Articles & Showcase
A place to read & write articles as well as showcase work being done with the NL API & Edge
- 11 Topics
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Hello and welcome to the expert.ai Community! I'm Fil Emanuele, Head of NL strategy at expert.ai. I'm here to make sure you get the most out of your experience with expert.ai Edge NL API. If you have any questions along the way or need some pointers, please feel free to reach out. I'm excited to get to know you all and give you the support you need to become a Natural Language Understanding expert. I look forward to hearing from you!
Hello and welcome to the expert.ai Community!My name is Luisa Herrmann-Nowosielski and I’m the head of Product Management, and also the person responsible for the NL API. I would love to get to know all our users and interact with you here!Feel free to say hello and introduce yourselves in this topic, and reach out to me if you have any questions!I look forward to hearing from you!
Modern approaches to Natural Language Processing are offering a streamlining of the process of document analysis by way of simplification.Simply put, there’s a tendency to drop the hard stuff (i.e., understanding the content) for more direct techniques like looking at words, how often they appear in documents, what other words show up next to them or somewhere else in the same document; this kind of statistical information is collected and carefully optimized during what is known in Machine Learning as the Training stage. Practically speaking, a person will manually tag a document that talks, for instance, about sports with the label “Sports” (known as the Target), and, when that document is processed, the engine will collect all the words present and mark them as potentially leading to the assumption they indicate a sport context. When more content from the Training Set of documents is analyzed, some of those words will be present again (reinforcing the idea that they truly are indica
Transform speech into knowledge with HuggingFace/Facebook AI and expert.ai Photo by Volodymyr Hryshchenko on Unsplash Over the years I’ve saved tons of podcasts, telling myself I would soon listen to them. This folder has now become an enormous messy heap of audios, and I often don’t even remember what each particular file is about. That’s why I wanted to create a program to analyze audio files and produce a report on their content. I needed something that with a simple click would show me topics, main words, main sentences, etc. To achieve this, I used Facebook AI/Hugging Face Wav2Vec 2.0 model in combination with expert.ai’s NL API. I uploaded the code here, hoping that it would be helpful to others as well. MODELThis solution is broken down in three main steps:Pre-processing stage (extension handling and resampling) Speech to Text conversion Text analysis and report generationFor the first step, I checked many options. Some were very practical (did not require a subscription, and we
Over two hackathons, I have created two Amazon Alexa Skills that integrate with Expert.ai textual analysis APIs. Source code is available on GitHub (see links below). Sentiment ExpertSentiment Expert is an Amazon Alexa skill that uses Expert.ai sentiment analysis to measure sentiment in the user's utterance. Rather than just providing raw measurements, Sentiment Expert challenges the user to match target measurements for positive and negative measurements, with higher scores awarded for proximity to the targets. Sentiment Expert was built as an Alexa voice interaction model (a set of JSON files) with an AWS Lambda back end written in Node.js. The back end code makes calls to the Expert.ai sentiment analysis API. Devpost: https://devpost.com/software/sentiment-expertGitHub repo: https://github.com/srnelson/sentimentexpertAmazon Skills Store: https://www.amazon.com/SN-forward-Typical-Topical/dp/B097BV8C5M/Alexa Quick Link: https://alexa-skills.amazon.com/apis/custom/skills/amzn1.ask.sk
A concrete example of the value for developersIn the following use case in the financial services industry, we’re going to look at how expert.ai NL API compares in practical terms to other popular NLP models. Language powers many strategic and operational activities within banks and financial services organizations. For example, processing and interpreting documents , enterprise search and customer interaction all require domain expertise and accurate understanding of language. With the volume of these contents growing exponentially, leveraging accurate scalable NLU technology that can be implemented across the organization is a critical success factor. Rethinking Investment Research Targeting, collecting and creating quality research continue to be a moving target for financial services organizations of all sizes. The industry has become commoditized, and it is difficult to determine the value of one investment research platform over another. The opportunity to capture valuable, ti
