What are the most valuable NLP applications in business?

Desenvolvemos na minha empresa um assistente virtual com inteligência artificial que atende a toda popula??o do Estado do Paraná, buscando servi?os e oferecendo informa??es úteis aos usuários. Realmente os chatbots desempenham um papel significativo na transforma??o digital.

An insightful overview of NLP's diverse applications in business. Your points on chatbots and sentiment analysis are particularly well-articulated. However, a more nuanced approach could involve discussing the interpretability and ethical considerations inherent in NLP solutions. For instance, how can businesses ensure unbiased and transparent NLP models, given that they often operate as 'black boxes'? Addressing these complexities would add a layer of depth to the article and guide business leaders in more responsible NLP adoption.

I believe that due to current advancement in Large Langauge Models (GPT, llama, mistral models) and their wide applications such as text generation, code generation with satisfactory accuracy is giving new life back to chat-bots and personal assistants. Due to their resemblance to responses from the human mind but with better data, the ability to hold conversation yields good ROI for utilising these chat-bots for both internal organization level and enterprises level for customers for knowledge sharing support at cost efficient basis. Also integration of these LLMs with other Text-To-Speech and Speech-To-Text models is surely pushing us more towards Artificial General Intelligence which is not far from reality now.

Chatbots and virtual assistants have become integral to modern services, catering to customers who seek rapid and straightforward access to information. By allowing users to inquire directly, these tools not only retrieve the desired information but can also facilitate actions, such as booking travel plans or purchasing specific products. Moreover, they can offer recommendations tailored to a customer's needs and preferences, enhancing the overall user experience.

Abstractive Text Summarization is very much helpful and appropriate for the business context utilizing neural networks. This is because, nowadays, people are more focused only reading the headline due to priority and time. Summarization is the task of producing shorter version of one or more or several documents that preserves most of the meaning of the input data. However, sometimes this problem can be related to sentence compression. Sentence compression produces a shorter sentence by removing redundant information, preserving the grammatically correct and the important content of the original sentence.

Here are two examples where Sentiment analysis made a huge impact: 1: A major airline company used sentiment analysis to monitor customer feedback on social media. They found that a significant number of customers were complaining about the long wait times for customer service. The company improved their customer service response time. It resulted in 25% increase in positive sentiment. 2: A popular fast-food chain monitored customer feedback on review platforms. They found that many customers were complaining about the quality of their food and service. The improved their food preparation process & trained their staff. As a result, they saw a 15% increase in positive sentiment and a 10% increase in foot traffic to their restaurants.

Sentiment analysis involves identifying and extracting emotions, opinions, and attitudes from text or speech data, enabling insights into how individuals perceive brands, products, services, or events. It aids in enhancing marketing strategies, customer service, product development, and reputation management. For instance, it can be used to monitor customer feedback on various platforms like social media and surveys, as well as to gauge market and industry sentiment, helping businesses make informed decisions and improve their overall performance.

Sentiment analysis has a huge application in marketing and improving quality of services by gauging customer/user sentiment and feelings after a service or a product usage from their reviews or feedback or comments on social media. The sentiment analysis can be combined with Named Entity Recognition to gauge the impact of a news or a change or a business decision on audience/users. Further to this, based on sentiment analyzed , another model can be used to predict rise or fall in stock prices. The sentiment analysis can also be utilized to improve marketing practices and have targeted marketing campaigns.

Most companies today ask for 'aspect based' sentiment analysis. Most reviews are neither positive nor negative. " A good phone packed with a glazing display and an awful battery" Models need to pick more information from reviews. Aspect being talked about and corresponding sentiment is the next big thing business needs.

Sentiment analysis extracts emotions, opinions, and attitudes from text or speech. It helps businesses understand customer perception, improve marketing, enhance customer service, drive product development, and manage reputation. By monitoring feedback on social media, reviews, and surveys, sentiment analysis provides real-time insights. Analyzing sentiments in the market, media, or industry informs strategic decision-making. It empowers businesses to make data-driven choices, boost customer satisfaction, and stay competitive.

Text summarization is the technique of condensing lengthy text into a brief and precise summary, aimed at extracting crucial information efficiently. It serves to save time, improve comprehension, and aid in decision-making. Text summarization finds applications in generating concise summaries for various content types such as news articles, research papers, legal documents, and reports, as well as in creating headlines, captions, and abstracts, offering valuable assistance across a range of fields and tasks.

