📚 自然语言处理基础

1. 自然语言处理概述

自然语言处理（Natural Language Processing, NLP）是人工智能的一个重要分支，旨在让计算机能够理解、处理和生成人类语言。在Text2SQL任务中，NLP技术用于理解用户的自然语言查询意图，并将其转换为结构化的SQL语句。

1.1 NLP的发展历程

规则基础时期（1950s-1980s）

• 特点：基于手工编写的语法规则和词典
• 代表：ELIZA、SHRDLU等早期系统
• 局限：规则复杂，难以处理语言的多样性

统计方法时期（1990s-2000s）

• 特点：基于统计模型和机器学习
• 代表：隐马尔可夫模型、条件随机场
• 优势：能够从数据中学习，处理不确定性

深度学习时期（2010s-至今）

• 特点：基于神经网络的端到端学习
• 代表：RNN、LSTM、Transformer、BERT、GPT
• 突破：大幅提升了NLP任务的性能

1.2 NLP的核心任务

• 词法分析：分词、词性标注、命名实体识别
• 句法分析：语法解析、依存分析
• 语义分析：语义角色标注、语义解析
• 语用分析：指代消解、情感分析
• 生成任务：机器翻译、文本摘要、对话生成

2. 文本预处理

2.1 分词（Tokenization）

分词是将连续的文本分割成有意义的词汇单元的过程。

英文分词

import nltk
from nltk.tokenize import word_tokenize, sent_tokenize

# 句子分割
text = "What is the average salary? Show me all employees."
sentences = sent_tokenize(text)
print(sentences)
# ['What is the average salary?', 'Show me all employees.']

# 词汇分割
words = word_tokenize(sentences[0])
print(words)
# ['What', 'is', 'the', 'average', 'salary', '?']

中文分词

import jieba

# 中文分词
text = "查询所有员工的平均工资"
words = jieba.lcut(text)
print(words)
# ['查询', '所有', '员工', '的', '平均', '工资']

子词分词（Subword Tokenization）

from transformers import AutoTokenizer

# 使用BERT分词器
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
text = "What is the average salary?"
tokens = tokenizer.tokenize(text)
print(tokens)
# ['what', 'is', 'the', 'average', 'salary', '?']

# 转换为ID
token_ids = tokenizer.encode(text)
print(token_ids)

2.2 文本清洗

import re
import string

def clean_text(text):
    """文本清洗函数"""
    # 转换为小写
    text = text.lower()
    
    # 移除标点符号
    text = text.translate(str.maketrans('', '', string.punctuation))
    
    # 移除多余空格
    text = re.sub(r'\s+', ' ', text).strip()
    
    # 移除数字（可选）
    # text = re.sub(r'\d+', '', text)
    
    return text

# 示例
original = "What's the AVERAGE salary???"
cleaned = clean_text(original)
print(f"原文: {original}")
print(f"清洗后: {cleaned}")

2.3 停用词处理

from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize

# 获取英文停用词
stop_words = set(stopwords.words('english'))

def remove_stopwords(text):
    """移除停用词"""
    words = word_tokenize(text.lower())
    filtered_words = [word for word in words if word not in stop_words]
    return ' '.join(filtered_words)

# 示例
text = "What is the average salary of all employees?"
filtered = remove_stopwords(text)
print(f"原文: {text}")
print(f"移除停用词后: {filtered}")
# 原文: What is the average salary of all employees?
# 移除停用词后: average salary employees ?

2.4 词干提取和词形还原

词干提取（Stemming）

from nltk.stem import PorterStemmer

stemmer = PorterStemmer()

words = ['running', 'runs', 'ran', 'runner']
stemmed = [stemmer.stem(word) for word in words]
print(f"原词: {words}")
print(f"词干: {stemmed}")
# 原词: ['running', 'runs', 'ran', 'runner']
# 词干: ['run', 'run', 'ran', 'runner']

