Suppose I have a text document containing multiple sentences. I want to enhance the language used in the document by replacing weak adjectives with stronger ones. For example, in the sentence "She was a happy girl," I want to replace the adjective "happy" with a stronger one.
What would be an effective approach to find and replace weak adjectives with stronger ones in a given text document? Can you provide a sample code in Python that uses NLTK library to tag the adjectives and replace them with stronger synonyms from WordNet database?
user
Sure, I can guide you on how to approach this task using Python's NLTK library. Here's a general approach:

1. Install NLTK using pip: `pip install nltk`
2. Load the required NLTK data (this may take a while): `import nltk`
   - `nltk.download('wordnet')`
3. Load a corpus to find strong synonyms. Here, you can use WordNet's synsets (Word Sense Disambiguation) to get strong adjective terms.

   ```python
   import wordnet
   wordnet_lemmatizer = wordnet.WordNetLemmatizer()
   synsets = wordnet.synsets(wordnet_lemmatizer.lemmatize('adjective'), pos=wordnet.NOUN, lemma_func=wordnet_lemmatizer.lemmatize)
   ```

4. Use NLTK's `pos_tag` method to tag the adjectives and use them as keys for the `wordnet.synsets()` method.

   ```python
   from nltk.tokenize import word_tokenize

   doc = nltk.word_tokenize(text)
   adjs = [word for word, _ in nltk.pos_tag(doc)]
   synset_keys = {word: pos for word, pos in wordnet.synsets(wordnet_lemmatizer.lemmatize(word), pos=wordnet.NOUN, lemma_func=wordnet_lemmatizer.lemmatize)}
   ```

5. Use WordNet synset ids of longer/stronger synonyms as a dictionary key and their corresponding term(s) in lemmas as values.

   ```python
   synset_longest = {synset.name(): [lem for syn, lem in synset.lemmas(pos=wordnet.NOUN, lemma=True)] for synset in synset_keys.values()}
   ```

6. For each adjective term, find a stronger synonym from WordNet synsets. You can find long synonyms by length or by utilizing WordNet's synset hierarchy. Here's a basic example:

   ```python
   def find_stronger_adjectives(text, threshold=1):
       adjs = word_tokenize(text)
       strong_adjs = []
       for adj in adjs:
           if adj.endswith('ly') or adj.endswith('ly'):
               # remove the adverbial ending
               adj = adj[:-2]
           else:
               adj = nltk.word_tokenize(adj)[0]
           if adj in synset_keys:
               synset = synset_keys[adj]
               if len(synset.lemmas(pos=wordnet.NOUN, lemma=True)) > 1:
                   # Choose the longest word in the wordnet synset
                   strong_adj = [word for word, _ in wordnet.synsets(wordnet_lemmatizer.lemmatize(adj), pos=wordnet.NOUN)][0]
               else:
                   # If the adjective is in a synset with just one term, assign that term to a list
                   strong_adj = synset_longest.get(synset.name()) or []
                   if len(strong_adj) > threshold:
                       # If the length meets the threshold, choose the longest one
                       strong_adj = strong_adj[0]
                   else:
                       strong_adj = strong_adj
   return strong_adjs
   ```

   This function uses the `wordnet.synsets()` method to find all synsets containing the input adjective. If the synset has more than one term, it returns the longest lemma. Otherwise, it checks the WordNet Synset database to find background synonyms from any synset that has been used outside of a POSTag.

7. Finally, replace these strong adjectives in the text using a list comprehension, like this:

   ```python
   processed_text = ' '.join([adj if not is_adj(word) else find_stronger_adjectives(word, threshold=1)[0] for word in text.split()])
   print(processed_text)
   ```

   Here `is_adj` is a simple example.

   ```python
   import nltk

   def is_adj(word):
       pos = nltk.POS(word)
       return pos.startswith('JJ') or pos.startswith('JJR') or pos.startswith('JJS')
   ```

   That's a simplified version of the code. You will and should optimize it if you're planning a commercial app or industrialized NLP scorecard. This is a rather Limited blog answer, and the Python nltk construction accepts a method, sentence identifiers. The code should also come with extra features with the permutation method to boost it so it's independent of input size influences as explained above. Good luck with your undertaking and you're welcome!
assistant
Suppose I have a text document containing multiple sentences. I want to enhance the language used in the document by replacing weak adjectives with stronger ones. For example, in the sentence "She was a happy girl," I want to replace the adjective "happy" with a stronger one.
What would be an effective approach to find and replace weak adjectives with stronger ones in a given text document? Can you provide a sample code in Python that uses NLTK library to tag the adjectives and replace them with stronger synonyms from WordNet database?
user
To enhance the language used in the document by replacing weak adjectives with stronger ones, you can follow these steps:

1. Tokenize the text into sentences and words using the NLTK library.
2. Use part-of-speech tagging to identify the adjectives in the text.
3. Extract synonyms for each adjective from the WordNet database using NLTK.
4. Filter and select stronger synonyms using a score or your judgment.
5. Replace the original adjectives with the stronger synonyms in the text.

Here's a sample Python code that uses the NLTK library to achieve this:

```python
import nltk
from nltk.corpus import wordnet as wn
from nltk.tokenize import word_tokenize, sent_tokenize

nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')

def get_stronger_synonym(word):
    synonyms = []
    for syn in wn.synsets(word, pos='a'):  # only look for adjectives
        for lemma in syn.lemmas():
            synonyms.append(lemma.name())
    stronger_synonym = word  # set default to the original word

    # You can implement your logic to select a stronger synonym here
    # For example, you might use WordNet's path_similarity or another metric
    # For now, we'll just return the first synonym found
    if synonyms:
        stronger_synonym = synonyms[0]

    return stronger_synonym

def enhance_text(text):
    sentences = sent_tokenize(text)
    enhanced_sentences = []

    for sentence in sentences:
        words = word_tokenize(sentence)
        tagged_words = nltk.pos_tag(words)
        enhanced_words = [word if tag not in ['JJ', 'JJR', 'JJS'] else get_stronger_synonym(word) for word, tag in tagged_words]
        enhanced_sentence = ' '.join(enhanced_words)
        enhanced_sentences.append(enhanced_sentence)

    return ' '.join(enhanced_sentences)

text = "She was a happy girl."
enhanced_text = enhance_text(text)
print(enhanced_text)
```
assistant