Text Classification is the task of assigning the right label to a given piece of text. This text can either be a phrase, a sentence or even a paragraph. Our aim would be to take in some text as input and attach or assign a label to it. Since we will be using Tensorflow deep learning library, we can call this the Tensorflow text classification system. This task involves training a neural network with lots of data indicating what a piece of text represents.

Posted by Sara Robinson (Developer Advocate), Josh Gordon (Developer Advocate), and Marianne Linhares Monteiro (DA Intern). As humans, our brains can easily read a piece of text and extract the topic, tone, and sentiment. Up until just a few years ago, teaching a computer to do the same thing required extensive machine learning expertise and access to powerful computing resources. Now, frameworks like TensorFlow are helping to simplify the process of building machine learning models, and making it more accessible to developers with no background in ML. In this post, we'll show you how to build a simple model to predict the tag of a Stack Overflow question.

Natural language interactive narratives are a variant of traditional branching storylines where player actions are expressed in natural language rather than by selecting among choices. Previous efforts have handled the richness of natural language input using machine learning technologies for text classification, bootstrapping supervised machine learning approaches with human-in-the-loop data acquisition or by using expected player input as fake training data. This paper explores a third alternative, where unsupervised text classifiers are used to automatically route player input to the most appropriate storyline branch. We describe the Data-driven Interactive Narrative Engine (DINE), a web-based tool for authoring and deploying natural language interactive narratives. To compare the performance of different algorithms for unsupervised text classification, we collected thousands of user inputs from hundreds of crowdsourced participants playing 25 different scenarios, and hand-annotated them to create a gold-standard test set. Through comparative evaluations, we identified an unsupervised algorithm for narrative text classification that approaches the performance of supervised text classification algorithms. We discuss how this technology supports authors in the rapid creation and deployment of interactive narrative experiences, with authorial burdens similar to that of traditional branching storylines.

Elections unleash strong political views on Twitter, but what do people really think about politics? Opinion and trend mining on micro blogs dealing with politics has recently attracted researchers in several fields including Information Retrieval and Machine Learning (ML). Since the performance of ML and Natural Language Processing (NLP) approaches are limited by the amount and quality of data available, one promising alternative for some tasks is the automatic propagation of expert annotations. This paper intends to develop a so-called active learning process for automatically annotating French language tweets that deal with the image (i.e., representation, web reputation) of politicians. Our main focus is on the methodology followed to build an original annotated dataset expressing opinion from two French politicians over time. We therefore review state of the art NLP-based ML algorithms to automatically annotate tweets using a manual initiation step as bootstrap. This paper focuses on key issues about active learning while building a large annotated data set from noise. This will be introduced by human annotators, abundance of data and the label distribution across data and entities. In turn, we show that Twitter characteristics such as the author's name or hashtags can be considered as the bearing point to not only improve automatic systems for Opinion Mining (OM) and Topic Classification but also to reduce noise in human annotations. However, a later thorough analysis shows that reducing noise might induce the loss of crucial information.

Another TensorFlow feature you typically want to use is checkpointing – saving the parameters of your model to restore them later on. Checkpoints can be used to continue training at a later point, or to pick the best parameters setting using early stopping. Checkpoints are created using a Saver object. Before we can train our model we also need to initialize the variables in our graph. The initialize_all_variables function is a convenience function run all of the initializers we've defined for our variables. You can also call the initializer of your variables manually. That's useful if you want to initialize your embeddings with pre-trained values for example. Let's now define a function for a single training step, evaluating the model on a batch of data and updating the model parameters.

We address the problem of multi-class classification in the case where the number of classes is very large. We propose a double sampling strategy on top of a multi-class to binary reduction strategy, which transforms the original multi-class problem into a binary classification problem over pairs of examples. The aim of the sampling strategy is to overcome the curse of long-tailed class distributions exhibited in majority of large-scale multi-class classification problems and to reduce the number of pairs of examples in the expanded data. We show that this strategy does not alter the consistency of the empirical risk minimization principle defined over the double sample reduction. Experiments are carried out on DMOZ and Wikipedia collections with 10,000 to 100,000 classes where we show the efficiency of the proposed approach in terms of training and prediction time, memory consumption, and predictive performance with respect to state-of-the-art approaches.

A great variety of text tasks such as topic or spam identification, user profiling, and sentiment analysis can be posed as a supervised learning problem and tackle using a text classifier. A text classifier consists of several subprocesses, some of them are general enough to be applied to any supervised learning problem, whereas others are specifically designed to tackle a particular task, using complex and computational expensive processes such as lemmatization, syntactic analysis, etc. Contrary to traditional approaches, we propose a minimalistic and wide system able to tackle text classification tasks independent of domain and language, namely microTC. It is composed by some easy to implement text transformations, text representations, and a supervised learning algorithm. These pieces produce a competitive classifier even in the domain of informally written text. We provide a detailed description of microTC along with an extensive experimental comparison with relevant state-of-the-art methods. mircoTC was compared on 30 different datasets. Regarding accuracy, microTC obtained the best performance in 20 datasets while achieves competitive results in the remaining 10. The compared datasets include several problems like topic and polarity classification, spam detection, user profiling and authorship attribution. Furthermore, it is important to state that our approach allows the usage of the technology even without knowledge of machine learning and natural language processing.

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. Most patients with UDH receive no treatment and have minimal or no increased risk of cancer, while patients with DCIS are more likely to be diagnosed with invasive breast cancer1, 2. Treatment to reduce DCIS recurrence and invasive carcinoma has notable risks and side effects, given the extensive methods of lumpectomy with radiation, mastectomy, and tamoxifen hormonal treatment3. Diagnostic oversights can lead to either untreated cancer or unnecessary radiation treatment and chemotherapy, both of which have detrimental consequences. Thus, accurate diagnosis is critical for patients as well as for hospitals to reduce extraneous treatment costs. However, human pathologists may not always be in concordance as there is no strict set of instructions on how to carry out a diagnosis.

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.

They took the NYC Data Science Academy 12-week full-time data science bootcamp program from Sep. 23 to Dec. 18, 2015. The post was based on their fourth in-class project (due after the 8th week of the program). Walmart uses trip type classification to segment its shoppers and their store visits to better improve the shopping experience. Walmart's trip types are created from a combination of existing customer insights and purchase history data. The purpose of the Kaggle competition is to use only the purchase data provided to derive Walmart's classification labels.