6 Real-World Examples of Natural Language Processing

Improving the Performance of NLP Systems on the Gender-Neutral They

nlp examples

NER can be implemented through both nltk and spacy`.I will walk you through both the methods. NER is the technique of identifying named entities in the text corpus and assigning them pre-defined categories such as ‘ person names’ , ‘ locations’ ,’organizations’,etc.. In spacy, you can access the head word of every token through token.head.text. For better understanding of dependencies, you can use displacy function from spacy on our doc object.

nlp examples

Then, let’s suppose there are four descriptions available in our database. Chunking means to extract meaningful phrases from unstructured text. By tokenizing a book into words, it’s sometimes hard to infer meaningful information. Chunking takes PoS tags as input and provides chunks as output. Chunking literally means a group of words, which breaks simple text into phrases that are more meaningful than individual words. NLP is special in that it has the capability to make sense of these reams of unstructured information.

Challenges with NLP

Starbucks also uses natural language processing for opinion analysis to keep track of consumer comments on social media. It assesses public opinion of its goods and services and offers data that can be used to boost customer happiness and promote development. Financial services company American Express utilizes NLP to spot fraud. The system examines multiple text data types to find patterns suggestive of fraud, such as transaction records and consumer complaints.

These models (the clue is in the name) are trained on huge amounts of data. And this has upped customer expectations of the conversational experience they want to have with support bots. These are the most common natural language processing examples that you are likely to encounter in your day to day and the most useful for your customer service teams.

Natural language processing

The list of keywords is passed as input to the Counter,it returns a dictionary of keywords and their frequencies. The above code iterates through every token and stored the tokens that are NOUN,PROPER NOUN, VERB, ADJECTIVE in keywords_list. As you can see, as the length or size of text data increases, it is difficult to analyse frequency of all tokens. So, you can print the n most common tokens using most_common function of Counter.

Read more about https://www.metadialog.com/ here.

Tags: No tags

Add a Comment

Your email address will not be published. Required fields are marked *