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Future of NLP: The Future & Scope of Natural Language Processing – AskSid

The Future of Natural Language Processing

Understanding Natural Language Processing (NLP)

Natural Language Processing, or NLP, is a subset of AI that enables computers to converse with humans. This involves using AI to ‘understand’ human text or speech – comprehend the meaning, context, requirement, etc., and then deliver a response in text or speech that satisfies the user.

NLP achieves this by combining computational linguistics with machine learning, statistical, and deep learning models. NLP gives the system the ability to fully determine the writer or speaker’s intent, context, and sentiment.

The simplest example of NLP in action is Siri and Google Assistant. Both voice bots accept human speech, comprehend what is being said, and respond in speech. The underlying technology that allows the app to understand what a human says and create a satisfactory response is NLP.

NLP is not just for conversational bots. In essence, any application that can accept text or speech from a human to execute a task is using NLP. A car’s dashboard that recognizes human speech to activate maps, for example, is using NLP. A text-to-speech converter or speech-to-text dictation app uses NLP. Chatbots use NLP. With the line between digital and physical blurring, the future of NLP is bright.

Natural Language Processing

How does NLP work?

For a machine to understand human text or speech is a complicated task. Human conversation is not just about the words, it involves understanding the context, emotion, grammar, sarcasm, idioms, lingo, etc. That is why it has been incredibly difficult to create software that fully understands human speech or text until NLP was created.

Advancements in artificial intelligence gave rise to a subset, NLP, which uses a series of different tasks to understand human speech and text:

Speech recognition – Involves converting human speech into text or machine language. Any application that allows voice commands needs speech recognition.

Grammatical tagging – Also called part of speech tagging, is the process of tagging words or pieces of text in speech based on the context. For example, grammatical tagging identifies the word ‘load’ as a verb in ‘they will load the truck’ and as a noun in ‘he lifted the load onto his shoulders’.

Tasks that NLP can complete

Word sense disambiguation – Involves determining the meaning of a word (especially when the same word can have multiple meanings) based on the context it is used in. NLP achieves this through semantic analysis. For example, the word load in ‘they will load the truck’ means to carry or fill, and in the sentence ‘he lifted the load onto his shoulders,’ it means a heavy or bulky object. Word sense disambiguation identifies this difference.

Named entity recognition – Identifies and extracts named entities present in unstructured text and classifies them into pre-defined categories. This involves recognizing person names, locations, organizations, time and dates, quantities, currencies, percentages, etc. 

Co-reference resolution – Involves identifying when two or more words refer to the same entity. For example, in the sentence ‘Mary had a puppy who she loved’, the words ‘Mary’ and ‘she’ refer to the same entity and co-reference resolution identifies such words and corresponding entities.

Sentiment analysis – Involves the identification, extraction, and quantification of sentiments or emotional cues present in the data. Words like ‘happy’, ‘sad’, and ‘angry’, for example, indicate emotions and are tagged.

Natural language generation – After understanding what the human user is saying, natural language generation computes and delivers a response by putting structured information into human language.

Current & Future Uses of NLP

Spam detection: Spam detection is one of the most common and often underappreciated NLP solutions. Spam detection applications use NLP’s text classification capabilities to scan the text in emails and look for text that indicates spam, threats, phishing attempts, etc. Identifying could be as simple as looking for trigger words that are threatening, promising exorbitant financial gain, inappropriate urgency, etc., or can be as complex as understanding the context of a seemingly harmless email.

Language translation: Language translators, like Google Translate, use NLP technology to accurately convert text from one language to another. Because human language is complex – for example, one word can have more than one meaning depending on the context, translating words one-to-one from one language to another does not work. The positioning of words also changes from one language to another, and this is yet another reason why simply translating words one-to-one does not work. A commonly cited example of bad translation is that before NLP was refined, translating “The spirit is willing but the flesh is weak” from English to Russian and then back from Russian to English yielded “The vodka is good but the meat is rotten.” The translation software needs to understand the meaning of the sentence or paragraph being converted and then translated, and this is where NLP comes into play.

Present and Future of the NLP

Voice assistants and chatbots: Voice assistants like Siri and Alexa, and chatbots like Tidio use NLP to convert human speech and text into machine language and the response back into human speech and text. Voice assistants and chatbots need to understand the statement being made before a response can be computed, and by using features of NLP like NER, sentiment analysis, and grammatical tagging, the app achieves this. Once it has computed a response, the app uses natural language generation in order to respond in speech or text.

Sentiment analysis: Sentiment analysis applications are important components of a business’s brand management arsenal and help in brand reputation management. At the core of every sentiment analysis tool is NLP. The sentiment analysis tool uses NLP to analyze text such as social media comments, descriptions, blog posts, reviews, etc., to generate insights on how people across the internet perceive the brand, what their feelings are towards the company, and what is the company’s reputation among its audience.

Text summarization: Text summarization is a feature of NLP that compresses long-form text like blogs and articles into succinct summaries. An important aspect of compressing any long-form article is maintaining its meaning and message intact. This requires first comprehending the message that the author is trying to deliver, and NLP enables text summarizes to achieve this. Using methods of NLP like extraction-based and abstraction-based summarization, the application analyzes the original text to deliver a shortened summary. This has immense applications in financial research, legal contract analysis, document workflows, generating newsletters, etc.

Top 10 NLP trends and predictions of 2022

Conclusion

NLP has found applications even in technology that is not customer or human-facing like voice assistants and chatbots. NLP is also used to make sense of unstructured data and is one of the largest fields of data science. As AI grows and as the need for applications and humans to inter-communicate increases, NLP will find more and more applications in everyday technology.

About AskSid

AskSid is a global conversational solutions company that partners with retail brands to elevate shopping experiences by leveraging the power of Artificial Intelligence. Our capabilities extend beyond support automation, extending to insight extraction, analysis, and business opportunity generation, all by leveraging the power of conversational data. We combine domain expertise and cutting-edge technology to build a Retail AI Brain for our clients, that mines insights from conversational data and becomes the basis of the business opportunity creation we offer. The power of actionable customer data has allowed us to create value and bring change to global brands including AkzoNobel, Danone, Wolford, Akris, and Himalaya. AskSid has live implementations in 23+ countries and supports 100+ international languages, managing millions of satisfied customers the world over.