词形还原（Lemmatization）

from nltk.stem import WordNetLemmatizer
from nltk.corpus import wordnet

lemmatizer = WordNetLemmatizer()

def get_wordnet_pos(word):
    """获取词性"""
    tag = nltk.pos_tag([word])[0][1][0].upper()
    tag_dict = {"J": wordnet.ADJ,
                "N": wordnet.NOUN,
                "V": wordnet.VERB,
                "R": wordnet.ADV}
    return tag_dict.get(tag, wordnet.NOUN)

words = ['running', 'runs', 'ran', 'better', 'good']
lemmatized = [lemmatizer.lemmatize(word, get_wordnet_pos(word)) for word in words]
print(f"原词: {words}")
print(f"词形还原: {lemmatized}")

3. 词汇表示

3.1 独热编码（One-Hot Encoding）

import numpy as np
from sklearn.preprocessing import LabelEncoder

# 构建词汇表
vocab = ['what', 'is', 'the', 'average', 'salary']
vocab_size = len(vocab)

# 创建词汇到索引的映射
word_to_idx = {word: idx for idx, word in enumerate(vocab)}
idx_to_word = {idx: word for word, idx in word_to_idx.items()}

def one_hot_encode(word, vocab_size, word_to_idx):
    """独热编码"""
    vector = np.zeros(vocab_size)
    if word in word_to_idx:
        vector[word_to_idx[word]] = 1
    return vector

# 示例
word = 'average'
vector = one_hot_encode(word, vocab_size, word_to_idx)
print(f"词汇: {word}")
print(f"独热编码: {vector}")

3.2 词袋模型（Bag of Words）

from sklearn.feature_extraction.text import CountVectorizer

# 文档集合
documents = [
    "What is the average salary",
    "Show me all employees",
    "What is the maximum salary"
]

# 创建词袋模型
vectorizer = CountVectorizer()
bow_matrix = vectorizer.fit_transform(documents)

# 获取特征名称
feature_names = vectorizer.get_feature_names_out()

print("词汇表:", feature_names)
print("词袋矩阵:")
print(bow_matrix.toarray())

3.3 TF-IDF

from sklearn.feature_extraction.text import TfidfVectorizer

# 创建TF-IDF向量化器
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform(documents)

print("TF-IDF矩阵:")
print(tfidf_matrix.toarray())

# 查看特定文档的TF-IDF值
feature_names = tfidf_vectorizer.get_feature_names_out()
for i, doc in enumerate(documents):
    print(f"\n文档 {i+1}: {doc}")
    tfidf_scores = tfidf_matrix[i].toarray()[0]
    for j, score in enumerate(tfidf_scores):
        if score > 0:
            print(f"  {feature_names[j]}: {score:.4f}")

3.4 词向量（Word Embeddings）

Word2Vec

from gensim.models import Word2Vec
from nltk.tokenize import word_tokenize

# 准备训练数据
sentences = [
    "what is the average salary",
    "show me all employees",
    "what is the maximum salary",
    "list all departments",
    "count employees in each department"
]

# 分词
tokenized_sentences = [word_tokenize(sent.lower()) for sent in sentences]

# 训练Word2Vec模型
model = Word2Vec(sentences=tokenized_sentences, 
                 vector_size=100,  # 向量维度
                 window=5,         # 上下文窗口大小
                 min_count=1,      # 最小词频
                 workers=4)        # 线程数

# 获取词向量
word = 'salary'
if word in model.wv:
    vector = model.wv[word]
    print(f"'{word}'的词向量维度: {vector.shape}")
    print(f"向量前5个元素: {vector[:5]}")

# 查找相似词
similar_words = model.wv.most_similar('salary', topn=3)
print(f"与'salary'最相似的词: {similar_words}")

预训练词向量

import gensim.downloader as api

# 下载预训练的词向量（需要网络连接）
# word_vectors = api.load("glove-wiki-gigaword-100")

# 使用预训练词向量
# vector = word_vectors['salary']
# similar_words = word_vectors.most_similar('salary')

4. 语言模型

4.1 N-gram语言模型

from collections import defaultdict, Counter
import re

class NGramLanguageModel:
    def __init__(self, n=2):
        self.n = n
        self.ngrams = defaultdict(Counter)
        self.vocab = set()
    
    def train(self, texts):
        """训练N-gram模型"""
        for text in texts:
            # 添加句子开始和结束标记
            tokens = ['<s>'] * (self.n - 1) + text.lower().split() + ['</s>']
            self.vocab.update(tokens)
            
