nltk关键字抽取与轻量级搜索引擎(Whoosh, ElasticSearcher)

背景

有时候你想用一句完整的话或一个文本在基于关键字的搜索引擎里搜索,但是如果把整个文本放进去搜索的话,效果不是很好,因为你的搜索引擎是基于关键字而不是sematic search。那怎么抽取关键字呢?

利用NLTK抽取关键的代码

python 复制代码
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.probability import FreqDist

# Download NLTK resources
nltk.download('punkt')
nltk.download('stopwords')

def extract_keywords(text):
    # Tokenize the text
    words = word_tokenize(text)

    # Remove stopwords
    stop_words = set(stopwords.words('english'))
    filtered_words = [word.lower() for word in words if word.isalnum() and word.lower() not in stop_words]
    print('filtered words:', filtered_words)
    # Calculate word frequency
    freq_dist = FreqDist(filtered_words)

    # Extract keywords based on frequency or other criteria
    keywords = [word for word, freq in freq_dist.most_common(10)]  # Adjust the number of keywords as needed

    return keywords

if __name__ == '__main__':
    text = """
    Elasticsearch provides powerful search capabilities and is commonly used in production environments for large-scale document search and retrieval. However, it might be overkill for small projects or scenarios where simpler solutions like Whoosh are sufficient. Choose the solution that best fits your needs.
    """
    keywords = extract_keywords(text)
    print(keywords)

执行结果

python 复制代码
filtered words: ['elasticsearch', 'provides', 'powerful', 'search', 'capabilities', 'commonly', 'used', 'production', 'environments', 'document', 'search', 'retrieval', 'however', 'might', 'overkill', 'small', 'projects', 'scenarios', 'simpler', 'solutions', 'like', 'whoosh', 'sufficient', 'choose', 'solution', 'best', 'fits', 'needs']
['search', 'elasticsearch', 'provides', 'powerful', 'capabilities', 'commonly', 'used', 'production', 'environments', 'document']

基于关键的搜索-whoosh

python 复制代码
from keywords_extractor import *

from whoosh.fields import Schema, TEXT
from whoosh.index import create_in, open_dir
from whoosh.qparser import QueryParser

# Define the schema for the index
schema = Schema(question=TEXT(stored=True))

# Create or open the index
INDEX_DIR = "indexdir"
ix = create_in(INDEX_DIR, schema)  # Use create_in for creating a new index or open_dir for opening an existing one

# Index your documents (replace doc_content with the actual content of your documents)
writer = ix.writer()
doc_content = "what is angular"

questions = ["How to implement autocomplete, I don't know?", "How does Angular work?", "how Python programming language", "Example question", "Another question"]

for question in questions:
    writer.add_document(question=question)

writer.commit()

# Search using keywords
search_keywords = extract_keywords(doc_content)
query_str = " OR ".join(search_keywords)
print(query_str)

with ix.searcher() as searcher:
    query_parser = QueryParser("question", ix.schema)
    query = query_parser.parse(query_str)
    results = searcher.search(query)

    for result in results:
        print(result)

执行结果

python 复制代码
filtered words: ['angular']
angular
<Hit {'question': 'How does Angular work?'}>
python 复制代码
from elasticsearch import Elasticsearch

# Connect to the Elasticsearch server (make sure it's running)
es = Elasticsearch([{'host': 'localhost', 'port': 9200}])

# Create an index
index_name = "your_index_name"

if not es.indices.exists(index=index_name):
    es.indices.create(index=index_name, ignore=400)

# Index a document (replace doc_content with the actual content of your documents)
doc_content = "This is the content of your document."
document = {"content": doc_content}

es.index(index=index_name, body=document)

# Search using keywords
search_keywords = extract_keywords(doc_content)
query_body = {
    "query": {
        "terms": {
            "content": search_keywords
        }
    }
}

results = es.search(index=index_name, body=query_body)

for hit in results['hits']['hits']:
    print(hit['_source'])
相关推荐
_WndProc4 分钟前
【Python】Flask网页
开发语言·python·flask
互联网搬砖老肖6 分钟前
Python 中如何使用 Conda 管理版本和创建 Django 项目
python·django·conda
测试者家园17 分钟前
基于DeepSeek和crewAI构建测试用例脚本生成器
人工智能·python·测试用例·智能体·智能化测试·crewai
大模型真好玩22 分钟前
准确率飙升!Graph RAG如何利用知识图谱提升RAG答案质量(四)——微软GraphRAG代码实战
人工智能·python·mcp
前端付豪29 分钟前
11、打造自己的 CLI 工具:从命令行到桌面效率神器
后端·python
前端付豪29 分钟前
12、用类写出更可控、更易扩展的爬虫框架🕷
后端·python
江太翁40 分钟前
Pytorch torch
人工智能·pytorch·python
网安INF1 小时前
深度学习中的逻辑回归:从原理到Python实现
人工智能·python·深度学习·算法·逻辑回归
青苔猿猿1 小时前
(5)aconda之.condarc文件配置
python·镜像源·.condarc文件
ningdisheng1 小时前
Python用图片生成banner.txt文件
python