Natural language processing(NLP)

Natural language processing(NLP)

Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written referred to as natural language. It is a component of artificial intelligence (AI).

NLP has existed for more than 50 years and has roots in the field of linguistics. It has a variety of real-world applications in a number of fields, including medical research, search engines and business intelligence.

Why is NLP important?

Large volumes of textual data

Businesses use massive quantities of unstructured, text-heavy data and need a way to efficiently process it. A lot of the information created online and stored in databases is natural human language, and until recently, businesses could not effectively analyze this data. This is where natural language processing is useful.

The advantage of natural language processing can be seen when considering the following two statements: “Cloud computing insurance should be part of every service-level agreement,” and, “A good SLA ensures an easier night’s sleep – even in the cloud.” If a user relies on natural language processing for search, the program will recognize that cloud computing is an entity, that cloud is an abbreviated form of cloud computing and that SLA is an industry acronym for service-level agreement.

Structuring a highly unstructured data source

Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there hundreds of languages and dialects, but within each language is a unique set of grammar and syntax rules, terms and slang. When we write, we often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter and borrow terms from other languages.

Breaking down the elemental pieces of language

How does NLP work?

Natural language processing includes many different techniques for interpreting human language, ranging from statistical and machine learning methods to rules-based and algorithmic approaches. We need a broad array of approaches because the text- and voice-based data varies widely, as do the practical applications.

Basic NLP tasks include tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection and identification of semantic relationships. If you ever diagramed sentences in grade school, you’ve done these tasks manually before.

In general terms, NLP tasks break down language into shorter, elemental pieces, try to understand relationships between the pieces and explore how the pieces work together to create meaning.

NLP capabilities

These underlying tasks are often used in higher-level NLP capabilities, such as:

• Content categorization. A linguistic-based document summary, including search and indexing, content alerts and duplication detection.
• Large Language Model (LLM)-based classification. BERT-based classification is used to capture the context and meaning of words in a text to improve accuracy compared to traditional models.
• Corpus Analysis. Understand corpus and document structure through output statistics for tasks such as sampling effectively, preparing data as input for further models and strategizing modeling approaches.
• Contextual extraction. Automatically pull structured information from text-based sources.
• Sentiment analysis. Sentiment analysis attempts to extract subjective qualities—attitudes, emotions, sarcasm, confusion, suspicion—from text.
• Speech-to-text and text-to-speech conversion. Transforming voice commands into written text, and vice versa.
• Document summarization.Automatically generating synopses of large bodies of text and detect represented languages in multi-lingual corpora (documents).
• Machine translation. Automatic translation of text or speech from one language to another.