Sure, here are the steps and example Python code for each of the approaches:

1. Fuzzy search with embedding:

Step 1: Load pre-trained word embeddings
```python
import gensim.downloader as api
word_vectors = api.load("glove-wiki-gigaword-100")
```
Step 2: Define a function to preprocess text and generate embeddings
```python
def preprocess(text):
    # Implement your preprocessing logic here
    return word_vectors[text]
```
Step 3: Define a function to find the most similar question in the question set using fuzzy search
```python
from fuzzywuzzy import fuzz

def find_similar_question(user_question, question_set):
    similarity_scores = []
    for question in question_set:
        similarity_scores.append(fuzz.ratio(user_question, question))
    max_score = max(similarity_scores)
    return question_set[similarity_scores.index(max_score)]
```
Step 4: Define a function to classify a user's question into a topic based on the topic of the most similar question in the question set and the embedding-based similarity score
```python
def classify_by_fuzzy_with_embedding(user_question, question_set, topic_dict):
    similar_question = find_similar_question(user_question, question_set)
    X_train = [preprocess(text) for text in question_set]
    X_test = preprocess(user_question)
    similarity_scores = []
    for x_train in X_train:
        similarity_scores.append(word_vectors.similarity(x_train, X_test))
    max_score = max(similarity_scores)
    if max_score >= 0.7:
        topic = topic_dict[similar_question]
    else:
        topic = "Other"
    return topic
```
2. Semantic search with embedding:

Step 1: Load pre-trained word embeddings and define a function to preprocess text and generate embeddings as in the previous example.

Step 2: Train a topic modeling algorithm on the question set using the preprocessed embeddings
```python
from sklearn.decomposition import NMF
from sklearn.feature_extraction.text import TfidfVectorizer

vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(question_set)
nmf = NMF(n_components=3, random_state=1)
nmf.fit(X.dot(word_vectors.vectors))
```
Step 3: Define a function to classify a user's question into a topic based on the topic modeling results and the embedding-based similarity score
```python
def classify_by_semantic_with_embedding(user_question, question_set, topic_dict, nmf_model):
    X_test = vectorizer.transform([user_question])
    topic_scores = nmf_model.transform(X_test.dot(word_vectors.vectors))
    topic = topic_scores.argmax()
    similar_question = find_similar_question(user_question, question_set)
    X_train = [preprocess(text) for text in question_set]
    X_test = preprocess(user_question)
    similarity_scores = []
    for x_train in X_train:
        similarity_scores.append(word_vectors.similarity(x_train, X_test))
    max_score = max(similarity_scores)
    if max_score >= 0.7:
        topic = topic_dict[similar_question]
    else:
        topic = "Other"
    return topic
```
3. Hybrid approach:

Step 1: Load pre-trained word embeddings and define a function to preprocess text and generate embeddings as in the previous examples.

Step 2: Define a function to combine fuzzy search and embedding-based similarity to find the most similar question in the question set
```python
def find_similar_question_hybrid(user_question, question_set):
    similarity_scores = []
    for question in question_set:
        X_train = preprocess(question)
        X_test = preprocess(user_question)
        similarity_scores
```
각 접근 방식에 대한 단계와 예제 Python 코드는 다음과 같습니다:1. 임베딩을 사용한 퍼지 검색:1단계: 사전 학습된 단어 임베딩 불러오기```pythongensim.downloader를 api로 가져옵니다.word_vectors = api.load("glove-wiki-gigaword-100")```2단계: 텍스트를 전처리하고 임베딩을 생성하는 함수를 정의합니다.```pythondef preprocess(text):    # 여기에 전처리 로직을 구현하세요.    return word_vectors[text]```3단계: 퍼지 검색을 사용하여 문제 집합에서 가장 유사한 문제를 찾는 함수를 정의합니다.```pythonfrom fuzzywuzzy import fuzzdef find_similar_question(user_question, question_set):    유사도_점수 = []    질문_셋의 질문에 대해:        similarity_scores.append(fuzz.ratio(user_question, question))    max_score = max(유사도_점수)    return question_set[similarity_scores.index(max_score)]```4단계: 질문 세트에서 가장 유사한 질문의 주제와 임베딩 기반 유사도 점수를 기반으로 사용자의 질문을 토픽으로 분류하는 함수를 정의합니다.```pythondef classify_by_fuzzy_with_embedding(user_question, question_set, topic_dict):    similar_question = find_similar_question(user_question, question_set)    X_train = [question_set의 텍스트에 대한 사전 처리(text)]    X_test = 전처리(user_question)    유사도_점수 = []    for x_train in X_train:        similarity_scores.append(word_vectors.similarity(x_train, X_test))    max_score = max(유사도_점수)    max_score >= 0.7:        topic = topic_dict[similar_question]    else:        topic = "기타"    반환 주제```2. 임베딩을 사용한 시맨틱 검색:1단계: 사전 학습된 단어 임베딩을 로드하고 이전 예제에서와 같이 텍스트를 전처리하고 임베딩을 생성하는 함수를 정의합니다.2단계: 사전 처리된 임베딩을 사용하여 질문 세트에 대해 토픽 모델링 알고리즘을 훈련합니다.'``pythonsklearn.decomposition에서 NMF를 가져옵니다.sklearn.feature_extraction.text에서 TfidfVectorizer를 가져옵니다.벡터라이저 = TfidfVectorizer(stop_words='english')X = 벡터라이저.fit_transform(question_set)nmf = NMF(n_components=3, random_state=1)nmf.fit(X.dot(word_vectors.vectors))```3단계: 토픽 모델링 결과와 임베딩 기반 유사도 점수를 기반으로 사용자의 질문을 토픽으로 분류하는 함수를 정의합니다.```pythondef classify_by_semantic_with_embedding(user_question, question_set, topic_dict, nmf_model):    X_test = 벡터라이저.transform([user_question])    topic_scores = nmf_model.transform(X_test.dot(word_vectors.vectors))    topic = topic_scores.argmax()    similar_question = find_similar_question(user_question, question_set)    X_train = [question_set의 텍스트에 대한 사전 처리(텍스트)]    X_test = 전처리(user_question)    유사도_점수 = []    for x_train in X_train:        similarity_scores.append(word_vectors.similarity(x_train, X_test))    max_score = max(유사도_점수)    max_score >= 0.7:        topic = topic_dict[similar_question]    else:        topic = "기타"    반환 주제```3. 하이브리드 접근 방식:1단계: 사전 학습된 단어 임베딩을 로드하고 이전 예제에서와 같이 텍스트를 전처리하고 임베딩을 생성하는 함수를 정의합니다.2단계: 퍼지 검색과 임베딩 기반 유사도를 결합하여 문제 집합에서 가장 유사한 문제를 찾는 함수를 정의합니다.```pythondef find_similar_question_hybrid(user_question, question_set):    유사도_점수 = []    질문_셋의 질문에 대해:        X_train = 사전 처리(question)        X_test = 사전 처리(user_question)        유사도_점수```