Machine Learning and Natural Language Processing are two significant subfields of Artificial Intelligence that have recently gained importance. Machine Learning and Natural Language Processing are critical in transforming an artificial agent into an artificially intelligent one. Because of advancements in Natural Language Processing, an Artificially Intelligent system can receive better information from the environment and act on it in a more user-friendly manner.
These technologies aid in the analysis of data analytics solutions, the discovery of novel insights, the automation of laborious and time-consuming operations, and the acquisition of competitive advantages for both individuals and companies.
Similarly, by utilizing Machine Learning methods, an Artificially Intelligent System may digest acquired information and produce better predictions for its actions so; how NLP works in machine learning.
Natural Language Processing in Machine Learning
Many processes are involved in processing natural language so the machine can understand it. In some way, machine learning adds significant value to virtually all these activities. Let us strive to figure out how.
The initial step in the morphological analysis is to identify the words and phrases. This process is known as tokenization. Tokenization has been accomplished using various Machine Learning and Deep Learning techniques, including Support Vector Machine and Recurrent Neural Networks.
When the tokenization is finished, the machine has a collection of words and phrases. Affixes are used in most sentences. These affixes complicate matters for devices since having a word-meaning dictionary with all the terms with their possible affixes is challenging.
The next challenge in natural language processing is determining if the provided sentence adheres to a language’s grammatical rules. To do this, each word is first labeled with its part of speech—these aids syntactic parsers in verifying grammatical rules.
Machine learning and deep learning algorithms such as the random forest and the recurrent neural network have been effectively deployed for this job. Machine learning methods such as K- closest neighbor have also been utilized to create syntactic parsers.
Word meanings are identified at this level using word-meaning dictionaries. The issue is that the same word might have several meanings depending on the statement’s context. For example, the term ‘Bank’ might refer to a Blood Bank, a Financial Bank, or even a River Bank / Shore, creating ambiguity. As a result, resolving this ambiguity is an important problem at this level of natural language processing, known as Word Sense Disambiguation.
Word sense disambiguation is a classic classification issue that has been studied with varying degrees of success. Machine learning methods such as random forest, gradient boosting, and decision trees have been used successfully.
There are situations where pronouns are used, or specific subjects/objects are referred to that are not covered by the present analysis preview. In such circumstances, the semantic analysis will not provide accurate meaning to the text. This is another typical reference resolution problem that machine learning and deep learning systems have handled.
Often, phrases express a deeper meaning than the words themselves can describe. After semantic analysis, the machine must eliminate the understood word meaning and capture the intended or inferred purpose. It is simpler stated than done. For many years, scholars have been fascinated by the natural language process. Sarcasm identification is a typical example of pragmatic analysis.
Natural language processing and machine learning systems are less expensive and more effective than using competent manual labor once they have been effectively applied.
Businesses may provide quicker reaction times and more responsive customer support thanks to natural language processing. Customers and potential prospects will get prompt responses to their questions at any time of day.
For qualified developers, pre-trained machine learning algorithms are accessible to simplify various natural language processing applications, making their implementation simple. SG Analytics uses contextual intelligence solutions to build and improve our core NLP features.