Machine learning is a subfield of artificial intelligence (AI) concerned with algorithms that allow computers to learn; these algorithms rely heavily on mathematics and statistics. What learning means, in most cases, is that an algorithm is given a set of data and infers information about the properties of the data—and that information allows it to make predictions about other data that it might see in the future. This is possible because almost all nonrandom data contains patterns, and these patterns allow the machine to generalize. In order to generalize, it trains a model with what it determines are the important aspects of the data.
There are many different machine-learning algorithms, all with different strengths and suited to different types of problems. Some, such as decision trees, are transparent, so that an observer can totally understand the reasoning process undertaken by the machine. Others, such as neural networks, are black box, meaning that they produce an answer, but it’s often very difficult to reproduce the reasoning behind it.
Machine learning is not without its weaknesses. The algorithms vary in their ability to generalize over large sets of patterns, and a pattern that is unlike any seen by the algorithm before is quite likely to be misinterpreted. Machine-learning methods can only generalize based on the data that has already been seen, and even then in a very limited manner. Methods can over generalize, or overfit; when any of these circumstances occur machine learning objectives fail.
NLP and machine learning
We can view NLP as “an extension of what machine learning” or “a special kind of machine learning”. Both need to build models using algorithms and datasets in order to be able to process the new data with these already built models.
Machine-learning can provide natural language processing a range of alternative Learning algorithms as well as additional general approaches and methodologies.
NLP also introduces new learning frameworks and techniques such as: information retrieval and extraction, through speech recognition to syntax, semantics and language understanding related tasks. It also presents the theoretical paradigms: learning theoretic, probabilistic and information theoretic, and the relations among them, along with the main algorithmic techniques developed within these and in key natural language applications.