Lemmatization in Natural Language Processing (NLP) and Machine LearningData Science by Sunny Srinidhi - February 26, 2020February 26, 20200 Lemmatization is one of the most common text pre-processing techniques used in Natural Language Processing (NLP) and machine learning in general. If you've already read my post about stemming of words in NLP, you'll already know that lemmatization is not that much different. Both in stemming and in lemmatization, we try to reduce a given word to its root word. The root word is called a stem in the stemming process, and it is called a lemma in the lemmatization process. But there are a few more differences to the two than that. Let's see what those are. How is Lemmatization different from Stemming In stemming, a part of the word is just chopped off at the tail end to arrive at
Stemming of words in Natural Language Processing, what is it?Data Science by Sunny Srinidhi - February 19, 2020August 27, 20241 Stemming is one of the most common data pre-processing operations we do in almost all Natural Language Processing (NLP) projects. If you're new to this space, it is possible that you don't exactly know what this is even though you have come across this word. You might also be confused between stemming and lemmatization, which are two similar operations. In this post, we'll see what exactly is stemming, with a few examples here and there. I hope I'll be able to explain this process in simple words for you. Stemming To put simply, stemming is the process of removing a part of a word, or reducing a word to its stem or root. This might not necessarily mean we're reducing a word
Overriding Spring Boot properties in Amazon LambdaTech by Sunny Srinidhi - February 11, 2020February 11, 20200 In this post, we’ll see how we can maintain Spring Boot properties in an Amazon Lambda function without making code changes.
Removing stop words in Java as part of data cleaning in Artificial IntelligenceData Science by Sunny Srinidhi - February 5, 2020February 5, 20200 More in The fastText Series. Working with text datasets is very common in data science problems. A good example of this is sentiment analysis, where you get social network posts as data sets. Based on the content of these posts, you need to estimate the sentiment around a topic of interest. When we're working with text as the data, there are a lot of words which we want to remove from the data to "clean" it, such as normalising, removing stop words, stemming, lemmatizing, etc. In this post, we'll see how we can remove stop words from our input text to clean our data so that our analysis is based only on the actual content of the data. But wait, what are stop
Descriptive and Inferential statistics – the two types of statisticsData Science by Sunny Srinidhi - January 30, 2020January 30, 20200 In this post, we’re looking at two major types of statistics – descriptive statistics and inferential statistics.
Binary Tree Implementation in JavaTech by Sunny Srinidhi - January 27, 2020January 27, 20201 In this post, we will see how we can implement a binary tree in Java. We will be using Double Linked List internally for this implementation.
Sorting in MongoDB in Java using BasicDBObjectTech by Sunny Srinidhi - January 24, 2020January 24, 20200 In this post, we’ll see how we can write a sort query for MongoDB in Java using the BasicDBObject class. I’ll use Spring Boot for this.
Emulating Apache Kafka with Amazon SNS and SQSTech by Sunny Srinidhi - January 22, 2020January 24, 20200 We’ll learn how to introduce the concept of consumer groups from Kafka in the AWS world using Amazon SNS and Amazon SQS.
Publishing messages to Amazon SNS from a Spring Boot applicationTech by Sunny Srinidhi - January 20, 2020January 24, 20200 We’ll learn how we can publish messages to an SNS topic from a Spring Boot application. This can be done from any Java code or framework.
Receiving messages from Amazon SQS in a Spring Boot applicationTech by Sunny Srinidhi - January 16, 2020January 24, 20200 In this post, we'll see how we can receive messages from an Amazon SQS queue in a Spring Boot application. This is a continuation of the previous post where we talked about how we can send messages to an SQS queue. The obvious next part of that is how do we receive those messages. So in this post, we'll do just that. If you don't have an Amazon SQS queue created already, checkout the previous post on how to do it. Here, I'll assume that you already have that pipeline setup. So I'm going to skip that part of the post. We'll jump right into the code. The Code The first thing we need to add in our Spring Boot application is the