Why NLP is the Next Frontier in AI for Enterprises

NLP is data-driven, but which kind of data and how much of it is not an easy question to answer. Scarce and unbalanced, as well as too heterogeneous data often reduce the effectiveness of NLP tools. However, in some areas obtaining more data will either entail more variability (think of adding new documents to a dataset), or is impossible (like getting more resources for low-resource languages). Besides, even if we have the necessary data, to define a problem or a task properly, you need to build datasets and develop evaluation procedures that are appropriate to measure our progress towards concrete goals.

Interviews, surveys, and focus group discussions are conducted regularly to better understand the needs of affected populations. Alongside these “internal” sources of linguistic data, social media data and news media articles also convey information that can be used to monitor and better understand humanitarian crises. People make extensive use of social media platforms like Twitter and Facebook in the context of natural catastrophes and complex political crises, and news media often convey information on crisis-related drivers and events. Human language is filled with ambiguities that make it incredibly difficult to write software that accurately determines the intended meaning of text or voice data. Businesses use massive quantities of unstructured, text-heavy data and need a way to efficiently process it.

Classical Approaches

To ensure a consistent user experience, you need an easy way to push new updates to production and determine which versions are currently in use. The CircleCI platform excels at integrating testing into the development process. Support for automated testing makes it easy to ensure code performs as expected before it goes to production. You can customize tests on the CircleCI platform using one of many third-party integrations called orbs. Testing is crucial in developing any software project and especially for ML-powered programs. By nature of their complexity and training, ML models tend to feature implementation that is opaque to the user, making it near-impossible to determine a model’s correctness by inspection.

• As an example, the know-your-client (KYC) procedure or invoice processing needs someone in a company to go through hundreds of documents to handpick specific information.
• For English, for example, a character tokenization vocabulary would have about 26 characters.
• So TF–IDF is zero for the word “this”, which implies that the word is not very informative as it appears in all documents.
• By defining specific patterns, these algorithms can identify and extract useful information from the given text.Another type of rule-based algorithm in NLP is syntactic parsing, which aims to understand the grammatical structure of sentences.
• Our goal is to
  
  

  help you build intuition and experience working with NLP, chapter by

  
  

  chapter, so that by the end of the book, you’ll be able to

  
  

  build real applications that add real value to the world.

• By contrast, the focus should be on a particle part of the text where the most important information for a specific question is stored.

Here, we summarize NLP, its applications, the challenges it encounters, and, most importantly, how enterprises can leverage it to gain big. Whether you’re a data scientist, a developer, or someone curious about the power of language, our tutorial will provide you with the knowledge and skills you need to take your understanding of NLP to the next level. Natural language processing tasks are deemed more technically diverse when compared to computer vision procedures.

2. Needs assessment and the humanitarian response cycle

Along with computer vision, NLP is now poised to have

many broad-based applications in the enterprise. With this book, we hope

to share some concepts and tools that will help you build some of

these applications at your company. These days companies strive to keep up with the trends in intelligent process automation. OCR and NLP are the technologies that can help businesses win a host of perks ranging from the elimination of manual data entry to compliance with niche-specific requirements. A tax invoice is more complex since it contains tables, headlines, note boxes, italics, numbers – in sum, several fields in which diverse characters make a text.

Sometimes it’s hard even for another human being to parse out what someone means when they say something ambiguous. There may not be a clear concise meaning to be found in a strict analysis of their words. In order to resolve this, an NLP system must be able to seek context to help it understand the phrasing. NLP machine learning can be put to work to analyze massive amounts of text in real time for previously unattainable insights. Synonyms can lead to issues similar to contextual understanding because we use many different words to express the same idea. Furthermore, some of these words may convey exactly the same meaning, while some may be levels of complexity (small, little, tiny, minute) and different people use synonyms to denote slightly different meanings within their personal vocabulary.

Videos and images as user-generated content are quickly becoming mainstream, which in turn means that our technology needs to adapt. Web scraping refers to the practice of fetching and extracting information from web pages, either manually or by automated processes (the former being a lot more common than the latter). Omoju recommended to take inspiration from theories of cognitive science, such as the cognitive development theories by Piaget and Vygotsky. In another course, we’ll discuss how another technique called lemmatization can correct this problem by returning a word to its dictionary form. Next, you might notice that many of the features are very common words–like “the”, “is”, and “in”.

Human language is complex, and constantly evolving, which means natural language processing has quite the challenge. The process of finding all expressions that refer to the same entity in a text is called coreference resolution. It is an important step for a lot of higher-level NLP tasks that involve natural language understanding such as document summarization, question answering, and information extraction. Notoriously difficult for NLP practitioners in the past decades, this problem has seen a revival with the introduction of cutting-edge deep-learning and reinforcement-learning techniques. At present, it is argued that coreference resolution may be instrumental in improving the performances of NLP neural architectures like RNN and LSTM.

Recommenders and Search Tools

HUMSET is an original and comprehensive multilingual collection of humanitarian response documents annotated by humanitarian response professionals through the DEEP platform. The dataset contains approximately 17,000 annotated documents in three languages (English, French, and Spanish) and covers a variety of humanitarian emergencies from 2018 to 2021 related to 46 global operations. Creating large-scale resources and data standards that can scaffold the development of domain-specific NLP models is essential to make many of these goals realistic and possible to achieve.

Read more about https://www.metadialog.com/ here.