#!/usr/bin/env python
# coding: utf-8# In[6]:import nltk
import string# Function to get the lemma of a word based on its POS tag
def getLemma(myWord, myTag):wnl = nltk.WordNetLemmatizer()if myTag.startswith('V'):return wnl.lemmatize(myWord, 'v')else:return wnl.lemmatize(myWord.lower())# Function to check if a word is in the dictionary
def isInDict(myWord, myTag):if myWord in string.punctuation:return Truewordlist = nltk.corpus.words.words()myLemma = getLemma(myWord, myTag)return myLemma in wordlist# Read the file
f = open ('error.txt')
text = f.read()# Tokenize into sentences
sentences = nltk.sent_tokenize(text)# List of modal verbs
modal_verbs = set(['can', 'could', 'may', 'might', 'shall', 'should', 'will', 'would', 'must', 'ought'])# Process each sentence
for sentence in sentences:print(sentence)errors = []# Capitalization errorwords = nltk.word_tokenize(sentence)if len(words) > 0:first_word = words[0]if len(first_word) > 0 and first_word[0].islower():errors.append(f"** Capitalization error: {first_word}")# POS tag the sentencetagged = nltk.pos_tag(words)# Fragment error (no verb in sentence)has_verb = any(tag.startswith('V') for word, tag in tagged)if not has_verb:errors.append("** Fragment error")# Verb form errorfor i, (word, tag) in enumerate(tagged):if word in modal_verbs:if i+1 < len(tagged):next_word, next_tag = tagged[i+1]if next_tag.startswith('V') and not getLemma(next_word, next_tag) == next_word:errors.append(f"** Verb form error: {next_word}")elif word == 'to':if i+1 < len(tagged):next_word, next_tag = tagged[i+1]if next_tag.startswith('V') and not getLemma(next_word, next_tag) == next_word:errors.append(f"** Verb form error: {next_word}")# Subject-verb agreement errorsubjects = [word for word, tag in tagged if tag in ['NN', 'NNS', 'PRP']]verbs = [word for word, tag in tagged if tag.startswith('V')]if subjects and verbs:# Assume first subject and first verbsubj = subjects[0]verb = verbs[0]# Determine subject numberif subj.endswith('s') or subj in ['they', 'them', 'their', 'theirs', 'we', 'us', 'our', 'ours']:subj_plural = Trueelse:subj_plural = False# Determine verb numberif verb.endswith('s'):verb_singular = Trueelse:verb_singular = False# Check agreementif subj_plural and verb_singular:errors.append(f"** Subject-verb agreement error: {verb}")elif not subj_plural and not verb_singular:errors.append(f"** Subject-verb agreement error: {verb}")# Spelling errorfor word, tag in tagged:if tag not in ['NNP', 'NNPS'] and word not in string.punctuation:if not isInDict(word, tag):errors.append(f"** Spelling error: {word}")# Print errorsfor error in errors:print(error)print()
f.close()# In[ ]:
以下是对上述代码的详细解释：
1. 代码功能概述
- 该Python脚本实现了一个简单的文本语法和拼写检查器。它读取一个文本文件，对其中的每个句子进行分析，检查并报告可能存在的多种错误类型，包括大写错误、句子片段错误、动词形式错误、主谓一致错误和拼写错误等。
2. 代码详细解释
导入必要的库
import nltk
import string
- 导入
nltk库用于自然语言处理任务，如词性标注、词干提取和句子切分等。string库用于处理字符串相关的操作，这里主要用于判断标点符号。
定义获取词干的函数
def getLemma(myWord, myTag):wnl = nltk.WordNetLemmatizer()if myTag.startswith('V'):return wnl.lemmatize(myWord, 'v')else:return wnl.lemmatize(myWord.lower())
- 该函数根据给定单词的词性标签（POS tag）获取其词干。如果词性标签以
V开头（表示动词），则使用动词词干提取方法；否则，将单词转换为小写后进行普通词干提取。
定义检查单词是否在字典中的函数
def isInDict(myWord, myTag):if myWord in string.punctuation:return Truewordlist = nltk.corpus.words.words()myLemma = getLemma(myWord, myTag)return myLemma in wordlist
- 该函数首先检查单词是否为标点符号，如果是则返回
True。然后获取单词的词干，并检查该词干是否在nltk的单词列表中。
读取文本文件并进行句子切分
f = open('error.txt')
text = f.read()
sentences = nltk.sent_tokenize(text)
- 打开名为
error.txt的文件，读取其中的文本内容，并使用nltk的sent_tokenize函数将文本切分为句子列表。
定义模态动词列表
modal_verbs = set(['can', 'could', 'may', 'might', 'shall', 'should', 'will', 'would', 'must', 'ought'])
- 定义了一个包含常见模态动词的集合，用于后续检查动词形式错误。
对每个句子进行错误检查
for sentence in sentences:print(sentence)errors = []...
- 遍历每个句子，对其进行多种错误类型的检查，并将发现的错误信息存储在
errors列表中，最后打印出句子和对应的错误信息。
大写错误检查
words = nltk.word_tokenize(sentence)
if len(words) > 0:first_word = words[0]if len(first_word) > 0 and first_word[0].islower():errors.append(f"** Capitalization error: {first_word}")
- 对句子进行单词切分，检查句子的第一个单词是否为小写，如果是则报告大写错误。
句子片段错误检查
tagged = nltk.pos_tag(words)
has_verb = any(tag.startswith('V') for word, tag in tagged)
if not has_verb:errors.append("** Fragment error")
- 对句子进行词性标注，检查句子中是否存在动词，如果不存在则报告句子片段错误。
动词形式错误检查
for i, (word, tag) in enumerate(tagged):if word in modal_verbs:if i+1 < len(tagged):next_word, next_tag = tagged[i+1]if next_tag.startswith('V') and not getLemma(next_word, next_tag) == next_word:errors.append(f"** Verb form error: {next_word}")elif word == 'to':if i+1 < len(tagged):next_word, next_tag = tagged[i+1]if next_tag.startswith('V') and not getLemma(next_word, next_tag) == next_word:errors.append(f"** Verb form error: {next_word}")
- 遍历句子中的单词及其词性标签，检查模态动词或
to后面的动词是否使用了