Beginner, Libraries, NLP, Python, Text, Unstructured Data Leave a comment

How to Use Texthero to Prepare a Text-based Dataset for Your NLP Project

Introduction Natural Language Processing (NLP) is one of the most important fields of study and research in today’s world. It has many applications in the business sector such as chatbots, sentiment analysis, and document classification. Preprocessing and representing text is one of the trickiest and most annoying parts of working on an NLP project. Text-based datasets can be incredibly thorny and difficult to preprocess. But fortunately, the latest Python package called Texthero can help you solve these challenges. What is […]

Read more
Advanced, NLP, Project, Python, Ranking, Technique, Text, Unstructured Data, Word Embeddings Leave a comment

Information Retrieval using word2vec based Vector Space Model

Overview Learn about Information Retrieval (IR), Vector Space Models (VSM), and Mean Average Precision (MAP) Create a project on Information Retrieval using word2vec based Vector Space Model   Introduction “Google it!”- Isn’t it something we say every day? Whenever we come across something that we don’t know about, we “Google it.” Google Search is a great tool that can be used for even finding a needle from a haystack. This generation absolutely relies on Google for answers to all kinds […]

Read more
Algorithm, Deep Learning, Intermediate, NLP, Supervised, Text, Unstructured Data Leave a comment

A Simple Introduction to Sequence to Sequence Models

Overview In this article, I would give you an overview of sequence to sequence models which became quite popular for different tasks like machine translation, video captioning, image captioning, question answering, etc. Prerequisites: The reader should already be familiar with neural networks and, in particular, recurrent neural networks (RNNs). In addition, knowledge of LSTM or GRU models is preferable. If you are not familiar with LSTM I would prefer you to read LSTM- Long Short-Term Memory.

Read more
NLP

How to develop LSTM recurrent neural network models for text classification problems in Python using Keras deep learning library

How to develop LSTM recurrent neural network models for text classification problems in Python using Keras deep learning library Automatic text classification or document classification can be done in many different ways in machine learning as we have seen before. This article aims to provide an example of how a Recurrent Neural Network (RNN) using the

Read more
NLP

Deep dive into multi-label classification..! (With detailed Case Study)

We first convert the comments to lower-case and then use custom made functions to remove html-tags, punctuation and non-alphabetic characters from the comments. import nltkfrom nltk.corpus import stopwordsfrom nltk.stem.snowball import SnowballStemmerimport reimport sysimport warningsdata = data_rawif not sys.warnoptions:warnings.simplefilter(“ignore”)def cleanHtml(sentence):cleanr = re.compile(”)cleantext = re.sub(cleanr, ‘ ‘, str(sentence))return cleantextdef cleanPunc(sentence): #function to clean the word of any punctuation or special characterscleaned Visit source site to finish reading.

Read more
NLP

Building a Simple Chatbot from Scratch in Python (using NLTK)

A chatbot is an artificial intelligence-powered piece of software in a device (Siri, Alexa, Google Assistant etc), application, website or other networks that try to gauge consumer’s needs and then assist them to perform a particular task like a commercial transaction, hotel booking, form submission etc . Today almost every company has a chatbot deployed to engage with the users. Some of the ways in which companies are using chatbots are: To deliver flight information to connect customers and their […]

Read more
Machine Learning, NETWORK PRUNING

A Gradient Flow Framework For Analyzing Network Pruning

Recent network pruning methods focus on pruning models early-on in training. To estimate the impact of removing a parameter, these methods use importance measures that were originally designed for pruning trained models… Despite lacking justification for their use early-on in training, models pruned using such measures result in surprisingly minimal accuracy loss. To better explain this behavior, we develop a general, gradient-flow based framework that relates state-of-the-art importance measures through an order of time-derivative of the norm of model parameters. […]

Read more
GRAPH EMBEDDING, Machine Learning

Scalable Recommendation of Wikipedia Articles to Editors Using Representation Learning

Wikipedia is edited by volunteer editors around the world. Considering the large amount of existing content (e.g. over 5M articles in English Wikipedia), deciding what to edit next can be difficult, both for experienced users that usually have a huge backlog of articles to prioritize, as well as for newcomers who that might need guidance in selecting the next article to contribute… Therefore, helping editors to find relevant articles should improve their performance and help in the retention of new […]

Read more
LANGUAGE MODELLING, Machine Learning

Grounded Compositional Outputs for Adaptive Language Modeling

Language models have emerged as a central component across NLP, and a great deal of progress depends on the ability to cheaply adapt them (e.g., through finetuning) to new domains and tasks. A language model’s emph{vocabulary}—typically selected before training and permanently fixed later—affects its size and is part of what makes it resistant to such adaptation… Prior work has used compositional input embeddings based on surface forms to ameliorate this issue. In this work, we go one step beyond and […]

Read more
Machine Learning

Secure Data Sharing With Flow Model

In the classical multi-party computation setting, multiple parties jointly compute a function without revealing their own input data. We consider a variant of this problem, where the input data can be shared for machine learning training purposes, but the data are also encrypted so that they cannot be recovered by other parties… We present a rotation based method using flow model, and theoretically justified its security. We demonstrate the effectiveness of our method in different scenarios, including supervised secure model […]

Read more
1 … 768 769 770 771 772 … 919