            # 生成N-gram
            for i in range(len(tokens) - self.n + 1):
                context = tuple(tokens[i:i+self.n-1])
                next_word = tokens[i+self.n-1]
                self.ngrams[context][next_word] += 1
    
    def probability(self, context, word):
        """计算条件概率"""
        context = tuple(context)
        if context not in self.ngrams:
            return 0.0
        
        total_count = sum(self.ngrams[context].values())
        word_count = self.ngrams[context][word]
        
        return word_count / total_count if total_count > 0 else 0.0
    
    def generate_next_word(self, context):
        """生成下一个词"""
        context = tuple(context)
        if context not in self.ngrams:
            return None
        
        # 选择概率最高的词
        return self.ngrams[context].most_common(1)[0][0]

# 训练示例
texts = [
    "what is the average salary",
    "show me the maximum salary",
    "what is the minimum salary",
    "display all employee names"
]

model = NGramLanguageModel(n=2)  # 二元语法
model.train(texts)

# 测试
context = ['what']
next_word = model.generate_next_word(context)
print(f"给定上下文 {context}，下一个词可能是: {next_word}")

# 计算概率
prob = model.probability(['what'], 'is')
print(f"P(is|what) = {prob:.4f}")

4.2 神经语言模型

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader

class SimpleRNNLanguageModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(SimpleRNNLanguageModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.rnn = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
        self.linear = nn.Linear(hidden_dim, vocab_size)
        self.dropout = nn.Dropout(0.2)
    
    def forward(self, x, hidden=None):
        embedded = self.embedding(x)
        output, hidden = self.rnn(embedded, hidden)
        output = self.dropout(output)
        output = self.linear(output)
        return output, hidden

# 模型参数
vocab_size = 1000
embedding_dim = 128
hidden_dim = 256

# 创建模型
model = SimpleRNNLanguageModel(vocab_size, embedding_dim, hidden_dim)
print(f"模型参数数量: {sum(p.numel() for p in model.parameters())}")

5. 序列标注

5.1 命名实体识别（NER）

import spacy

# 加载spaCy模型
nlp = spacy.load("en_core_web_sm")

def extract_entities(text):
    """提取命名实体"""
    doc = nlp(text)
    entities = []
    for ent in doc.ents:
        entities.append({
            'text': ent.text,
            'label': ent.label_,
            'start': ent.start_char,
            'end': ent.end_char
        })
    return entities

# 示例
text = "Show me employees from New York with salary greater than 50000"
entities = extract_entities(text)
print("命名实体:")
for entity in entities:
    print(f"  {entity['text']} -> {entity['label']}")

5.2 词性标注（POS Tagging）

import nltk
from nltk.tokenize import word_tokenize

def pos_tagging(text):
    """词性标注"""
    tokens = word_tokenize(text)
    pos_tags = nltk.pos_tag(tokens)
    return pos_tags

# 示例
text = "What is the average salary of employees?"
pos_tags = pos_tagging(text)
print("词性标注:")
for word, pos in pos_tags:
    print(f"  {word} -> {pos}")

5.3 依存句法分析

import spacy

nlp = spacy.load("en_core_web_sm")

def dependency_parsing(text):
    """依存句法分析"""
    doc = nlp(text)
    dependencies = []
    for token in doc:
        dependencies.append({
            'text': token.text,
            'pos': token.pos_,
            'dep': token.dep_,
            'head': token.head.text,
            'children': [child.text for child in token.children]
        })
    return dependencies

# 示例
text = "Show me the average salary"
deps = dependency_parsing(text)
print("依存关系:")
for dep in deps:
    print(f"  {dep['text']} ({dep['pos']}) --{dep['dep']}--> {dep['head']}")