Six months after the launch of our NL API and many subsequent industry- or use case-specific extensions, it would be a timely opportunity to take stock of the situation on how these API can simplify the development of NL based applications, and address questions I received from the broad community of software developers.How to Think About NLP APIsLet’s begin addressing the most common challenge developers with no language processing experience face on how to think in terms of NLP APIs in general. If you have been asked to build software that requires an understanding of text, simply think of this as a design pattern, or real-time inference, in which your NLP API is deployed like a microservice. To illustrate how this works in simple terms, imagine you want to develop an email categorization engine. When an email is delivered to the target account, your application needs to:extract the text and maybe other metadata (e.g. the sender, the level of urgency, etc…); query an NLP API with the
Ciao Experts’ Community!We recently introduced three new extensions to our NL API ecosystem: Emotional, Behavioral traits, and Writeprint detector.I would like to take advantage of this thread to give you a few references to get the most out of these new addictions, driving through our documentation portal.Emotional traits and Behavioral traits are two taxonomies for document classification available for the English language.Writeprint is an information detector available for all five main languages: English, Spanish, French, German, and Italian.The Python SDK, now at version 2.3, has undergone a minor release update to allow the use of the new detector: it’s available on Github and, as usual, on the pypi site.You can try these new features directly on our live demo.As well, specific OpenAPI contracts have been implemented to develop clients able to use the Emotional traits taxonomy to obtain the main groups of emotions and to have the results of the Writeprint detection in the form of
How to resolve ambiguity for homographs and polysemy using expert.ai NLP technology. A series of picture frames of different colours and different dimensions.Photo by Markus Spiske on Unsplash Read the article on Towards Data Science. Ambiguity is one of the biggest challenges in NLP. When trying to understand the meaning of a word we consider several different aspects, such as the context in which it is used, our own knowledge of the world, and how a given word is generally used in society. Words change meaning over time and can also mean one thing in a certain domain and another in a different one. This phenomenon can be observed in homographs — two words that happen to be written in the same way, usually coming from different etymologies — and polysemy — one word that carries different meanings.In this tutorial, we’ll see how to resolve ambiguity in PoS tagging and semantic tagging, using expert.ai technology. Before you start Please check how to install expert.ai NL API python SDK,
Resolving ambiguity is not ambiguous anymore If you landed here, it means that you’re curious enough to learn more about the different ways to resolve ambiguity in NLP/NLU. Background information is the reason of ambiguity for machines. This ambiguous information arises from the human natural language used in communication. The process to “translate” this language into a comprehensive artificial language for machines could produce ambiguity. This can be explained by the fact that human language itself is inherently informal and ambiguous.Traditional distributional semantics approaches are based on words vectorization to address semantics. The alternative shown here is based on a knowledge graph with straightforward requests to solve lexical ambiguity. This tutorial will stress out some ambiguity resolution tasks that we can use to solve this problem, via an easy and convenient application, thus, a Natural Language API (NL API). Photo by Paweł Czerwiński on UnsplashThe article is availa
A brief tutorial on how to retrieve and visualize hidden relevant data in textual documents, using the expert.ai NL API. A series of random typography letters.Photo by Patrick Fore on Unsplash Read the article on Towards Data Science.If you have ever worked with text datasets, you know how difficult it is to retrieve central information in the dataset, avoiding noise or redundancy. Many approaches can get good results if you want to train your own model, but this requires time and resources. In the following tutorial we will explore how to retrieve hidden information in texts using expert.ai NL API Python SDK, that requires no model training, and then visualize the results in graphs that can be added to a report. What is expert.ai NL API?The expert.ai Natural Language API is a service developed by expert.ai, that can be used to easily build NLP applications. The library includes multiple features such as an NLP pipeline (tokenization, lemmatization, PoS tagging, dependency parsing, syn
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