NLP

Best Practices for Data-Efficient Modeling in NLG: How to Train Production-Ready Neural Models with Less Data

December 8, 2020 By: Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White Abstract Natural language generation (NLG) is a critical component in conversational systems, owing to its role of formulating a correct and natural text response. Traditionally, NLG components have been deployed using template-based solutions. Although neural network solutions recently developed in the research community have been shown to provide several benefits, deployment of […]

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Machine Learning

Semi-Supervised Learning With Label Spreading

Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike supervised learning algorithms that are only able to learn from labeled training data. A popular approach to semi-supervised learning is to create a graph that connects examples in the training dataset and propagates known labels through the edges of the graph to label unlabeled examples. An example of this approach to semi-supervised learning is the label spreading algorithm […]

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python_tutorials
Python

How to Use Global and Nonlocal Variables in Python

Introduction In this article we’ll be taking a look at Global and Non-Local Variables in Python and how you to use them to avoid issues when writing code. We’ll be starting off with a brief primer on variable scopes before we launch into the how and why of using global and non-local variables in your own functions. Scopes in Python Before we can get started, we first have to touch on scopes. For those of you who are less familiar, […]

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Machine Learning

Semi-Supervised Learning With Label Propagation

Semi-supervised learning refers to algorithms that attempt to make use of both labeled and unlabeled training data. Semi-supervised learning algorithms are unlike supervised learning algorithms that are only able to learn from labeled training data. A popular approach to semi-supervised learning is to create a graph that connects examples in the training dataset and propagate known labels through the edges of the graph to label unlabeled examples. An example of this approach to semi-supervised learning is the label propagation algorithm […]

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Machine Learning

Multinomial Logistic Regression With Python

Multinomial logistic regression is an extension of logistic regression that adds native support for multi-class classification problems. Logistic regression, by default, is limited to two-class classification problems. Some extensions like one-vs-rest can allow logistic regression to be used for multi-class classification problems, although they require that the classification problem first be transformed into multiple binary classification problems. Instead, the multinomial logistic regression algorithm is an extension to the logistic regression model that involves changing the loss function to cross-entropy loss […]

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Advanced, Deep Learning, NLP, Project, Python, Semi-supervised, Structured Data, Technique, Text Leave a comment

Fake news classifier on US Election News📰 | LSTM 🈚

Introduction News media has become a channel to pass on the information of what’s happening in the world to the people living. Often people perceive whatever conveyed in the news to be true. There were circumstances where even the news channels acknowledged that their news is not true as they wrote. But some news has a significant impact not only on the people or    

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python_tutorials
Python

Ultimate Guide to Heatmaps in Seaborn with Python

Introduction A heatmap is a data visualization technique that uses color to show how a value of interest changes depending on the values of two other variables. For example, you could use a heatmap to understand how air pollution varies according to the time of day across a set of cities. Another, perhaps more rare case of using heatmaps is to observe human behavior – you can create visualizations of how people use social media, how their answers on surveys […]

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Machine Learning

Histogram-Based Gradient Boosting Ensembles in Python

Gradient boosting is an ensemble of decision trees algorithms. It may be one of the most popular techniques for structured (tabular) classification and regression predictive modeling problems given that it performs so well across a wide range of datasets in practice. A major problem of gradient boosting is that it is slow to train the model. This is particularly a problem when using the model on large datasets with tens of thousands of examples (rows). Training the trees that are […]

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NLP

Efficient One-Pass End-to-End Entity Linking for Questions

November 16, 2020 By: Belinda Z. Li, Sewon Min, Srinivasan Iyer, Yashar Mehdad, Wen-tau Yih Abstract We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass. Evaluated on WebQSP and GraphQuestions with extended annotations that cover multiple entities per question, ELQ outperforms the previous state of the art by a large margin of +12.7% and +19.6% F1, respectively. With a very fast inference time (1.57 […]

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Machine Learning

Feature Selection with Stochastic Optimization Algorithms

Typically, a simpler and better-performing machine learning model can be developed by removing input features (columns) from the training dataset. This is called feature selection and there are many different types of algorithms that can be used. It is possible to frame the problem of feature selection as an optimization problem. In the case that there are few input features, all possible combinations of input features can be evaluated and the best subset found definitively. In the case of a […]

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