す': 1, '大切_学ぶ': 1, '成績_決定': 1, '資料_写す': 1, '使う_学ぶ': 1, '配分_間違える': 1, '結果_出す': 1, 'こと_使う': 1, 'こと_実感': 1, 'アイディア_出す': 1, '物事_伝える': 1, 'スキル_向上': 1, '書_紹介': 1, '力_活かす': 1, '情報_得る': 1, '時間_とる': 1, '顔_合わせる': 1, '説明_行く': 1, '手_焼く': 1, '報告会_行う': 1, 'javascript_用いる': 1, 'アプリケーション_開発': 1, 'コーディング_教える': 1, 'フレームワーク_試す': 1})

22.3. ネットワーク描画してみる#

「目的語=>係り受け先単語」の関係を「単語間動詞を接続したエッジ」として可視化してみよう。まずはpd.DataFrame形式で「係り受け元単語、係り受け先単語、出現回数」として保存し直す。

columns = ['from', 'to', 'weight']
df = pd.DataFrame(columns=columns)

index = 0
nodes = []
for key, value in result.items():
    node1, node2 = key.split('_')
    if node1 not in nodes:
        nodes.append(node1)
    if node2 not in nodes:
        nodes.append(node2)
    df.loc[index] = [node1, node2, value]
    index += 1

df
from to weight
0 例題 取り入れる 1
1 文字 する 1
2 行列 扱う 1
3 演習 出す 1
4 時間 おく 1
... ... ... ...
146 報告会 行う 1
147 javascript 用いる 1
148 アプリケーション 開発 1
149 コーディング 教える 1
150 フレームワーク 試す 1

151 rows × 3 columns

22.4. networkx + pyvizで描画#

  • networkxはネットワークの描画・操作・解析などに使われるライブラリ。

  • pyvizはグラフ描画ライブラリ。

  • グラフ描画するだけなら networkx + matplotlib でも可能だが、日本語には未対応で文字化けしてしまうため、ここではpyvizを採用している。

import networkx as nx
from pyvis.network import Network

G = nx.from_pandas_edgelist(df, source='from', target='to', edge_attr='weight')

pyvis_G = Network(notebook=True, cdn_resources='in_line') # .pyで実行するならFalse
pyvis_G.from_nx(G)
pyvis_G.save_graph("mygraph.html")
#pyvis_G.show('mygraph.html')

22.5. グラフ参照する手順#

  • “mygraph.html” をダウンロード

  • ブラウザで新規タブを作成

  • mygraph.html をダブルクリック。もしくはブラウザ新規タブにドラッグ＆ドロップ。