[et_pb_section fb_built=”1″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ _module_preset=”default” text_text_color=”#E8EFF2″ background_color=”#283147″ custom_padding=”10px|10px|10px|10px|true|true” border_radii=”on|5px|5px|5px|5px” box_shadow_style=”preset3″ global_colors_info=”{}”]<\/p>\n
NLP, or natural language processing, is a field of artificial intelligence that focuses on computer-based methods to identify and extract information from natural human language (spoken and written).<\/p>\n
In other words, NLP is the study of how computers can understand human language.<\/p>\n
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<\/span><\/p>\n If you\u2019ve ever used Siri or Google Search to:<\/span><\/p>\n You\u2019ve already run into natural language processing.<\/span><\/p>\n In plain English, it is the ability for machines to understand and interact with language as it would be spoken naturally by humans.<\/span><\/p>\n It\u2019s one of the hottest new technologies available today and can help computers better understand speech and react accordingly.<\/span><\/p>\n In this post, we\u2019ll look at the most basic terms you\u2019ll need to understand to get started in NLP. We\u2019ll cover the terms, their meanings and give examples for each one. <\/span><\/p>\n And I’ll teach you how to approach NLP, so you can use it for yourself or for your career.<\/span><\/p>\n <\/span><\/p>\n <\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n <\/span><\/p>\n In the past, companies couldn\u2019t process large volumes of unstructured data fast enough to stay competitive. This is why many companies began investing in Natural Language Processing technologies. <\/span><\/p>\n With Natural Language Processing, organizations can now more efficiently process and analyze large amounts of data quickly and consistently.<\/span><\/p>\n Allowing them to gain valuable insights to make better decisions.<\/span><\/p>\n <\/span><\/p>\n <\/span><\/p>\n [\/et_pb_text][et_pb_text ol_item_indent=”35px” _builder_version=”4.16″ _module_preset=”default” ol_font=”Oswald||||||||” ol_text_color=”#7B676A” global_colors_info=”{}”]<\/p>\n <\/span><\/p>\n NLP is what gives computers the ability to understand human language.<\/span><\/p>\n But languages are complex.<\/span><\/p>\n It\u2019s not just about spelling and grammar. Before a proper understanding can be achieved, there are nuances, subtleties, and various elements that need to be considered, such as intent, tone, and context. That\u2019s why multiple algorithms and techniques are used in NLP to interpret linguistic rules.<\/span><\/p>\n Natural language processing is most often a combination of computational linguistics rule-based and deep learning machine algorithms.<\/span><\/p>\n This paves the way for unstructured data to be processed and analyzed so that a computer can understand.\u00a0<\/span><\/p>\n The two main techniques used in NLP to achieve this are:<\/span><\/p>\n <\/p>\n <\/span><\/p>\n Syntactic analysis refers to the process of analyzing a piece of text for syntactic correctness. In other words, it is a way of seeing if the provided text conforms to grammar rules.<\/span><\/p>\n Here are some of the main syntactic techniques used:<\/span><\/p>\n <\/span><\/p>\n Morphological Segmentation<\/strong><\/p>\n Morphological segmentation is the process of breaking a word down into its constituent parts, morphemes.<\/span><\/p>\n <\/span><\/p>\n Tokenization<\/strong><\/p>\n Tokenization is the process of breaking down text into smaller units or tokens. Tokens are a semantically independent combination of morphemes.<\/span><\/p>\n <\/span><\/p>\n Stop Word Removal<\/strong><\/p>\n A stop word is a word that is not meaningful to the text being analyzed. For example, the removal of words like \u201cthe,\u201d \u201ca,\u201d \u201can,\u201d \u201cthe,\u201d and \u201cof\u201d from a text. To ensure words that provide the most value or meaning remain.<\/span><\/p>\n <\/span><\/p>\n Lemmatization and Stemming<\/strong><\/p>\n Lemmatization is the process of analyzing a sentence and dividing it into its lemmas, the smallest meaningful units that can stand on their own in a sentence.<\/span><\/p>\n Stemming cuts off the prefixes or suffixes found in inflected words.<\/span><\/p>\n The goal is to reduce inflectional words into their root forms.<\/span><\/p>\n <\/span><\/p>\n Part of Speech Tagging<\/strong><\/p>\n Part-of-speech tagging is a process of assigning a part-of-speech tag to each word, such as nouns, verbs, and adjectives. This is usually done by examining the surrounding words and context.<\/span><\/p>\n <\/span><\/p>\n <\/span><\/p>\n <\/span><\/p>\n Semantic analysis is the process of identifying the meaning of words, phrases, and sentences.<\/span><\/p>\n It involves extracting information about the semantic context of a word in a text. And can be done on individual words and larger chunks of text.<\/span><\/p>\n \u00a0Here are some of the common semantic analysis techniques:<\/span><\/p>\n Named Entity Recognition (NER)<\/strong><\/p>\n Named Entity Recognition (NER) is the task of identifying words that represent people, organizations, locations, dates, times, monetary values, quantities, percentages, and other named units of information in text.