/ Natural language processing and data science workflows

Natural language processing and data science workflows

Natural language processing (NLP) is a component of artificial intelligence (AI). It enables a computer program to understand written or spoken human language. NLP relies on machine learning techniques to complete tasks such as translating language or answering questions.

NLP has its origins in the field of linguistics, and it has applications across a variety of fields, from business intelligence to medical research. Search engines even use NLP to improve results for users.

 

 

Natural language understanding and natural language generation

Natural language processing consists of two components: natural language understanding (NLU) and natural language generation (NLG).

In natural language understanding, computers analyze the grammar (syntax) and meaning (semantics) of a sentence to determine its meaning. NLU creates a data structure that clarifies how words and phrases relate to each other. Humans can use context to understand homonyms and homophones, for example, but computers require a combination of analyses to understand the nuances of language.

On the other side of natural language processing, natural language generation enables computers to produce human language text in response to data input. NLG used to operate by machines filling in blanks in pre-written sentences. This made for awkward language, so NLG systems developed tools for producing more natural-sounding language in real time.

 

Why is natural language processing important?

Natural language processing has a myriad of applications across industries. Because NLP converts words into structured data that enable computer analysis, it reveals previously inaccessible insights. This provides solutions to a number of problems.

Sentiment analysis uses natural language processing to determine whether human language contains positive, negative, or neutral opinions. For this reason, it is sometimes called opinion mining. Businesses can use sentiment analysis to monitor social media interactions, track public perceptions of brands, and understand consumer needs.

Chatbots are excellent examples of natural language generation in action. AI enables companies to use chatbots as a critical tool in customer service. Since they are available around the clock, they accelerate response times and reduce the workload for human customer service representatives.

Natural language processing drives speech recognition technology. Virtual assistants, such as Siri and Alexa, rely on NLP for understanding and responding to voice commands. This aspect of NLP is also the foundation of many assistive technologies, such as voice-to-text programs, used by people with disabilities.

Even word processors rely on natural language processing for autocorrect features that identify misspellings. Unlike spell check, autocorrect relies on pre-entered terms rather than a dictionary. This means NLP tools like autocorrect are more customizable.

 

 

How does natural language processing work?

Natural language processing takes unstructured data and changes it into structured data. In other words, it changes human language into numbers that a computer can analyze. Through a process known as entity recognition, a computer identifies named entities and word patterns. The computer then can convert the structured data back into human language as required using natural language generation.

Currently, natural language processing relies on deep learning. This type of AI identifies and applies patterns to improve a program’s understanding. To be effective, deep learning models require large amounts of data to be sufficient for natural language processing algorithms.

Syntactic vs. semantic analysis

The two main techniques used in natural language processing are syntactic analysis and semantic analysis.

Syntactic analysis refers to syntax, the arrangement of words in a sentence. Using grammatical rules, NLP uses syntax to determine meaning from language. NLP identifies parts of speech, such as nouns and verbs, and even the root forms of words. For example, the algorithm can recognize the root of “played” as “play.”

While syntactic analysis focuses on grammatical structures, semantic analysis involves the meaning behind words. Word sense disambiguation derives a word’s meaning from its context. Named entity recognition analyzes words that can be grouped together in categories. Both of these techniques are important in effective natural language generation.

 

 

What should I look for in natural language processing tools?

Natural language processing tools should provide fast, actionable insights and automate redundant functions to free humans to focus on more complicated tasks.

Domo’s BI and analytics tools integrate data science with natural language queries. Users can ask questions about their data in natural language and get an instant response with text bots. This makes data accessible to more parties across an organization to enhance data-driven decision making.

Domo’s platform uses sentiment analysis with Valence Aware Dictionary and sEntiment Reader (VADER). This tool helps users understand and analyze sentiments expressed in social media. Because VADER can understand the sentiment of a text containing emoticons and slang, it is especially helpful for companies monitoring social media. When key themes emerge in text, Domo delivers actionable insights to users.

 

How do different industries use natural language processing?

Because natural language processing has wide applications, a wide range of industries use it to improve their customer experience and gain valuable insights.

Finance

Financial institutions use sentiment analysis to analyze massive amounts of market research. They can leverage these insights to make informed investment decisions for their clients. The insights gained from NLP also help mitigate risk and protect their customers. Cutting-edge NLP technologies identify fraudulent actions, such as money laundering, more effectively than ever before.

Insurance

Similarly, NLP helps insurers identify fraudulent claims, analyzing everything from customer communications to social media profiles to identify indicators of fraud. The program can flag these claims for insurers to further inspect.

NLP helps insurers navigate an increasingly competitive marketplace. Insurers can use text mining to get insight into their competitors’ doings, and they can get a competitive edge in their product development.

Manufacturing

NLP gives manufacturers insight into where they can improve their supply chain by analyzing thousands of shipping documents to identify areas that are lagging. Manufacturers can use this information to make upgrades to their processes. Resulting logistical changes can enhance efficiency.

 

 

How will natural language processing evolve in the future?

There is no doubt that natural language processing will continue to improve BI by making it more insightful and accessible. As technology improves, users will be able to ask questions like, How have gross earnings changed over the last five years?

Instead of simply showing you raw data, a chatbot will be able to respond in a way that is understandable to more than just data scientists. Democratizing data and access to AI-driven tools will lead to more data-driven decisions across an organization.

NLP also will improve answers to queries by taking more types of data into account. As more unstructured data becomes understandable to a machine, it can provide better insights into natural language queries.

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