使用 NLTK 或类似方法将名词分类为抽象或具体

问题描述

如何在 Python 中将名词列表分类为抽象的或具体的?

How can I categorize a list of nouns into abstract or concrete in Python?

例如:

"Have a seat in that chair."

在上面的句子中chair是名词，可以归类为具体的.

In above sentence chair is noun and can be categorized as concrete.

推荐答案

我建议使用预训练的词向量训练分类器.

I would suggest training a classifier using pretrained word vectors.

您需要两个库:spacy 用于标记文本和提取词向量，scikit-learn 用于机器学习:

You need two libraries: spacy for tokenizing text and extracting word vectors, and scikit-learn for machine learning:

import spacy
from sklearn.linear_model import LogisticRegression
import numpy as np
nlp = spacy.load("en_core_web_md")

区分具体名词和抽象名词是一项简单的任务，因此您可以用很少的例子训练一个模型:

Distinguishing concrete and abstract nouns is a simple task, so you can train a model with very few examples:

classes = ['concrete', 'abstract']
# todo: add more examples
train_set = [
    ['apple', 'owl', 'house'],
    ['agony', 'knowledge', 'process'],
]
X = np.stack([list(nlp(w))[0].vector for part in train_set for w in part])
y = [label for label, part in enumerate(train_set) for _ in part]
classifier = LogisticRegression(C=0.1, class_weight='balanced').fit(X, y)

当您拥有经过训练的模型后，您可以将其应用于任何文本:

When you have a trained model, you can apply it to any text:

for token in nlp("Have a seat in that chair with comfort and drink some juice to soothe your thirst."):
    if token.pos_ == 'NOUN':
        print(token, classes[classifier.predict([token.vector])[0]])

结果看起来令人满意:

# seat concrete
# chair concrete
# comfort abstract
# juice concrete
# thirst abstract

您可以通过将模型应用于不同的名词、发现错误并将它们添加到正确标签下的训练集中来改进模型.

You can improve the model by applying it to different nouns, spotting the errors and adding them to the training set under the correct label.

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