<\/span><\/p>\n Named Entity Recognition can be used for many purposes, including information extraction and question answering.<\/span><\/p>\n <\/span><\/p>\n Word Sense Disambiguation (WSD)<\/strong><\/p>\n Word sense disambiguation is the process of determining the correct meaning of a word in a given context.<\/span><\/p>\n <\/span><\/p>\n <\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n <\/span><\/p>\n Natural language generation is a subset of Natural language processing that automatically produces natural-sounding sentences that mimic real human speech. <\/span><\/p>\n This includes generating text that sounds natural (in other words, it is not robotic).<\/span><\/p>\n <\/span><\/p>\n <\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n <\/span><\/p>\n Natural language understanding (NLU) refers to the process of extracting information about the context, structure, and intent of natural language input, including written and spoken language.<\/span><\/p>\n It is part of the broader field of natural language processing (NLP).<\/span><\/p>\n The primary task of NLU is to identify a meaning of a specific set of words (lexical analysis) and determine the meaning of a phrase or sentence (semantic analysis).<\/span><\/p>\n <\/span><\/p>\n <\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n <\/span><\/p>\n Natural language processing has many benefits. Too many to list in an article, so I\u2019ll just mention a few.<\/span><\/p>\n <\/span><\/p>\n <\/span><\/p>\n One of the most prominent use cases for NLP is to automate tedious manual tasks. In healthcare, for example, NLP can be used to assist in clinical documentation.<\/span><\/span><\/p>\n <\/span><\/p>\n One of the most important uses for NLP is the speed it can provide. You can have a computer look at a document and generate reports based on its findings in mere seconds. This allows for faster turnaround times for business needs.<\/span><\/span><\/p>\n Natural language processing can be much faster when performing tasks such as information extraction, semantic analysis, and question answering than traditional data collection and analysis methods.<\/span><\/p>\n <\/span><\/p>\n Traditional methods of collecting and analyzing data usually require many resources, including money, time, and people.<\/span><\/p>\n Natural language processing can be done by machines with minimal human involvement. This is especially useful in industries with large amounts of data to analyze but not enough time or money to analyze it all.<\/span><\/p>\n <\/span><\/p>\n <\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n <\/span><\/p>\n Although natural language processing technology is advancing rapidly, it\u2019s still very much a work in progress and far from perfect.<\/span><\/p>\n The main challenge of natural language processing is dealing with the ambiguity and variability of natural language. <\/span><\/p>\n Although humans are incredibly adept at using language, they are often unable to provide a clear, unambiguous definition of the concept or item that is being described.\u00a0<\/span><\/p>\n There is also an issue of polysemy. That is, words can have multiple meanings. For example, the term \u201cbank\u201d can refer to a building that serves as a place to keep money or the bank of a river.<\/span><\/p>\n Another issue is homonymy. This is when words sound the same but mean different things. For example, the word \u201cbank\u201d can be used to refer to the process of collecting depositing money, as well as the place where you put your money.<\/span><\/p>\n Furthermore, training a computer to understand context is complicated. It requires an understanding of various domains and types of data and the ability to discern between similar or synonymous concepts.<\/span><\/p>\n <\/span><\/p>\n <\/span><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n <\/span><\/p>\n Let\u2019s go over some examples of NLP in action to show you how it\u2019s being used in the real world.<\/span><\/p>\n Speech recognition is the process of converting spoken words into text.<\/span><\/p>\n This process is often used to convert speech into machine-readable text to be stored, transmitted, or analyzed. It is a vital component in many applications, including mobile phones, dictation, voice search, and automated customer service.<\/span><\/p>\n <\/span><\/p>\n\n
Why is NLP Important?<\/span><\/h2>\n
How Does NLP Work?<\/span><\/h2>\n
\n
Syntactic Analysis<\/span><\/h3>\n
Semantic Analysis<\/span><\/h3>\n
Natural Language Generation (NLG)<\/span><\/h2>\n
Natural Language Understanding (NLU)<\/span><\/h2>\n
Benefits of Natural Language Processing<\/span><\/h2>\n
Automation<\/span><\/span><\/h3>\n
Speed<\/span><\/h3>\n
Efficiency<\/span><\/h3>\n
Challenges of Natural Language Processing<\/span><\/h2>\n
Examples of NLP Applications<\/span><\/h2>\n
\u00a0<\/span><\/h3>\n
Speech Recognition<\/span><\/h3>\n
AI Writing Software<\/h3>\n