6. 语义表示

6.1 语义角色标注（SRL）

# 使用AllenNLP进行语义角色标注
# pip install allennlp allennlp-models

from allennlp.predictors.predictor import Predictor

# 加载预训练的SRL模型
# predictor = Predictor.from_path(
#     "https://storage.googleapis.com/allennlp-public-models/bert-base-srl-2020.03.24.tar.gz"
# )

def semantic_role_labeling(text):
    """语义角色标注"""
    # result = predictor.predict(sentence=text)
    # return result
    pass

# 示例（需要网络连接下载模型）
# text = "John gave Mary a book"
# srl_result = semantic_role_labeling(text)

6.2 语义解析

语义解析是将自然语言转换为机器可理解的语义表示的过程，这正是Text2SQL的核心任务。

class SemanticParser:
    """简单的语义解析器示例"""
    
    def __init__(self):
        # 定义语义模板
        self.templates = {
            'select_all': {
                'pattern': r'(show|display|list).*all.*(\w+)',
                'sql_template': 'SELECT * FROM {table}'
            },
            'count': {
                'pattern': r'(count|number of).*(\w+)',
                'sql_template': 'SELECT COUNT(*) FROM {table}'
            },
            'average': {
                'pattern': r'average.*(\w+)',
                'sql_template': 'SELECT AVG({column}) FROM {table}'
            }
        }
    
    def parse(self, text):
        """解析自然语言查询"""
        import re
        
        text = text.lower()
        
        for intent, template in self.templates.items():
            match = re.search(template['pattern'], text)
            if match:
                return {
                    'intent': intent,
                    'sql_template': template['sql_template'],
                    'entities': match.groups()
                }
        
        return None

# 示例
parser = SemanticParser()
queries = [
    "Show me all employees",
    "Count the number of departments",
    "What is the average salary"
]

for query in queries:
    result = parser.parse(query)
    print(f"查询: {query}")
    print(f"解析结果: {result}")
    print()

7. 预训练语言模型

7.1 BERT

from transformers import BertTokenizer, BertModel
import torch

# 加载预训练的BERT模型
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased')

def get_bert_embeddings(text):
    """获取BERT词向量"""
    # 分词和编码
    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True)
    
    # 获取模型输出
    with torch.no_grad():
        outputs = model(**inputs)
    
    # 获取最后一层的隐藏状态
    last_hidden_states = outputs.last_hidden_state
    
    return last_hidden_states

# 示例
text = "What is the average salary?"
embeddings = get_bert_embeddings(text)
print(f"BERT嵌入维度: {embeddings.shape}")
print(f"序列长度: {embeddings.shape[1]}")
print(f"隐藏维度: {embeddings.shape[2]}")

7.2 GPT

from transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch

# 加载GPT-2模型
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')

# 设置pad_token
tokenizer.pad_token = tokenizer.eos_token

def generate_text(prompt, max_length=50):
    """使用GPT-2生成文本"""
    inputs = tokenizer.encode(prompt, return_tensors='pt')
    
    with torch.no_grad():
        outputs = model.generate(
            inputs, 
            max_length=max_length, 
            num_return_sequences=1,
            temperature=0.7,
            pad_token_id=tokenizer.eos_token_id
        )
    
    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return generated_text

# 示例
prompt = "The SQL query to find all employees is"
generated = generate_text(prompt)
print(f"提示: {prompt}")
print(f"生成: {generated}")

8. Text2SQL中的NLP应用

8.1 查询意图识别

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline

class QueryIntentClassifier:
    """查询意图分类器"""
    
    def __init__(self):
        self.pipeline = Pipeline([
            ('tfidf', TfidfVectorizer(ngram_range=(1, 2))),
            ('classifier', MultinomialNB())
        ])
        
        # 定义意图类别
        self.intents = {
            'select': 'SELECT查询',
            'count': '计数查询',
            'aggregate': '聚合查询',
            'filter': '过滤查询',
            'sort': '排序查询'
        }
    
    def train(self, texts, labels):
        """训练分类器"""
        self.pipeline.fit(texts, labels)
    
    def predict(self, text):
        """预测查询意图"""
        intent = self.pipeline.predict([text])[0]
        confidence = max(self.pipeline.predict_proba([text])[0])
        return intent, confidence

