13 Natural Language Processing Examples to Know

Marketers are always looking for ways to analyze customers, and NLP helps them do so through market intelligence. Market intelligence can hunt through unstructured data for patterns that help identify trends that marketers can use to their advantage, including keywords and competitor interactions. Using this information, marketers can help companies refine their marketing approach and make a bigger impact.

For example, businesses can recognize bad sentiment about their brand and implement countermeasures before the issue spreads out of control. Just like any new technology, it is difficult to measure the potential of NLP for good without exploring its uses. Most important of all, you should check how natural language processing examples of natural language comes into play in the everyday lives of people. Here are some of the top examples of using natural language processing in our everyday lives. NLP is one of the fast-growing research domains in AI, with applications that involve tasks including translation, summarization, text generation, and sentiment analysis.

Lexical analysis

In today’s hyperconnected world, our smartphones have become inseparable companions, constantly gathering and transmitting data about our whereabouts and movements. This trove of information, often referred to as mobile traffic data, holds a wealth of insights about human behaviour within cities, offering a unique perspective on urban dynamics and patterns of movement. Many of the unsupported languages are languages with many speakers but non-official status, such as the many spoken varieties of Arabic. Creating a perfect code frame is hard, but thematic analysis software makes the process much easier. Many people don’t know much about this fascinating technology, and yet we all use it daily.

Sentiment analysis is an example of how natural language processing can be used to identify the subjective content of a text. This is naturally very useful for companies that want to monitor social media traffic regarding their brands and competitor brands or key topics, and also to monitor the sentiment of dialogue between users and chatbots or customer support agents. Sentiment analysis has been used in finance to identify emerging trends which can indicate profitable trades. NLP is a subfield of linguistics, computer science, and artificial intelligence that uses 5 NLP processing steps to gain insights from large volumes of text—without needing to process it all. This article discusses the 5 basic NLP steps algorithms follow to understand language and how NLP business applications can improve customer interactions in your organization. Businesses use sentiment analysis to gauge public opinion about their products or services.

Faster Typing using NLP

Figure 5.12 shows the arguments and results for several special functions that we might use to make a semantics for sentences based on logic more compositional. Domain independent semantics generally strive to be compositional, which in practice means that there is a consistent mapping between words and syntactic constituents and well-formed expressions in the semantic language. Most logical frameworks that support compositionality derive their mappings from Richard Montague[19] who first described the idea of using the lambda calculus as a mechanism for representing quantifiers and words that have complements. Subsequent work by others[20], [21] also clarified and promoted this approach among linguists.

• The ‘bag-of-words’ algorithm involves encoding a sentence into numerical vectors suitable for sentiment analysis.
• Uber took advantage of this concept and developed a Facebook Messenger chatbot, thereby creating a new source of revenue for themselves.
• Smart assistants such as Google’s Alexa use voice recognition to understand everyday phrases and inquiries.
• However, as you are most likely to be dealing with humans your technology needs to be speaking the same language as them.

The results are surprisingly personal and enlightening; they’ve even been highlighted by several media outlets. The monolingual based approach is also far more scalable, as Facebook’s models are able to translate from Thai to Lao or Nepali to Assamese as easily as they would translate between those languages and English. As the number of supported languages increases, the number of language pairs would become unmanageable if each language pair had to be developed and maintained. Earlier iterations of machine translation models tended to underperform when not translating to or from English.

Finally, the machine analyzes the components and draws the meaning of the statement by using different algorithms. The rise of human civilization can be attributed to different aspects, including knowledge and innovation. However, it is also important to emphasize the ways in which people all over the world have been sharing knowledge and new ideas.

First introduced by Google, the transformer model displays stronger predictive capabilities and is able to handle longer sentences than RNN and LSTM models. While RNNs must be fed one word at a time to predict the next word, a transformer can process all the words in a sentence simultaneously and remember the context to understand the meanings behind each word. Text suggestions on smartphone keyboards is one common example of Markov chains at work.