Text summarization is the process of condensing a lengthy piece of text into a shorter version while retaining its key information and meaning. It can be done through extractive summarization, where the summary consists of selected sentences or phrases from the original text, or abstractive summarization, which involves generating new sentences that capture the essence of the text. Text summarization is widely used in various applications, including news aggregation, document summarization, and content extraction, to save time and provide concise, informative summaries of large volumes of text.

Text summarization takes the longer text and converts it into shorter versions, highlighting the main ideas and important details. This saves time for busy professionals by giving them quick overviews of reports, articles, or documents. It's also useful for making large volumes of data more manageable. So, text summarization makes it easier to stay informed and make decisions faster in the business world, which is really valuable.

Text summarization is the art of distillation in the digital realm. Using advanced algorithms, it condenses lengthy passages into concise summaries, extracting the essence without losing meaning. This tool streamlines information consumption, aiding professionals in swift decision-making and ensuring that valuable insights are captured efficiently. It's the efficiency booster, allowing individuals to grasp the core of extensive content without drowning in the details.

Imagine being able to grab the most important info from news articles, lengthy papers, or hefty reports in a snap. It's like having a shortcut to the main point, helping you wrap your head around a load of text without diving into the nitty-gritty. It's not just about summaries; it's your go-to for snappy headlines, catchy captions, or to-the-point abstracts, making it super useful for decision-making or sharing insights without the fluff.

Text generation is extremely helpful for multiple purposes at work. It helps in expanding from a basic topic and brainstorm on possible ideas for blog writing (e.g. 5 interesting topics to write blog on weight reduction). One can start broadly and then narrow down based on iteratively verifying the content. Though this may involve human involvement, it can still drastically reduce the time by at least 80% if used well.

Text generation is a valuable application that makes work easier and more effective. It can create human-like text, like product descriptions, reports, or even chatbot responses. This saves time and can be used in marketing to make personalized messages for customers. It's also used to draft emails, news articles, and social media posts automatically. This helps businesses communicate more efficiently and stay engaged with their audience

Text generation involves generating new textual content based on provided input or context, serving as a tool for creating engaging and diverse text across different applications and audiences. It enhances productivity and creativity, enabling the generation of content like blog posts, social media updates, emails, and stories. Additionally, text generation can be utilized for generating captions, slogans, and titles, offering a versatile approach to content creation.

Text generation in machine learning is a dynamic field that focuses on developing algorithms and models capable of producing human-like text. By leveraging techniques such as recurrent neural networks (RNNs) and transformer models, machine learning algorithms can learn patterns, semantics, and context from vast amounts of training data. These models are trained on a wide range of text sources, including books, articles, and even social media posts, enabling them to generate coherent and contextually relevant text. With the advancement of deep learning models like GPT-3.5, text generation has reached new levels of sophistication, allowing for the creation of natural language responses, creative writing, and automated content generation.

Text generation, powered by NLP, is revolutionizing content creation. It equips businesses with the ability to craft reports, articles, and even code at unprecedented speeds. This isn't about replacing human creativity; it's about augmenting it. By automating routine writing tasks, teams are freed to innovate and personalize, adding unique value where it truly matters. This smart application is not just a tool but a collaborator, helping to shape narratives that resonate deeply with audiences and keep businesses one step ahead in the content game.

Text classification has a huge space in Financial Crimes Industry in Banks, Corporations and other companies. For example: able to correctly classify payment messages as of the many fraud categories or not. Also, in manual testing procedures in corporations there is a lot of text classification use cases in this domain. Another use case is in engagement surveys in corporations.

Indeed, the text classification, along with NER, is a very basic but extremely important NLP task. In CcaaS for example it plays a crucial role in routing a client call/mail/message either to a right automated part of the scenario if possible or to a competent agent in order to respond accurately and effectively to his query. Thus it helps to have a smart interaction with a client, identify quickly the intention of his call/mail/message and provide assistance to him 24/7. Nowadays to cover this matter in the industry Deep Learning Transformer models are considered to be the best as they are capable to “understand” a context and pay attention to a right part of the text, and consequently extract an accurate information.

Text classification involves categorizing or labeling text based on its content or purpose, enabling efficient organization, filtering, and analysis of textual data. It contributes to workflow optimization, performance improvement, and insights discovery. Text classification finds applications in sorting emails, messages, or documents by topic, sentiment, urgency, or type, as well as in detecting spam, fraud, or abuse, providing valuable assistance in various domains and tasks involving large volumes of text data.

