Text Classification


Text Classifier Algorithms in Machine Learning – Stats and Bots

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In fields such as computer vision, there's a strong consensus about a general way of designing models deep networks with lots of residual connections. In this article, we'll focus on the few main generalized approaches of text classifier algorithms and their use cases. When researchers compare the text classification algorithms, they use them as they are, probably augmented with a few tricks, on well-known datasets that allow them to compare their results with many other attempts on the same problem. The go-to solution here is to use pretrained word2vec embeddings and try to use lower learning rates for the embedding layer (multiply general learning rate by 0.1).


The Best Metric to Measure Accuracy of Classification Models

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To understand the implication of translating the probability number, let's understand few basic concepts relating to evaluating a classification model with the help of an example given below. Since we are now comfortable with the interpretation of the Confusion Matrix, let's look at some popular metrics used for testing the classification models: Since the formula doesn't contain FP and TN, Sensitivity may give you a biased result, especially for imbalanced classes. In the example of Fraud detection, it gives you the percentage of Correctly Predicted Frauds from the pool of Actual Frauds. In the example of Fraud detection, it gives you the percentage of Correctly Predicted Frauds from the pool of Total Predicted Frauds.


Instagram launches AI-backed offensive comment blocker

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Instagram (NASDAQ:FB) launches an AI-backed offensive comment blocker and a multilingual spam filter, according to a company post. Wired has a deep dive into the AI system backing the offensive comment blocker, which builds off a text classification system called DeepText that Facebook developed to help search for inappropriate content on the social networking site. DeepText can analyze the context, intent, and source of words to differentiate spam from real content and hate speech from harmless comments. A DeepText spam filter launched on Instagram last October.


JasonKessler/scattertext

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The following code creates a stand-alone HTML file that analyzes words used by Democrats and Republicans in the 2012 party conventions, and outputs some notable term associations. To look for differences in parties, set the category_col parameter to'party', and use the speeches, present in the text column, as the texts to analyze by setting the text col parameter. In order to visualize Empath (Fast 2016) topics and categories instead of terms, we'll need to create a Corpus of extracted topics and categories rather than unigrams and bigrams. Scattertext can also be used to visualize topic models, analyze how word vectors and categories interact, and understand document classification models.


How To Solve The Double Intent Issue For Chatbots – Chatbots Magazine

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After conducting research and trying all the major bot development platforms, I realized the need for a long and intensive training to provide accurate answers to users' requests. For example, in the sentence "I want a pepperoni pizza," most chatbot frameworks -- after being properly configured and trained -- would detect "order food" as the intent, and "pepperoni pizza" as the "food type" entity. This is usually a design limitation, because intent detection is typically handled as a text classification problem, and text classification models are designed to output a single class for a given text. To avoid anyone building their bot, only to add extensive intent detection rules to answer any double intent request, the linguistic information provided by a Deep Linguistic Platform comes up as a solution.


Classification with scikit-learn

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For python programmers, scikit-learn is one of the best libraries to build Machine Learning applications with. Besides supervised machine learning (classification and regression), it can also be used for clustering, dimensionality reduction, feature extraction and engineering, and pre-processing the data. The glass dataset contains data on six types of glass (from building windows, containers, tableware, headlamps, etc) and each type of glass can be identified by the content of several minerals (for example Na, Fe, K, etc). The second dataset contains non-numerical data and we will need an additional step where we encode the categorical data to numerical data.


Email Spam Filtering : A python implementation with scikit-learn

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This article was written by ML bot2 on Machine Learning in Action. Automation of a number of applications like sentiment analysis, document classification, topic classification, text summarization, machine translation, etc has been done using machine learning models. Spam filtering is a beginner's example of document classification task which involves classifying an email as spam or non-spam (a.k.a. I have extracted equal number of spam and non-spam emails from Ling-spam corpus.


The Best Metric to Measure Accuracy of Classification Models

@machinelearnbot

To understand the implication of translating the probability number, let's understand few basic concepts relating to evaluating a classification model with the help of an example given below. Since we are now comfortable with the interpretation of the Confusion Matrix, let's look at some popular metrics used for testing the classification models: Since the formula doesn't contain FP and TN, Sensitivity may give you a biased result, especially for imbalanced classes. In the example of Fraud detection, it gives you the percentage of Correctly Predicted Frauds from the pool of Actual Frauds. In the example of Fraud detection, it gives you the percentage of Correctly Predicted Frauds from the pool of Total Predicted Frauds.


Document Classification with scikit-learn

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We're going to start with raw, labeled emails, and end with a working, reasonable accurate spam filter. We'll instantiate a CountVectorizer and then call its instance method fit_transform, which does two things: it learns the vocabulary of the corpus and extracts word count features. We hold out the smaller portion (the cross-validation set), train the classifier on the larger part, predict on the cross-validation set, and compare the predictions to the examples' already-known classes. Each pair contains a list of indices to select a training subset of the data and a list of indices to select a validation subset of the data.


Text Analysis 101: Document Classification

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Document classification is an example of Machine Learning (ML) in the form of Natural Language Processing (NLP). By classifying text, we are aiming to assign one or more classes or categories to a document, making it easier to manage and sort.