"Tokenizernltk" Example Sentences
1. The tokenizernltk module is a powerful tool for natural language processing.
2. She used the tokenizernltk library to split the text into individual words.
3. The tokenizernltk package comes with several different algorithms for tokenization.
4. Our team relied heavily on the tokenizernltk library when developing our chatbot.
5. The tokenizernltk module is easy to install and use in Python.
6. The tokenizernltk library provides a simple way to tokenize natural language text.
7. We used the tokenizernltk package to split the reviews into sentences.
8. The tokenizernltk module makes it easier to preprocess text data for machine learning models.
9. She wrote a custom tokenizer using tokenizernltk to better fit her specific use case.
10. The tokenizernltk package is often used in text classification and sentiment analysis tasks.
11. The tokenizernltk module can handle various types of text data, including social media posts and news articles.
12. We experimented with different tokenization methods provided by tokenizernltk to find the best one for our project.
13. The tokenizernltk library is widely used in the natural language processing community.
14. The tokenizernltk module can also be used to tokenize text in languages other than English.
15. The tokenizernltk library includes pre-trained models for tokenization tasks, making it easy to get started.
16. We used the tokenizernltk package to tokenize text before feeding it to our named entity recognition model.
17. The tokenizernltk module helped us to preprocess and clean our text data before applying machine learning algorithms.
18. By using tokenizernltk, we were able to easily extract named entities from the text.
19. The tokenizernltk package is a valuable tool for preprocessing/rawing text data for analysis.
20. The tokenizernltk module made it simple to tokenize the text and remove stop words.
21. We combined tokenizernltk with other natural language processing tools to achieve more accurate sentiment analysis.
22. The tokenizernltk library allowed us to tokenize the text without losing important context.
23. By using the tokenizernltk module, we were able to quickly preprocess and preprocess thousands of documents.
24. The tokenizernltk package works well with other Python libraries commonly used for natural language processing.
25. We relied on the tokenizernltk module when developing our text classification model.
26. By using tokenizernltk, we were able to tokenize the text data in a way that preserved important sentence boundaries.
27. The tokenizernltk library is an essential tool for many natural language processing tasks.
28. The tokenizernltk module was used to tokenize the text before it was fed into the chatbot model.
29. We used a combination of tokenizernltk and regular expressions to extract named entities from the text.
30. The tokenizernltk package is an easy-to-use library for natural language processing beginners.
31. The tokenizernltk module can tokenize text data from a variety of sources, including web pages and social media.
32. By using tokenizernltk, we were able to easily preprocess data in preparation for machine learning models.
33. The tokenizernltk library offers several different tokenization algorithms, each with its strengths and weaknesses.
34. The tokenizernltk module is an essential component of many natural language processing pipelines.
35. We used tokenizernltk to split the text into individual words and then remove stop words for improved analysis.
36. The tokenizernltk package helped us to preprocess and clean our text data before applying topic modeling techniques.
37. The tokenizernltk module was used to preprocess the text data before being summarized.
38. Using tokenizernltk, we were able to accurately tokenize text in a way that preserved important punctuation marks.
39. The tokenizernltk library is continuously being updated and improved with new tokenization methods.
40. The tokenizernltk module is widely used in academic research and industry projects alike.
Common Phases
1. Initialize the tokenizer using "from nltk.tokenize import word_tokenize;"
2. Pass a string of text to the tokenizer using "text = 'This is a sample sentence';"
3. Tokenize the text using "tokens = word_tokenize(text);"
4. Print out the tokens using "print(tokens);"
5. Remove punctuation from the tokens using "import string;filtered_tokens = [w for w in tokens if not w in string.punctuation];"
6. Convert all tokens to lowercase using "lowercase_tokens = [w.lower() for w in filtered_tokens];"
7. Remove stopwords from the tokens using "from nltk.corpus import stopwords;stop_words = set(stopwords.words('english'));filtered_tokens = [w for w in lowercase_tokens if not w in stop_words];"
8. Stem the tokens using "from nltk.stem import PorterStemmer;stemmer = PorterStemmer();stemmed_tokens = [stemmer.stem(w) for w in filtered_tokens];"
9. Create a frequency distribution of the tokens using "from nltk.probability import FreqDist;fdist = FreqDist(stemmed_tokens);"
10. Print the most common words using "print(fdist.most_common(10));"