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
In this tutorial, we will cover Natural Language Processing for Text Classification with NLTK & Scikit-learn. Remember the last Natural Language Processing project we did? We will be using all that information to create a Spam filter. This tutorial will also cover Feature Engineering and ensemble NLP in text classification. This project will use Jupiter Notebook running Python 2.7.
To test the effectiveness of process discovery algorithms, a Process Discovery Contest (PDC) has been set up. This PDC uses a classification approach to measure this effectiveness: The better the discovered model can classify whether or not a new trace conforms to the event log, the better the discovery algorithm is supposed to be. Unfortunately, even the state-of-the-art fully-automated discovery algorithms score poorly on this classification. Even the best of these algorithms, the Inductive Miner, scored only 147 correct classified traces out of 200 traces on the PDC of 2017. This paper introduces the rule-based log skeleton model, which is closely related to the Declare constraint model, together with a way to classify traces using this model. This classification using log skeletons is shown to score better on the PDC of 2017 than state-of-the-art discovery algorithms: 194 out of 200. As a result, one can argue that the fully-automated algorithm to construct (or: discover) a log skeleton from an event log outperforms existing state-of-the-art fully-automated discovery algorithms.
Text classification has become indispensable due to the rapid increase of text in digital form. Over the past three decades, efforts have been made to approach this task using various learning algorithms and statistical models based on bag-of-words (BOW) features. Despite its simple implementation, BOW features lack semantic meaning representation. To solve this problem, neural networks started to be employed to learn word vectors, such as the word2vec. Word2vec embeds word semantic structure into vectors, where the angle between vectors indicates the meaningful similarity between words. To measure the similarity between texts, we propose the novel concept of word subspace, which can represent the intrinsic variability of features in a set of word vectors. Through this concept, it is possible to model text from word vectors while holding semantic information. To incorporate the word frequency directly in the subspace model, we further extend the word subspace to the term-frequency (TF) weighted word subspace. Based on these new concepts, text classification can be performed under the mutual subspace method (MSM) framework. The validity of our modeling is shown through experiments on the Reuters text database, comparing the results to various state-of-art algorithms.
I am trying to perform multiclass text classification (for 24 classes) on a set documents, but I have a very small dataset currently (1200 total examples). The data collection process is a bit tedious in my case, hence the small dataset size. The best result I have achieved till now is 58% accuracy with an SVM model and a single layer CNN model. Is there any other approach I can try other than collecting more data? I have tried oversampling the training set, but it didn't seem to improve the performance.
Note: This post was written together with the awesome Julian Eisenschlos and was originally published on the TensorFlow blog. Throughout this post we will show you how to classify text using Estimators in TensorFlow. Welcome to Part 4 of a blog series that introduces TensorFlow Datasets and Estimators. You don't need to read all of the previous material, but take a look if you want to refresh any of the following concepts. Part 1 focused on pre-made Estimators, Part 2 discussed feature columns, and Part 3 how to create custom Estimators.
To augment that approach, we've found that we can use machine learning to improve the semantic data models as the data set evolves. Our specific use-case is text data in millions of documents. We've found that machine learning facilitates the storage and exploration of data that would otherwise be too vast to support valuable insights. Machine Learning (ML) allows for a model to improve over time given new training data, without requiring more human effort. For example, a common text-classification benchmark task is to train a model on messages for multiple discussion board threads and then later use it to predict what the topic of discussion was (space, computers, religion, etc).
This course will give you a fundamental understanding of Machine Learning overall with a focus on building classification models. Basic ML concepts of ML are explained, including Supervised and Unsupervised Learning; Regression and Classification; and Overfitting. There are 3 lab sections which focus on building classification models using Support Vector Machines, Decision Trees and Random Forests using real data sets. The implementation will be performed using the scikit-learn library for Python. The Intro to ML Classification Models course is meant for developers or data scientists (or anybody else) who knows basic Python programming and wishes to learn about Machine Learning, with a focus on solving the problem of classification.
Today the data science community is still lacking good practices for organizing their projects and effectively collaborating. ML algorithms and methods are no longer simple "tribal knowledge" but are still difficult to implement, manage and reuse. To address the reproducibility we have build Data Version Control or DVC. This example shows you how to solve a text classification problem using the DVC tool. Git branches should beautifully reflect the non-linear structure common to the ML process, where each hypotheses can be presented as a Git branch. However, inability to store data in a repository and the discrepancy between code and data make it extremely difficult to manage a data science project with Git.
Machine learning generates a lot of buzz because it's applicable across such a wide variety of use cases. That's because machine learning is actually a set of many different methods that are each uniquely suited to answering diverse questions about a business. To better understand machine learning algorithms, it's helpful to separate them into groups based on how they work.
Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review. The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review. In contrast to the existing text moderation service that flags profanity terms, the text classification feature helps detect potentially undesired content that may be deemed as inappropriate depending on context. In addition, to convey the likelihood of each category it may recommend a human review of the content. The text classification feature is in preview and supports the English language.