Tokenizing and tagging sentences is an important part of natural language processing (NLP). It is the process of breaking down a text into smaller units, such as words, phrases, symbols, and other elements. Tokenizing sentences also allows for the identification of parts of speech, such as nouns, adjectives, and verbs. In this article, we will explore how to tokenize and tag sentences using the Natural Language Toolkit (NLTK) Python library.
What Is Tokenizing?
Tokenizing is the process of breaking down a text into smaller units, such as words, phrases, symbols, and other elements. Tokenizing sentences allows us to identify which words are nouns and which are adjectives, as well as to identify the syntactic structure of a sentence. For example, if we tokenize the sentence “John ate the apple”, we would get the following tokens: John, ate, the, apple.
Tokenizing can also be used for more than just parsing sentences. It can also be used for extracting keywords from texts, as well as for identifying collocations, which are two or more words that often occur together.
How to Tokenize Sentences Using NLTK
Now that we know what tokenizing is, let’s take a look at how to do it using NLTK. The first step is to import the Twitter corpus from the NLTK library. We will then create a variable for our tweets, and assign to it the list of strings from the positive JSON file tweets.
from nltk.corpus import twitter_samples
tweets = twitter_samples.strings(‘positive_tweets.json’)
Once we have loaded our first list of tweets, every tweet will be represented as one string. Before we can determine which words in our tweets are nouns or adjectives, we must tokenize our sentences. To tokenize our Twitter samples, we must create a new variable, named tweets_tokens, and assign to it all the tokenized lists of tweets.
from nltk.corpus import twitter_samples
tweets = twitter_samples.strings(‘positive_tweets.json’)
tweets_tokens = twitter_samples.tokenized(‘positive_tweets.json’)
This new variable we have created contains all the elements in our Twitter list as a list of tokens. Now that we have the tokens from every tweet, we can tag them with the proper part of speech tags.
What Is Tagging?
Tagging is the process of assigning part of speech tags to words. These tags are used to identify the type of word, such as noun, verb, adjective, adverb, etc. Tagging is an important part of natural language processing and can be used for tasks such as text classification, sentiment analysis, and question answering.
How to Tag Sentences Using NLTK
Now that we know what tagging is, let’s take a look at how to do it using NLTK. The first step is to import the part of speech tagger from the NLTK library. We will then create a new variable, named tweets_tagged, and use it for storing our tagged lists.
from nltk.corpus import twitter_samples
from nltk.tag import pos_tag_sents
tweets = twitter_samples.strings(‘positive_tweets.json’)
tweets_tokens = twitter_samples.tokenized(‘positive_tweets.json’)
tweets_tagged = pos_tag_sents(tweets_tokens)
The first element in our tweets_tagged variable will look like this:
[(u’#FollowFriday’, ‘JJ’), (u’@France_Inte’, ‘NNP’), (u’@PKuchly57′, ‘NNP’), (u’@Milipol_Paris’, ‘NNP’),
(u’for’, ‘IN’), (u’being’, ‘VBG’), (u’top’, ‘JJ’), (u’engaged’, ‘VBN’), (u’members’, ‘NNS’), (u’in’, ‘IN’),
(u’my’, ‘PRP$’), (u’community’, ‘NN’), (u’this’, ‘DT’), (u’week’, ‘NN’), (u’:)’, ‘NN’)]
Here, we can see that the tweets we imported are characterized as a list representing each token we have information about as a part of speech tag. Moreover, each token pair or tag is saved as a tuple. In NLTK, adjectives are denoted as JJ while singular nouns are denoted as NN and plural nouns are denoted as NNS. In the next section, we will count only singular nouns.
Conclusion
In this article, we have explored how to tokenize and tag sentences using the Natural Language Toolkit (NLTK) Python library. We looked at how to tokenize sentences, which involves breaking down a text into smaller units, such as words, phrases, symbols, and other elements. We then looked at how to tag sentences, which involves assigning part of speech tags to words. Finally, we looked at how to count only singular nouns. We hope that this article has been helpful in understanding tokenizing and tagging sentences.