# 训练数据示例
train_texts = [
    "Show me all employees",
    "List all departments",
    "How many employees are there",
    "Count the number of departments",
    "What is the average salary",
    "Find the maximum salary",
    "Show employees with salary > 50000",
    "Find employees in IT department",
    "Sort employees by salary",
    "Order departments by name"
]

train_labels = [
    'select', 'select',
    'count', 'count',
    'aggregate', 'aggregate',
    'filter', 'filter',
    'sort', 'sort'
]

# 训练和测试
classifier = QueryIntentClassifier()
classifier.train(train_texts, train_labels)

# 测试
test_queries = [
    "Display all products",
    "How many orders were placed",
    "What is the total revenue"
]

for query in test_queries:
    intent, confidence = classifier.predict(query)
    print(f"查询: {query}")
    print(f"意图: {intent} (置信度: {confidence:.3f})")
    print()

8.2 实体抽取

import re
from typing import List, Dict

class SQLEntityExtractor:
    """SQL实体抽取器"""
    
    def __init__(self):
        # 定义实体模式
        self.patterns = {
            'table_names': [
                r'\b(employees?|departments?|products?|orders?|customers?)\b',
                r'\b(users?|accounts?|transactions?)\b'
            ],
            'column_names': [
                r'\b(name|salary|age|id|email|phone)\b',
                r'\b(price|quantity|date|status)\b'
            ],
            'operators': [
                r'\b(greater than|more than|>)\b',
                r'\b(less than|<)\b',
                r'\b(equal to|equals?|=)\b'
            ],
            'aggregations': [
                r'\b(average|avg|mean)\b',
                r'\b(sum|total)\b',
                r'\b(count|number)\b',
                r'\b(max|maximum)\b',
                r'\b(min|minimum)\b'
            ],
            'values': [
                r'\b\d+\b',  # 数字
                r'\b\d+\.\d+\b',  # 小数
                r"'[^']*'",  # 字符串
                r'"[^"]*"'   # 字符串
            ]
        }
    
    def extract_entities(self, text: str) -> Dict[str, List[str]]:
        """抽取实体"""
        text = text.lower()
        entities = {}
        
        for entity_type, patterns in self.patterns.items():
            entities[entity_type] = []
            for pattern in patterns:
                matches = re.findall(pattern, text)
                entities[entity_type].extend(matches)
        
        # 去重
        for entity_type in entities:
            entities[entity_type] = list(set(entities[entity_type]))
        
        return entities

# 示例
extractor = SQLEntityExtractor()
queries = [
    "Show me all employees with salary greater than 50000",
    "What is the average age of customers",
    "Count the number of orders in 2023"
]

for query in queries:
    entities = extractor.extract_entities(query)
    print(f"查询: {query}")
    print("抽取的实体:")
    for entity_type, values in entities.items():
        if values:
            print(f"  {entity_type}: {values}")
    print()

9. 评估指标

9.1 文本相似度

from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np

def calculate_text_similarity(text1, text2, method='cosine'):
    """计算文本相似度"""
    if method == 'cosine':
        vectorizer = TfidfVectorizer()
        tfidf_matrix = vectorizer.fit_transform([text1, text2])
        similarity = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])[0][0]
        return similarity
    
    elif method == 'jaccard':
        set1 = set(text1.lower().split())
        set2 = set(text2.lower().split())
        intersection = len(set1.intersection(set2))
        union = len(set1.union(set2))
        return intersection / union if union > 0 else 0

# 示例
text1 = "Show me all employees"
text2 = "Display all workers"

cosine_sim = calculate_text_similarity(text1, text2, 'cosine')
jaccard_sim = calculate_text_similarity(text1, text2, 'jaccard')

print(f"文本1: {text1}")
print(f"文本2: {text2}")
print(f"余弦相似度: {cosine_sim:.4f}")
print(f"Jaccard相似度: {jaccard_sim:.4f}")

