Text Classification

Pathologists must identify precursor lesions as either benign usual ductal hyperplasia (UDH) or malignant ductal carcinoma in situ (DCIS) for diagnosis and treatment of breast biopsies. Currently, computational pathologists identify morphological features from precursor lesions and apply statistical models on those features for lesion type discrimination. Second, existing models often have redundant training data of the morphological features, so active features should be identified and utilized to refine the algorithm input. Using diagnosis predictions from multiple models, we reduced prediction randomness and greatly improved accuracy.

So, I have been working in this field from last 1.5 years. I started as an intern and gradually become the software engineer in ML field. Till this day, I have text classification models in production, which are working really well from the accuracy and latency point of view. I am still not sure about the industrial process of deploying ML models and keeping them updated by analyzing various points.

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).

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 (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.

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.

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.

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.

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.

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.