Text classification involves categorizing and labeling textual data into categories. The primary goal of text classification is to organize, categorize, and make sense of large volumes of textual data. By applying text classification, companies can extract insights from large amounts of unstructured data, enhancing decision-making, customer interactions, and operational efficiency. Its applications are content categorization, spam detection, customer support ticket routing, and more. In business, it helps automate tasks, streamline information retrieval, and extract valuable insights from textual data, making it an essential tool for managing and understanding unstructured text data.

In insurance, NLP models are used to extract certain indicators from OCRed claim documents. These indicator are invaluable! Help in identifying - "surgery is wrongly prescribed to claimant, physical therapy instead would help"

In my opinion, the most important element in business is the unbreakable relationship between the customer and the company. Therefore, it is a chance for the customer to get detailed information about the company and find answers to their questions, which earns high points for the company's reputation. NLP question answering can also be used to improve the quality of internal business processes. For e.g., NLP can be used to develop tools that can help employees to find the information they need more quickly & easily.

Question answering involves providing a precise response to a natural language question, facilitating access to relevant information from diverse sources and formats. It serves to enhance knowledge, problem-solving, and curiosity satisfaction. Question answering applications range from finding answers to factual, analytical, or personal queries to interacting with databases, documents, and websites, making it a valuable tool for information retrieval and interaction across various contexts and needs.

Building a knowledge repository, indexing a good search engine on it, and with the advent of GenAI automatically identifying possible answers can not only help technical folk get up to speed on a new project, but also support business folk get answers without asking developers to run SQL queries for them

Question answering (QA) is the process of understanding and responding to natural language questions, making them invaluable for various business operations. They enhance customer support by providing quick and accurate responses to inquiries, reduce the burden on human agents in handling repetitive queries, and improve user experiences through chatbots and virtual assistants. In the financial sector, QA is used for data retrieval and analysis, aiding in decision-making and compliance.

The goal of QA is to enable machines to comprehend the meaning of questions and provide accurate and relevant answers. QA systems typically employ a combination of techniques, including information retrieval, natural language understanding, and knowledge representation. They analyze the structure and semantics of questions, search for relevant information in large corpora or knowledge bases, and generate concise and informative responses.

Named entity recognition is a key technique in NLP that drives high amounts of value in industry. On its most basic use-cases it can be used to perform part-of-speech tagging on components like dates, locations, etc. However this can be generalised to identify more complex entities in a flexible manner by learning from labeled data examples. This has led to a broad range of use-cases in various industries such as in the legal sector, which benefited from entity extraction at scale across complex documents, leading to a broad range of innovative technologies in the industry.

Two of the most useful NLP concepts that we were able to leverage in our company are topic modeling and keyword extraction. I can share a use case that our team tackled with emphasis on the "measured" and "real" business value this project brings. Keyword extraction/topic modeling in surveys: Going through free-text comments in surveys in our company takes around 2 weeks. With the help of NLP techniques and a well-designed dashboard for interpretability and actionability, analysis time is reduced to just around a day giving more time for root-cause analysis and action planning. This project proved to me that model outputs aren't enough. The final product has to be interpretable and should be compatible with the workflow of the users.

MACHINE TRANSLATION Machine translation is the process of translating the input text to any given output language. The training of the model is mostly supervised where training data is language pairs of the source and target languages, though unsupervised or semi-supervised learning is also possible and is being explored. I'm surprised machine translation is not added in the list of the top applications of NLP. With the SOTA models, machine translation task has greatly improved compared to the previous times. Nowadays there are a number of multilingual models as well available.

I believe the vision of new operating system evolving from the NLP domain specifically LLM's is going to be the most signification application in near future. Currently, one of the most imperative use cases of NLP is harnessing the functionalities of LLMs in conjunction with plugins, function calling (currently specific to OpenAI), and retrieval-augmented generation [RAG](I'd suggest Azure services for this). These advancements have proven to be exceptionally valuable, particularly in addressing specific domain use cases.

Surprised to not see 'Machine Translation' in the list! The Neural Machine Translation saw a revolution in 2017 when Google released 'Transformer', an architecture fully based on attention mechanism. And since then, machine translation has significantly levelled up. Transformer has been studied and many have created creative variants to improve it's performance by tweaking the architecture. During my Master's Thesis Project, I remember working on models like Quaternion Transformer, Subformer, Bert-fused Transformer, Cycle Transformers etc. Also created my own variant called "Sandwich Transformer" XD Nowadays, no other model is needed as GPT-4 is capable of doing Machine Translation (or any NLP task) with a single sentence prompt!