NLP | Classifier-based tagging Last Updated : 16 Dec, 2019 Summarize Comments Improve Suggest changes Share Like Article Like Report ClassifierBasedPOSTagger class: It is a subclass of ClassifierBasedTagger that uses classification technique to do part-of-speech tagging. From the words, features are extracted and then passed to an internal classifier. It classifies the features and returns a label i.e. a part-of-speech tag. The feature detector finds multiple length suffixes, does some regular expression matching, and looks at the unigram, bigram, and trigram history to produce a fairly complete set of features for each word Code #1 : Using ClassifierBasedPOSTagger Python3 1== from nltk.tag.sequential import ClassifierBasedPOSTagger from nltk.corpus import treebank # initializing training and testing set train_data = treebank.tagged_sents()[:3000] test_data = treebank.tagged_sents()[3000:] tagging = ClassifierBasedPOSTagger(train = train_data) a = tagging.evaluate(test_data) print ("Accuracy : ", a) Output : Accuracy : 0.9309734513274336 ClassifierBasedPOSTagger class inherits from ClassifierBasedTagger and only implements a feature_detector() method. All the training and tagging is done in ClassifierBasedTagger. Code #2 : Using MaxentClassifier Python3 1== from nltk.classify import MaxentClassifier from nltk.corpus import treebank # initializing training and testing set train_data = treebank.tagged_sents()[:3000] test_data = treebank.tagged_sents()[3000:] tagger = ClassifierBasedPOSTagger( train = train_sents, classifier_builder = MaxentClassifier.train) a = tagger.evaluate(test_data) print ("Accuracy : ", a) Output : Accuracy : 0.9258363911072739 custom feature detector detecting features There are two ways to do it: Subclass ClassifierBasedTagger and implement a feature_detector() method. Pass a function as the feature_detector keyword argument into ClassifierBasedTagger at initialization. Code #3 : Custom Feature Detector Python3 1== from nltk.tag.sequential import ClassifierBasedTagger from tag_util import unigram_feature_detector from nltk.corpus import treebank # initializing training and testing set train_data = treebank.tagged_sents()[:3000] test_data = treebank.tagged_sents()[3000:] tag = ClassifierBasedTagger( train = train_data, feature_detector = unigram_feature_detector) a = tagger.evaluate(test_data) print ("Accuracy : ", a) Output : Accuracy : 0.8733865745737104 Comment More infoAdvertise with us Next Article NLP | Training Tagger Based Chunker | Set 1 M mohit gupta_omg :) Follow Improve Article Tags : NLP Python-nltk Natural-language-processing Similar Reads NLP | Classifier-based Chunking | Set 1 The ClassifierBasedTagger class learns from the features, unlike most part-of-speech taggers. ClassifierChunker class can be created such that it can learn from both the words and part-of-speech tags, instead of just from the part-of-speech tags as the TagChunker class does. The (word, pos, iob) 3-t 2 min read NLP | Classifier-based Chunking | Set 2 Using the data from the treebank_chunk corpus let us evaluate the chunkers (prepared in the previous article). Code #1 :Â Python3 # loading libraries from chunkers import ClassifierChunker from nltk.corpus import treebank_chunk train_data = treebank_chunk.chunked_sents()[:3000] test_data = treebank_ 2 min read NLP | Training Tagger Based Chunker | Set 2 Conll2000 corpus defines the chunks using IOB tags. It specifies where the chunk begins and ends, along with its types.A part-of-speech tagger can be trained on these IOB tags to further power a ChunkerI subclass.First using the chunked_sents() method of corpus, a tree is obtained and is then transf 3 min read NLP | Training Tagger Based Chunker | Set 1 To train a chunker is an alternative to manually specifying regular expression (regex) chunk patterns. But manually training to specify the expression is a tedious task to do as it follows the hit and trial method to get the exact right patterns. So, existing corpus data can be used to train chunker 2 min read Dictionary Based Tokenization in NLP Natural Language Processing (NLP) is a subfield of artificial intelligence that aims to enable computers to process, understand, and generate human language. One of the critical tasks in NLP is tokenization, which is the process of splitting text into smaller meaningful units, known as tokens. Dicti 5 min read NLP | WordNet for tagging WordNet is the lexical database i.e. dictionary for the English language, specifically designed for natural language processing. Code #1 : Creating class to look up words in WordNet. Python3 1== from nltk.tag import SequentialBackoffTagger from nltk.corpus import wordnet from nltk.probability import 2 min read Like