Intel adds sentiment analysis model to NLP Architect

Once AI becomes more developed these tools will move beyond competency and into the realm of fluency with human communications. The removal of barriers to natural language will be one of the most disruptive influences in the technological world. The use cases are so vast that the NLP market is anticipated to be worth $13.4 billion by 2020. Human communication has become an incredibly valuable commodity for modern enterprises. Everything from our preferences to our opinions can be used to help advertisers product more effective advertising and targeted services. LSI helps overcome synonymy by increasing recall, one of the most problematic constraints of Boolean keyword search queries and vector space models.

What is holding NLP back? (The limitations of natural language processing platforms)

With the use of AI increasing inall areas the development of effective governance is paramount. Explore the future of AI on August 5 in San Francisco—join Block, GSK, and SAP at Autonomous Workforces to discover how enterprises are scaling multi-agent systems with real-world results. Computers have a tendency to ignore the subtle nuances in favor of black and white interpretations. Misinterpretations are a common complaint of virtual assistants which are notorious for taking information at face value because they lack the ability to read between the lines.

“My best business intelligence, in one easy email…”

Bellegarda showed massive improvements in speech recognition tasks due to the ability of the LSA to capture long-term (or semantic) context of text. All of NLP Architect’s models ship with end-to-end examples of training and inference processes and with supporting data pipelines, common functional calls, and other utilities related to natural language processing. They’re modularized for integration, and some of the components are exposed as APIs through Intel’s NLP Architect server, a platform designed to provide predictions across different models. NLP Architect also includes a web frontend for visualizations, plus templates for developers to add new services.

Why knowledge is the ultimate weapon in the Information Age

Intel today revealed that as of version 0.4, NLP Architect includes models for a particular type of sentiment analysis dubbed aspect-based sentiment analysis (ABSA). Of all the applications of NLP there is one that outshines all others; sentiment analysis. Sentiment analysis or opinion mining is used to find and extract opinions from written text on everything from social media to blogs and forums. A sentiment analysis program is designed to identify the subject of the opinion, the person giving the opinion and whether the opinion expressed is positive or negative.

The AI insights you need to lead

It’s more challenging than it sounds; aspects are often domain-sensitive and share close semantic similarity. For instance, an opinion that might be considered positive in the context of a movie review (e.g. “delicate”) may be negative in another (a cell phone review). Quantum information retrieval has the remarkable virtue of combining both geometry and probability in a common principled framework. The quantum-motivated representation is an alternative for geometrical latent topic modeling worthy of further exploration.

• They’re modularized for integration, and some of the components are exposed as APIs through Intel’s NLP Architect server, a platform designed to provide predictions across different models.
• When you load up a voice recognition application like Siri, NLP is being used to interpret everything you say into the microphone.
• The quantum-motivated representation is an alternative for geometrical latent topic modeling worthy of further exploration.
• Nowhere is this more apparent than the financial industry where NLP is used for general sentiment analysis and for chatbots.

Most of the AI platforms using sentiment analysis are designed to quantify news sources such as blogs, articles and social media posts to asset the market. Sentifi’s Sentifi Maven is an example of a platform that uses sentiment analysis to collect news from over 13 million sources to see what topics are getting the most attention. Supervised learning approaches, which rely on large data sets of annotated samples, handle domain sensitivity pretty well, but Intel notes that compiling the necessary corpora is labor- and time-intensive. That’s why their ABSA model is lightly supervised, meaning it’s able to ingest unlabeled text and output opinion and aspect lexicons after domain-specific lexicons are defined. Over the next few years NLP will have a central role in developing chatbots and voice assistants.

One of the most well-known chatbots platforms in the financial industry has been designed by Kasisto. Now that the digital age is in full swing, there are an abundance of natural language processing communications up for grabs. While AI tools only have a limited capacity to leverage this data right now they are becoming more advanced every year. The barrier between human communication and the interpretative capabilities of AI is slowly and systematically being erased. Despite the fact that all this data is out there for the taking, AI platforms aren’t at the level where they can extract this information completely.

• With Kasisto Kai customers can make payments, view account balance, check credit or loan applications and search for transactions.
• While AI tools only have a limited capacity to leverage this data right now they are becoming more advanced every year.
• LSI/LSA is an application of Singular Value Decomposition Technique (SVD) on the word-document matrix used in Information Retrieval.
• All of NLP Architect’s models ship with end-to-end examples of training and inference processes and with supporting data pipelines, common functional calls, and other utilities related to natural language processing.
• As these programs become more sophisticated they will become better able to tackle the nuance of human language.

Within the field of Natural Language Processing (NLP) there are a number of techniques that can be deployed for the purpose of information retrieval and understanding the relationships between documents. The growth in unstructured data requires better methods for legal teams to cut through and understand these relationships as efficiently as possible. The simplest way of finding similar documents is by using vector representation of text and cosine similarity. One method for concept searching and determining semantics between phrases is Latent Semantic Indexing/Latent Semantic Analysis (LSI/LSA).

When you load up a voice recognition application like Siri, NLP is being used to interpret everything you say into the microphone. As these programs become more sophisticated they will become better able to tackle the nuance of human language. A number of experiments have demonstrated that there are several correlations between the way LSI and humans process and categorize text. This is because traditionally, imbuing machines with human-like knowledge relied primarily on the coding of symbolic facts into computer data structures and algorithms. A critical limitation of this approach was that it failed to address the unconscious human ability to source vast amounts of data collected over the course of a human’s life. This also fails to address important questions about how humans acquire and represent this data in the first place.

We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.

Synonymy is often the cause of mismatches in the vocabulary used by the authors of documents and the users of information retrieval systems. As a result, Boolean or keyword queries often return irrelevant results and miss information that is relevant. We support CTOs, CIOs and other technology leaders in managing business critical issues both for today and in the future.