How to combine various ngram taggers like bigram and trigram taggers in nltk

This recipe helps you combine various ngram taggers like bigram and trigram taggers in nltk

Recipe Objective

This recipe explains how to combine various ngram taggers like bigram and trigram taggers.

Step 1: Importing library

Let us first import the necessary libraries. We'll import treebank, backoff_tagger, DefaultTagger and SequentialBackoffTagger from nltk.corpus, tag_util and, nltk.tag respectively.

from nltk.corpus import treebank
from nltk.tag import SequentialBackoffTagger
from tag_util import backoff_tagger
from nltk.tag import DefaultTagger

Step 2: Ngram taggers

ngram is a subsequence of n items. Each tagger maintains a context dictionary and this dictionary is used to guess that tag based on the context. For each tagger class backoff_tagger, function creates an instance. This tagger then becomes the backoff tagger for the next tagger class also the order of these taggers is important. The train is given as a backoff.

train = treebank.tagged_sents()[:1000]
test = treebank.tagged_sents()[1000:]
f = DefaultTagger('DT')
t = backoff_tagger(train, [UnigramTagger, BigramTagger, TrigramTagger], backoff = f)
tag.evaluate(test)

What Users are saying..

profile image

Anand Kumpatla

Sr Data Scientist @ Doubleslash Software Solutions Pvt Ltd
linkedin profile url

ProjectPro is a unique platform and helps many people in the industry to solve real-life problems with a step-by-step walkthrough of projects. A platform with some fantastic resources to gain... Read More

Relevant Projects

NLP Project on LDA Topic Modelling Python using RACE Dataset
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.

Deep Learning Project- Real-Time Fruit Detection using YOLOv4
In this deep learning project, you will learn to build an accurate, fast, and reliable real-time fruit detection system using the YOLOv4 object detection model for robotic harvesting platforms.

Build Real Estate Price Prediction Model with NLP and FastAPI
In this Real Estate Price Prediction Project, you will learn to build a real estate price prediction machine learning model and deploy it on Heroku using FastAPI Framework.

MLOps using Azure Devops to Deploy a Classification Model
In this MLOps Azure project, you will learn how to deploy a classification machine learning model to predict the customer's license status on Azure through scalable CI/CD ML pipelines.

Build CNN for Image Colorization using Deep Transfer Learning
Image Processing Project -Train a model for colorization to make grayscale images colorful using convolutional autoencoders.

Build a Similar Images Finder with Python, Keras, and Tensorflow
Build your own image similarity application using Python to search and find images of products that are similar to any given product. You will implement the K-Nearest Neighbor algorithm to find products with maximum similarity.

Predictive Analytics Project for Working Capital Optimization
In this Predictive Analytics Project, you will build a model to accurately forecast the timing of customer and supplier payments for optimizing working capital.

PyTorch Project to Build a GAN Model on MNIST Dataset
In this deep learning project, you will learn how to build a GAN Model on MNIST Dataset for generating new images of handwritten digits.

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

Build a Churn Prediction Model using Ensemble Learning
Learn how to build ensemble machine learning models like Random Forest, Adaboost, and Gradient Boosting for Customer Churn Prediction using Python