Big data technology and artificial intelligence industries are intertwined, and natural processing specifically can be used for automatic classification of documents. Natural processing can recognize patterns within texts, and furthermore classify texts in predefined groups automatically. Natural processing can be combined with machine language advance their features when processing texts, and then applied to systems for accurate classifications.

Machine learning relates to natural processing on a statistical standpoint, with decisions being made based off probability. This is done by giving fragments of text from the input data weights depending on the features of the data. These models combining machine learning and natural processing are trained on this input data, and then can be applied to new and unfamiliar data. They are capable of recognizing spelling errors or missing words from the text. This being so, the model will only be as accurate as the input data; consequently, continually updating the system with new data to further its knowledge and being conscious of the size of the data will determine the accuracy of the system.

Additionally, the proper training of the machine learning model is vital in getting accurate results. Generally, the goal of the training ends with a set of data with correct assignments of classifications. There are many different tools available to create a trained machine learning model, typically open sourcses based on the preferred language of the programmers. For example, OpenNPL, a Java based machine learning toolkit, uses text streams to create a binary model. Moreover, NLTK, a Python based platform, can create single and multi-class classifications. Furthermore, Spark, a large open source community, can also be used with NLP. Spark provides additional features to advance the machine learning model even further. Different software and platforms allow for programmers to access to create well-trained machine learning models.

Topic segmentation and recognition is an application of natural processing. There are two methods, unsupervised and supervised, that are capable of classifying text into different topics. Supervised learning considers known classes and topics, typically the input data already exists, and it becomes the systems reference point to sort new data into categories. Due to the reliance on the input data, the accuracy of a supervised system correlates with the content of the input data and the similarity between the input and new data. On the other hand, unsupervised learning has input data that has no indication of correct classes and topics. Despite its complexity and inaccuracy, unsupervised learning typically ends up being used if the data has more unknown objects than known. Consequently, the model relies on machine learning to accurately predict the classifications of data.

There are many applications to these systems, such as assigning products to proper departments, filter spam, predicting movie genres, and classifying ads. Moreover, by taking advantage of open source platforms, programmers can increase the accuracy of the input data while training the models. Overall, by combining machine language and natural processing, programmers can create a model that can accurately predict classes for new text data.

Do you think natural processing and machine language are the answer to sorting through large data sets? Let me know in the comments down below!