Deep learning is a subfield of machine learning, which helps to decipher the consumer’s intent, words and sentences. NLP is among the fast-growing analysis domains in AI, with purposes that contain tasks including translation, summarization, textual content era, and sentiment evaluation. We convey meaning in many alternative methods, and the identical word or phrase can have a totally completely different that means relying on the context and intent of the speaker or writer.
![natural language example](https://globalcloudteam.com/wp-content/uploads/2021/10/68a562d1-57b3-42d7-8c18-fa772450507d.jpg)
In 2021 OpenAI developed a natural language programming environment for their programming large language model known as Codex. These are the commonest pure language processing examples that you are likely to encounter in your everyday and the most useful on your customer service groups. Natural language processing (NLP) is the method by which computer systems perceive the human language. NLP allows you to carry out a wide range of tasks corresponding to classification, summarization, text-generation, translation and more. Once you have a working knowledge of fields such as Python, AI and machine studying, you can turn your consideration specifically to pure language processing. Let’s begin with a definition of natural language processing.
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Spacy also provies visualization for better understanding. Now, what in case you have huge knowledge, it is going to be inconceivable to print and examine for names. Once the cease natural language example words are eliminated and lemmatization is done ,the tokens we’ve may be analysed additional for details about the text information.
In the above output, you’ll be able to notice that solely 10% of unique textual content is taken as summary. You can change the default parameters of the summarize function based on your necessities. Let us say you have an article about economic junk meals ,for which you wish to do summarization.
MonkeyLearn is a good instance of a software that makes use of NLP and machine learning to investigate survey results. It can kind through giant quantities of unstructured information to provide you insights within seconds. Similarly, assist ticket routing, or ensuring the right query will get to the proper team, may also be automated. This is completed by using NLP to understand what the client needs based mostly on the language they’re using. This is then combined with deep learning expertise to execute the routing.
![natural language example](https://globalcloudteam.com/wp-content/uploads/2020/11/web-developer.webp)
For that, discover the very best frequency using .most_common technique . Then apply normalization formula to the all keyword frequencies within the dictionary. The above code iterates through every token and stored the tokens which might be NOUN,PROPER NOUN, VERB, ADJECTIVE in keywords_list.
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Therefore, Natural Language Processing (NLP) has a non-deterministic method. In different words, Natural Language Processing can be utilized to create a new intelligent system that can understand how people understand and interpret language in several situations. Python is taken into account the most effective programming language for NLP because of their numerous libraries, easy syntax, and ability to easily integrate with different programming languages.
For better understanding of dependencies, you ought to use displacy operate from spacy on our doc object. For better understanding, you must use displacy perform of spacy. You can print the same with the assistance of token.pos_ as proven in under code.
![natural language 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Some are centered directly on the models and their outputs, others on second-order concerns, such as who has access to those techniques, and the way training them impacts the natural world. A word is important if it occurs many occasions in a document. We resolve this concern by using Inverse Document Frequency, which is high if the word is uncommon and low if the word is frequent across the corpus.
The TF-IDF rating of a time period is the product of TF and IDF. NLP is used for all kinds of language-related tasks, including answering questions, classifying text in a variety of ways, and conversing with customers. Now that your mannequin is educated , you presumably can cross a new evaluation string to mannequin.predict() function and check the output.
Lemmatization
In this case, we’re going to use NLTK for Natural Language Processing. We will use it to carry out varied operations on the text. TextBlob is a Python library designed for processing textual knowledge. Gensim is an NLP Python framework usually used in subject modeling and similarity detection.
You can use Counter to get the frequency of every token as shown below. If you present a listing to the Counter it returns a dictionary of all parts with their frequency as values. Natural language processing is a captivating field and one that already brings many advantages to our day-to-day lives. As the technology advances, we are ready to expect to see further purposes of NLP throughout many different industries.
- There are examples of NLP getting used all over the place round you , like chatbots you utilize in an web site, news-summaries you want on-line, positive and neative film reviews and so forth.
- Today most individuals have interacted with NLP in the form of voice-operated GPS techniques, digital assistants, speech-to-text dictation software, customer service chatbots, and different shopper conveniences.
- Companies nowadays have to course of plenty of knowledge and unstructured text.
- You can move the string to .encode() which can converts a string in a sequence of ids, using the tokenizer and vocabulary.
- It makes use of massive amounts of information and tries to derive conclusions from it.
Infuse powerful natural language AI into commercial functions with a containerized library designed to empower IBM companions with larger flexibility. Accelerate the enterprise value of artificial intelligence with a powerful and flexible portfolio of libraries, providers and functions. You have seen the assorted makes use of of NLP techniques in this article. I hope you can now efficiently perform these duties on any actual dataset.
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Also, spacy prints PRON earlier than each pronoun in the sentence. Now that you’ve relatively higher text for analysis, let us have a glance at a number of different textual content preprocessing strategies. As we already established, when performing frequency analysis https://www.globalcloudteam.com/, cease words need to be removed. The process of extracting tokens from a text file/document is referred as tokenization. The uncooked textual content knowledge often referred to as textual content corpus has lots of noise.
![natural language example](https://globalcloudteam.com/wp-content/uploads/2021/02/image-aCLSPMwvAW2Vt9ko.png)
Whether it’s by way of Siri, Alexa, Google Assistant or different related expertise, many of us use these NLP-powered devices. As seen above, “first” and “second” values are important words that help us to distinguish between these two sentences. Named entity recognition can automatically scan entire articles and pull out some fundamental entities like individuals, organizations, places, date, time, money, and GPE discussed in them. Notice that the most used words are punctuation marks and stopwords. We will have to remove such words to investigate the actual textual content.
I shall first stroll you step-by step by way of the process to grasp how the subsequent word of the sentence is generated. After that, you probably can loop over the process to generate as many words as you want. You can notice that within the extractive methodology, the sentences of the abstract are all taken from the unique text. Next , you realize that extractive summarization relies on identifying the numerous words.
![natural language example](https://globalcloudteam.com/wp-content/uploads/2023/01/quality-assurance-vs-quality-control-main-differences.webp)
For instance, we’ve a database of hundreds of dog descriptions, and the person wants to seek for “a cute dog” from our database. The job of our search engine could be to display the closest response to the consumer query. The search engine will presumably use TF-IDF to calculate the rating for all of our descriptions, and the end result with the higher score will be displayed as a response to the person. Now, this is the case when there isn’t any precise match for the user’s query. If there might be an exact match for the person question, then that end result shall be displayed first.