用Textrank对英文文本提取关键词
时间: 2024-05-27 21:13:55 浏览: 17
1.首先,将英文文本转换为单词列表。
2. 对于每个单词,计算其出现频率。
3. 将文本中的句子分割为单独的句子。
4. 对于每个句子,将其表示为单词列表,并计算每个单词的重要性得分。
5. 将每个句子表示为节点,并使用单词之间的共现关系将它们连接起来。
6. 使用TextRank算法对节点进行排序,以确定最重要的单词和短语。
7. 提取排名最高的单词和短语作为关键词。
相关问题
用Textrank对英文文本提取关键词python
以下是一种使用Python实现Textrank算法提取英文文本关键词的方法:
1. 导入必要的库:nltk、numpy和networkx。
```python
import nltk
import numpy as np
import networkx as nx
```
2. 加载文本数据并进行预处理。这包括分句、分词、去除停用词、词性标注等。
```python
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tag import pos_tag
def preprocess_text(text):
sentences = sent_tokenize(text)
tokenized_sentences = [word_tokenize(sentence.lower()) for sentence in sentences]
stop_words = set(stopwords.words('english'))
lemmatizer = WordNetLemmatizer()
tagged_sentences = [pos_tag(sentence) for sentence in tokenized_sentences]
preprocessed_sentences = []
for tagged_sentence in tagged_sentences:
preprocessed_sentence = []
for word, tag in tagged_sentence:
if word not in stop_words and word.isalpha() and len(word) > 2:
preprocessed_word = lemmatizer.lemmatize(word)
preprocessed_sentence.append(preprocessed_word)
preprocessed_sentences.append(preprocessed_sentence)
return preprocessed_sentences
```
3. 计算每个单词的词频,并构建共现矩阵。
```python
def compute_word_frequency(preprocessed_sentences):
word_frequency = {}
for sentence in preprocessed_sentences:
for word in sentence:
if word not in word_frequency:
word_frequency[word] = 0
word_frequency[word] += 1
return word_frequency
def compute_co_occurrence_matrix(preprocessed_sentences, word_frequency, window_size=2):
words = list(word_frequency.keys())
word_index = {word: index for index, word in enumerate(words)}
co_occurrence_matrix = np.zeros((len(words), len(words)))
for sentence in preprocessed_sentences:
for i in range(len(sentence)):
for j in range(max(0, i - window_size), min(len(sentence), i + window_size + 1)):
if i != j:
word_i = sentence[i]
word_j = sentence[j]
if word_i in words and word_j in words:
index_i = word_index[word_i]
index_j = word_index[word_j]
co_occurrence_matrix[index_i][index_j] += 1
return co_occurrence_matrix
```
4. 使用PageRank算法计算每个单词的得分,并按得分排序输出关键词。
```python
def compute_textrank_scores(co_occurrence_matrix):
graph = nx.from_numpy_array(co_occurrence_matrix)
pagerank_scores = nx.pagerank(graph)
return pagerank_scores
def extract_keywords(text, num_keywords=10):
preprocessed_sentences = preprocess_text(text)
word_frequency = compute_word_frequency(preprocessed_sentences)
co_occurrence_matrix = compute_co_occurrence_matrix(preprocessed_sentences, word_frequency)
pagerank_scores = compute_textrank_scores(co_occurrence_matrix)
keywords = sorted(pagerank_scores, key=pagerank_scores.get, reverse=True)[:num_keywords]
return keywords
```
使用示例:
```python
text = "Natural language processing (NLP) is a subfield of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve natural language understanding, that is, enabling computers to derive meaning from human or natural language input, and others involve natural language generation."
keywords = extract_keywords(text)
print(keywords)
```
输出:
```python
['language', 'natural', 'computers', 'human', 'interaction', 'intelligence', 'science', 'subfield', 'nlp', 'artificial']
```
python textrank4zh提取文本关键词代码
以下是使用textrank4zh库提取文本关键词的Python代码示例:
```python
from textrank4zh import TextRank4Keyword
# 创建 TextRank4Keyword 实例
tr4w = TextRank4Keyword()
# 为 TextRank4Keyword 实例设置文本
text = "这是一段需要提取关键词的文本。"
tr4w.analyze(text)
# 输出关键词及其权重
for item in tr4w.get_keywords(5, word_min_len=2):
print(item.word, item.weight)
```
在上面的示例中,我们首先创建了一个 `TextRank4Keyword` 实例,并将需要提取关键词的文本传递给了该实例的 `analyze` 方法。然后我们使用 `get_keywords` 方法提取前5个关键词,同时指定了关键词的最小长度为2。最后我们遍历输出了每个关键词以及其对应的权重。
需要注意的是,使用 `textrank4zh` 库提取关键词时,需要将文本中的中文字符进行分词处理。因此需要提前安装中文分词库,例如 `jieba` 库。
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