Deep Learning, NLP, NMT, AI, ML
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
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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.
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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 […]
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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. […]
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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 […]
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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 […]
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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 […]
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Domain adaptation or transfer learning using pre-trained language models such as BERT has proven to be an effective approach for many natural language processing tasks. In this work, we propose to formulate word sense disambiguation as a relevance ranking task, and fine-tune BERT on sequence-pair ranking task to select the most probable sense definition given a context sentence and a list of candidate sense definitions… We also introduce a data augmentation technique for WSD using existing example sentences from WordNet. […]
Read more“There is no alternative to learning through experience.” This quote rings true for every aspect of our life. And it takes on a whole new meaning in data science. Data Science hackathons area great way to: Test your data science knowledge Compete against top data science experts from around the world and gauge where you stand Get hands-on practice of a data science problem working in a deadline environment Improve your existing data science skillset Enhance your existing data science […]
Read moreOverview Microsoft Excel is an excellent tool for learning and executing statistical functions Here are 12 statistical functions in Excel that you should master for a successful analytics career Let’s Excel in Statistics! “Statistics is the grammar of Science.” – Karl Pearson Let’s make that a bit more relevant for us – Statistics is the grammar of “Data” Science. You’ll notice that almost every successful data science professional or analytics professional has a solid understanding of statistics – but […]
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