"A text classifier is an automated means of determining some metadata about a document. Text classifiers are used for such diverse needs as spam filtering, suggesting categories for indexing a document created in a content management system, or automatically sorting help desk requests."
– John Graham-Cumming, Naive Bayesian Text Classification. Dr. Dobb's. May 1 2005.
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.
What are the advantages of different classification algorithms? For instance, if we have large training data set with approx more than 10,000 instances and more than 100,000 features, then which classifier will be best to choose for classification? The good news though is, that as many problems in life, you can address this question by following the Occam's Razor principle: use the least complicated algorithm that can address your needs and only go for something more complicated if strictly necessary. To read the full article (posted as a Quora question, including 22 answers), click here.
To address this need, the Facebook AI Research (FAIR) lab is open-sourcing fastText, a library designed to help build scalable solutions for text representation and classification. These different concepts are being used for two different tasks: efficient text classification and learning word vector representations. In fastText we also use vectors to represent word ngrams to take into account local word order, which is important for many text classification problems. We hope the introduction of fastText helps the community build better, more scalable solutions for text representation and classification.
There are lots of great tools out there for building machine learning models and data processing pipelines. To this end, we are pleased to showcase an end-to-end model construction process in Microsoft's Azure Machine Learning Studio. Whether you have created an account or not, you can view the page for our uploaded experiment (AML's term for a data pipeline and model) in the Cortana Intelligence Gallery. If you wind up with something you want to use to actually generate predictions on new data, just click the button labeled "Set up Web Service" and watch your experiment being transformed into a predictive model that you can deploy and put to use.