9.2 BLEU分数

from nltk.translate.bleu_score import sentence_bleu, corpus_bleu
from nltk.tokenize import word_tokenize

def calculate_bleu(reference, candidate):
    """计算BLEU分数"""
    # 分词
    ref_tokens = word_tokenize(reference.lower())
    cand_tokens = word_tokenize(candidate.lower())
    
    # 计算BLEU分数
    bleu_score = sentence_bleu([ref_tokens], cand_tokens)
    return bleu_score

# 示例
reference = "SELECT * FROM employees WHERE salary > 50000"
candidate = "SELECT * FROM employees WHERE salary > 50000"

bleu = calculate_bleu(reference, candidate)
print(f"参考: {reference}")
print(f"候选: {candidate}")
print(f"BLEU分数: {bleu:.4f}")

10. 实践项目

10.1 简单的Text2SQL预处理器

class Text2SQLPreprocessor:
    """Text2SQL预处理器"""
    
    def __init__(self):
        self.entity_extractor = SQLEntityExtractor()
        self.intent_classifier = QueryIntentClassifier()
        
        # 数据库schema信息
        self.schema = {
            'tables': ['employees', 'departments', 'projects'],
            'columns': {
                'employees': ['id', 'name', 'salary', 'department_id'],
                'departments': ['id', 'name', 'location'],
                'projects': ['id', 'name', 'budget', 'department_id']
            }
        }
    
    def preprocess(self, query):
        """预处理查询"""
        # 1. 文本清洗
        cleaned_query = self.clean_text(query)
        
        # 2. 实体抽取
        entities = self.entity_extractor.extract_entities(cleaned_query)
        
        # 3. Schema链接
        linked_entities = self.link_to_schema(entities)
        
        # 4. 构建结构化表示
        structured_query = {
            'original_query': query,
            'cleaned_query': cleaned_query,
            'entities': linked_entities,
            'schema_info': self.get_relevant_schema(linked_entities)
        }
        
        return structured_query
    
    def clean_text(self, text):
        """文本清洗"""
        # 基本清洗
        text = text.strip().lower()
        # 标准化同义词
        synonyms = {
            'show': 'select',
            'display': 'select',
            'list': 'select',
            'workers': 'employees',
            'staff': 'employees'
        }
        
        for synonym, standard in synonyms.items():
            text = text.replace(synonym, standard)
        
        return text
    
    def link_to_schema(self, entities):
        """将实体链接到数据库schema"""
        linked = {}
        
        # 链接表名
        for table in entities.get('table_names', []):
            if table in self.schema['tables']:
                linked.setdefault('tables', []).append(table)
        
        # 链接列名
        for column in entities.get('column_names', []):
            for table, columns in self.schema['columns'].items():
                if column in columns:
                    linked.setdefault('columns', []).append({
                        'column': column,
                        'table': table
                    })
        
        return linked
    
    def get_relevant_schema(self, linked_entities):
        """获取相关的schema信息"""
        relevant_schema = {}
        
        # 获取相关表的完整信息
        for table in linked_entities.get('tables', []):
            relevant_schema[table] = self.schema['columns'][table]
        
        return relevant_schema

# 示例使用
preprocessor = Text2SQLPreprocessor()
query = "Show me all employees with salary greater than 50000"
result = preprocessor.preprocess(query)

print("预处理结果:")
for key, value in result.items():
    print(f"{key}: {value}")

总结

本章详细介绍了自然语言处理的基础知识，这些是理解和实现Text2SQL系统的重要基础：

1. 文本预处理：分词、清洗、标准化等基础操作
2. 词汇表示：从独热编码到词向量的各种表示方法
3. 语言模型：N-gram模型和神经语言模型
4. 序列标注：NER、POS、依存分析等任务
5. 语义表示：语义角色标注和语义解析
6. 预训练模型：BERT、GPT等现代NLP模型
7. 实际应用：在Text2SQL中的具体应用
8. 评估方法：文本相似度、BLEU等评估指标

掌握这些NLP基础知识后，您将能够更好地理解Text2SQL系统如何处理自然语言输入，为后续的模型架构设计和实现打下坚实基础。在下一章中，我们将深入学习Text2SQL的具体模型架构。