Classification, or categorization, is the task of assigning labels to various items, such as products. Classification happens effortlessly in humans' everyday lives. Imagine, for example, that you are going to the grocery store. There, you will implicitly assign labels to the products such as "healthy" versus "not healthy", "GMO" versus "non GMO", or "fresh" versus "stale".
Text classification is the task of assigning labels to text documents. Documents can be webpages, emails, advertisements, or even product reviews. Here are some examples of text classification: categorizing a web page as "English language" versus "Chinese language" versus "other language", an email as "spam" versus "not spam", or a product review as "positive" versus "negative". Since the number of the existing documents is already huge and it is growing rapidly every day, it is impossible to ask humans to manually classify every document. As a result, we need techniques that can automatically assign labels to text.
In order to better understand how text classifiers work, let’s think about how people classify items. Back to the grocery store example, imagine that we want to classify an item as "healthy" versus "not healthy". In order to make the decision, we will first identify a set of item features that is important for the classification, (e.g., percentage of sugar, fat, salt). Then, we will extract those features from the actual product (e.g., 10% sugar, 1% of fat, and 0.1% of salt). Finally, we will combine the values of the features in some way and if they exceed a specific threshold we will classify the product as "healthy" or "not healthy".
A spam detection classifier that labels an email as "spam" or "not spam" works in a similar way: it accepts as input a set of emails, which have already been labeled as "spam" or "not spam", and extracts features, such as the domain of the sender (e.g., the country), or the appearance of links and images in the email. Text classification also benefits from features such as the words or phrases in the email. For example, spam emails usually contain phrases such as "free i-phone" or "your credit card info is needed". The classifier uses the documents with the known labels and learns a model that, given the values of the features, can classify new incoming emails as "spam" or "not spam".
Although there is a large body of research in the text classification domain, there is a growing need for developing new text classification models that are able to successfully distinguish the label of documents. This is due to reasons such as the arrival of the social networks, the evolution of the writing style, or the appearance of new formats of textual information (e.g., emoji). Recent research has witnessed much success in automatic text classification due to the advent of deep learning, but the problem continues to be a challenging one in the Artificial Intelligence community.
- Pigi Kouki
How to achieve classfication in tensorflow? Classification is the process of determining/predicting the class of given data points by using the existing points labeling. It comes under the category of supervised learning in which the model learns from the given data points and then uses this learning to classify new observations, this data can be bi-class(spam or not spam) or multi-class(Grade A, Grade B, Grade C or Grade D). A classification problem is when the output variable is a category, such as "green" or "red" and "spam" or "not spam". There are various applications in classification in many domains such as in Medical diagnosis, Grading system, Scores predication, etc.
We assess whether using six smoothing algorithms (moving average, exponential smoothing, Gaussian filter, Savitzky-Golay filter, Fourier approximation and a recursive median sieve) could be automatically applied to time series classification problems as a preprocessing step to improve the performance of three benchmark classifiers (1-Nearest Neighbour with Euclidean and Dynamic Time Warping distances, and Rotation Forest). We found no significant improvement over unsmoothed data even when we set the smoothing parameter through cross validation. We are not claiming smoothing has no worth. It has an important role in exploratory analysis and helps with specific classification problems where domain knowledge can be exploited. What we observe is that the automatic application does not help and that we cannot explain the improvement of other time series classification algorithms over the baseline classifiers simply as a function of the absence of smoothing.
Text classification is a challenging problem which aims to identify the category of texts. Recently, Capsule Networks (CapsNets) are proposed for image classification. It has been shown that CapsNets have several advantages over Convolutional Neural Networks (CNNs), while, their validity in the domain of text has less been explored. An effective method named deep compositional code learning has been proposed lately. This method can save many parameters about word embeddings without any significant sacrifices in performance. In this paper, we introduce the Compositional Coding (CC) mechanism between capsules, and we propose a new routing algorithm, which is based on k-means clustering theory. Experiments conducted on eight challenging text classification datasets show the proposed method achieves competitive accuracy compared to the state-of-the-art approach with significantly fewer parameters.
We define a new method to estimate centroid for text classification based on the symmetric KL-divergence between the distribution of words in training documents and their class centroids. Experiments on several standard data sets indicate that the new method achieves substantial improvements over the traditional classifiers.
This paper introduces PyDCI, a new implementation of Distributional Correspondence Indexing (DCI) written in Python. DCI is a transfer learning method for cross-domain and cross-lingual text classification for which we had provided an implementation (here called JaDCI) built on top of JaTeCS, a Java framework for text classification. PyDCI is a stand-alone version of DCI that exploits scikit-learn and the SciPy stack. We here report on new experiments that we have carried out in order to test PyDCI, and in which we use as baselines new high-performing methods that have appeared after DCI was originally proposed. These experiments show that, thanks to a few subtle ways in which we have improved DCI, PyDCI outperforms both JaDCI and the above-mentioned high-performing methods, and delivers the best known results on the two popular benchmarks on which we had tested DCI, i.e., MultiDomainSentiment (a.k.a. MDS -- for cross-domain adaptation) and Webis-CLS-10 (for cross-lingual adaptation). PyDCI, together with the code allowing to replicate our experiments, is available at https://github.com/AlexMoreo/pydci .
Machine Learning has become very famous currently which assist in identifying the patterns from the raw data. Technological advancement has led to substantial improvement in Machine Learning which, thus helping to improve prediction. Current Machine Learning models are based on Classical Theory, which can be replaced by Quantum Theory to improve the effectiveness of the model. In the previous work, we developed binary classifier inspired by Quantum Detection Theory. In this extended abstract, our main goal is to develop multi-class classifier. We generally use the terminology multinomial classification or multi-class classification when we have a classification problem for classifying observations or instances into one of three or more classes.
In this tutorial, we are going to use the K-Nearest Neighbors (KNN) algorithm to solve a classification problem. Firstly, what exactly do we mean by classification? Classification across a variable means that results are categorised into a particular group. The KNN algorithm is one the most basic, yet most commonly used algorithms for solving classification problems. KNN works by seeking to minimize the distance between the test and training observations, so as to achieve a high classification accuracy.
As we discussed in the previous article, the Top 3 Challenges of Records Management, records management automation is the best way to address these challenges. But what is automation, really? Within these two main categories there are seven types of automation we typically deal with in the records management world. They can use fingerprinting, linguistic analysis, or both as methods of automation. All of them help us to classify content correctly against the file plan, and in some cases, we can build relationships between content for event better classification.
Okay, our model above works but there are still common words and stop words in our model that LIME picks up on. Ideally, we would want to remove them before modeling and keep only relevant words. This we can accomplish by using additional steps and options in our preprocessing function. Important to know is that whatever preprocessing we do with our text corpus, train and test data has to have the same features (i.e. If we were to incorporate all the steps shown below into one function and call it separately on train and test data, we would end up with different words in our dtm and the predict() function won't work any more.
Last week I published a blog post about how easy it is to train image classification models with Keras. What I did not show in that post was how to use the model for making predictions. This, I will do here. But predictions alone are boring, so I'm adding explanations for the predictions using the lime package. I have already written a few blog posts (here, here and here) about LIME and have given talks (here and here) about it, too.