python - 导入 wordnet 和停用词时出现 chaquopy 错误

Question

from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.corpus import wordnet as wn
#from itertools import product

#variables that will be used

database_word_synset=[]
uploaded_sentence_synset=[]
uploaded_sentence_words_tokenized=[]
filtered_uploaded_sentences = []
database_sentence_words_tokenized=[]
filtered_database_sentence=[]
database_sentence_synset=[]

word_check=[0.0]
sentence_check=[0.0]
count_sentence=0
count_word=0
not_fond=0

#the given data

uploaded_sentence=" The issue of text semantics, such as word semantics and sentence semantics has received increasing attentions in recent years. However, rare research focuses on the document-level semantic matching due to its complexity. Long documents usually have sophisticated structure and massive information, which causes hardship to measure their semantic similarity. The semantic similarity between words, sentences, texts, and documents is widely studied in various fields, including natural language processing, document semantic comparison, artificial intelligence, semantic web, and semantic search engines. "
database_word=["car","complete",'run',"sleep"]
database_sentence="the earth is round not flat"

stopwords = stopwords.words('english')
uploaded_sentence_words_tokenized = word_tokenize(uploaded_sentence)

#filtering the sentence and synset

for word in uploaded_sentence_words_tokenized:
    if word not in stopwords:      
        filtered_uploaded_sentences.append(word)
print (filtered_uploaded_sentences)

for sentences_are in filtered_uploaded_sentences:
    uploaded_sentence_synset.append(wn.synsets(sentences_are))
    
print(uploaded_sentence_synset)

#for finding similrity in the words

for databasewords in database_word:
    database_word_synset.append(wn.synsets(databasewords))
    
print(database_word_synset)



words_list_synset=list()
for t in database_word_synset: 
    for x in t: 
        words_list_synset.append(x)

print(words_list_synset)




#removing empty list element and making single dimension list

removing_empty_list_uploaded_sentence=list()
removing_empty_list_uploaded_sentence = [x for x in uploaded_sentence_synset if x != []]

up_list_sentence=list()
for t in removing_empty_list_uploaded_sentence: 
    for x in t: 
        up_list_sentence.append(x)

print(up_list_sentence)

#the similarity main function for words
#sims=[]
#for sense1, sense2 in product(database_word_synset, up_list_sentence):
#    d = wn.wup_similarity(sense1, sense2)
#    sims.append(d)
#print (sims)
#word_found=list()
for data in words_list_synset:
    for sen in up_list_sentence :
        if wn.wup_similarity(data,sen) is None or wn.wup_similarity(data,sen) <0.70:
            not_fond=not_fond+1
        else:
            count_word=count_word+1


print (word_check)
print("\n words that are not found :",not_fond)
print("\n words that are found :", count_word)
#for finding similrity in the sentence

database_sentence_words_tokenized=word_tokenize(database_sentence)

for word in database_sentence_words_tokenized:
    if word not in stopwords:
        filtered_database_sentence.append(word)
print(filtered_database_sentence)

for sentence_synset in filtered_database_sentence:
    database_sentence_synset.append(wn.synsets(sentence_synset))
print(database_sentence_synset)

#removing empty list element and making single dimension list

removing_empty_list_db=list()
removing_empty_list_db = [x for x in database_sentence_synset if x != []]

db_list_sentence=list()
for t in removing_empty_list_db: 
    for x in t: 
        db_list_sentence.append(x)

print(db_list_sentence)

#the similarity main function for sentence

for db_sentence in db_list_sentence:
   for upl_sentence in up_list_sentence:
       sentence_check.append(wn.wup_similarity(db_sentence,upl_sentence))
           
for sentence_checks in sentence_check:
   if sentence_checks is None or sentence_checks <0.70:
      not_fond=not_fond+1
   else:
       count_sentence=count_sentence+1   
       
print (sentence_check)
print("\n words that are not found :",not_fond)
print("\n words that are found :",count_sentence)

在构建文件 android studio 中安装库：

在这个项目中，我们使用 chaquopy 在我们的 android 项目中使用 python，但它有一些问题，比如在导入库时我已经安装了 Nltk、wordnet、停止词和词标记化，但我无法在 python 文件中访问这些库和如果我们安装我们的应用程序，它会崩溃。

 if (! Python.isStarted()) {
           Python.start(new AndroidPlatform(this));
           Python py = Python.getInstance();
           final PyObject pyobj = py.getModule("sum");


           b2.setOnClickListener(new View.OnClickListener() {
               @Override
               public void onClick(View view) {
                   if (path==null) {
                       Toast.makeText(documentupload.this, " plz upload the doc", Toast.LENGTH_SHORT).show();
                       //upload.setText(path);


                       // Intent intent= new Intent(documentupload.this,result.class);
                       //startActivity(intent);
                   }
                   else {
                       PyObject obj = pyobj.callAttr("main", Words.toString());
                       upload.setText(obj.toString());
                       Toast.makeText(documentupload.this, "uploaded" + Words, Toast.LENGTH_LONG).show();
                      // Toast.makeText(documentupload.this, " plz upload the doc", Toast.LENGTH_LONG).show();
                   }
               }
           });

当应用程序崩溃时，它会给出以下错误消息：

标签： pythonandroid-studionltkchaquopy

Answer 1

我假设崩溃发生在打电话的时候wn.synsets？这是我看到的堆栈跟踪：

  File "/data/user/0/com.chaquo.python.pkgtest3/files/chaquopy/AssetFinder/requirements/nltk/corpus/util.py", line 120, in __getattr__
  File "/data/user/0/com.chaquo.python.pkgtest3/files/chaquopy/AssetFinder/requirements/nltk/corpus/util.py", line 85, in __load
  File "/data/user/0/com.chaquo.python.pkgtest3/files/chaquopy/AssetFinder/requirements/nltk/corpus/util.py", line 80, in __load
  File "/data/user/0/com.chaquo.python.pkgtest3/files/chaquopy/AssetFinder/requirements/nltk/data.py", line 585, in find
LookupError: 
**********************************************************************
  Resource [93mwordnet[0m not found.

我不认为“wordnet”和“corpus”pip 包与 nltk 有任何关系。相反，您应该使用 安装它们nltk.download，就像错误消息中所说的那样。

由于模拟器错误，您可能需要nltk.download循环调用，如本答案中所述。