Using Watson NLU to help address bias in AI sentiment analysis

What Is Natural Language Generation?

NLP attempts to analyze and understand the text of a given document, and NLU makes it possible to carry out a dialogue with a computer using natural language. When given a natural language input, NLU splits that input into individual words — called tokens — which include punctuation and other symbols. The tokens are run through a dictionary that can identify a word and its part of speech. The tokens are then analyzed for their grammatical structure, including the word’s role and different possible ambiguities in meaning.

In the realm of targeted marketing strategies, NLU and NLP allow for a level of personalization previously unattainable. By analyzing individual behaviors and preferences, businesses can tailor their messaging and offers to match the unique interests of each customer, increasing the relevance and effectiveness of their marketing efforts. This personalized approach not only enhances customer engagement but also boosts the efficiency of marketing campaigns by ensuring that resources are directed toward the most receptive audiences. NLP technologies of all types are further limited in healthcare applications when they fail to perform at an acceptable level. The researchers noted that, like any advanced technology, there must be frameworks and guidelines in place to make sure that NLP tools are working as intended. Many of these are shared across NLP types and applications, stemming from concerns about data, bias and tool performance.

Natural language understanding applications

LEIAs convert sentences into text-meaning representations (TMR), an interpretable and actionable definition of each word in a sentence. Based on their context and goals, LEIAs determine which language inputs need to be followed up. The all-in-one service includes everything from self-learning chatbot technology to agent assistance dashboards, and campaign management tools, to help businesses enhance the full customer journey. Additionally, a built-in marketplace allows developers to access a variety of reusable components model bots, and pre-built assets to customize omnichannel interactions. NLU is concerned with computer reading comprehension, focusing heavily on determining the meaning of a piece of text. GANs utilize multiple neural networks to create synthetic data instead of real-world data.

First, the computer must take natural language (humans speaking English) and convert it into artificial language. Once the information is in text form, NLU can take place to try to understand the meaning of that text. NLP helps uncover critical insights from social conversations brands have with customers, as well as chatter around their brand, through conversational AI techniques and sentiment analysis. Goally used this capability to monitor social engagement across their social channels to gain a better understanding of their customers’ complex needs. Natural language understanding (NLU) enables unstructured data to be restructured in a way that enables a machine to understand and analyze it for meaning.

SPEECH TO TEXT

Indeed, it’s a popular choice for developers working on projects that involve complex processing and understanding natural language text. IBM Watson Natural Language Understanding (NLU) is a cloud-based platform that uses IBM’s proprietary artificial intelligence engine to analyze and interpret text data. It can extract critical information from unstructured text, such as entities, keywords, sentiment, and categories, and identify relationships between concepts for deeper context. NLU and NLP have become pivotal in the creation of personalized marketing messages and content recommendations, driving engagement and conversion by delivering highly relevant and timely content to consumers. These technologies analyze consumer data, including browsing history, purchase behavior, and social media activity, to understand individual preferences and interests.

Continuously engage with NLP communities, forums, and resources to stay updated on the latest developments and best practices. We develop a model specializing in the temporal relation classification (TLINK-C) task, and assume that the MTL approach has the potential to contribute to performance improvements. 4, we designed deep neural networks with the hard parameter sharing strategy in which the MTL model has some task-specific layers and shared layers, which is effective in improving prediction results as well as reducing storage costs. As the MTL approach does not always yield better performance, we investigated different combinations of NLU tasks by varying the number of tasks N. Apart from leveraging the data produced on social media in the form of text, image, video, and user profiles, NLP is working as a key enabler with the AI programs. It is heightening the application of Artificial Intelligence programs for innovative usages like speech recognition, chatbots, machine translation, and OCR or optical character recognition.

• But with proper training, NLG can transform data into automated status reports and maintenance updates on factory machines, wind turbines and other Industrial IoT technologies.
• This folder has now become an enormous messy heap of audios, and I often don’t even remember what each particular file is about.
• We’re just starting to feel the impact of entity-based search in the SERPs as Google is slow to understand the meaning of individual entities.

Artificial Intelligence is quickly emerging as one of the most valuable tools in any customer experience (CX) strategy. As customer expectations continue to evolve, and buyer journeys grow more complex, business leaders are turning to AI to help them manage and deliver consistent, convenient, and personalized customer service. Despite the excitement around genAI, healthcare stakeholders should be aware that generative AI can exhibit bias, like other advanced analytics tools. Additionally, genAI models can ‘hallucinate’ by perceiving patterns that are imperceptible to humans or nonexistent, leading the tools to generate nonsensical, inaccurate, or false outputs.

The company also offers voice AI that helps people speak to their smart speakers, coffee machines, and cars. Sentiment analysis, language detection, and customized question answering are free for 5,000 text records per month. To see how Natural Language Understanding can detect sentiment in language and text data, try theWatson Natural Language Understanding demo.

Today the CMSWire community consists of over 5 million influential customer experience, customer service and digital experience leaders, the majority of whom are based in North America and employed by medium to large organizations. The subtleties of humor, sarcasm, and idiomatic expressions can still be difficult for NLU and NLP to accurately interpret and translate. To overcome these hurdles, brands often supplement AI-driven translations with human oversight. Linguistic experts review and refine machine-generated translations to ensure they align with cultural norms and linguistic nuances.

The year 2020 saw an unexpected, almost overnight surge in customer service traffic. Only the companies with a functional and robust virtual agent in place could mitigate the sudden rise in inquiry volume. If the chatbot encounters a complex question beyond its scope or an escalation from the customer end, the chatbot seamlessly transfers the customer to a human agent.

Google Cloud, a pioneer of language space, offers two types of NLPs, Auto Machine Learning and Natural Language API, to assess the framework and meaning of a text. Given a block of text, the algorithm counted the number of polarized words in the text; if there were more negative words than positive ones, the sentiment would be defined as negative. Depending on sentence structure, this approach could easily lead to bad results (for example, from sarcasm). Q&A systems are a prominent area of focus today, but the capabilities of NLU and NLG are important in many other areas.

By injecting the prompt with relevant and contextual supporting information, the LLM can generate telling and contextually accurate responses to user input. Use the services on the IBM Cloud to convert speech into text using AI-powered speech recognition and transcription in multiple languages for a variety of use cases. Generally, computer-generated content lacks the fluidity, emotion and personality that makes human-generated content interesting and engaging. However, NLG can be used with NLP to produce humanlike text in a way that emulates a human writer.

These pretrained models can be downloaded and fine-tuned for a wide variety of different target tasks. For example, sentiment analysis has been around for a very long time and has been very successful in social media to analyse tweets or Instagram or every kind of different post. The Markov model is a mathematical method used in statistics and machine learning to model and analyze systems that are able to make random choices, such as language generation.

Instead of relying solely on fully autonomous AI Agents from the get-go, introducing varying levels of agency/autonomy into everyday applications can provide a more balanced approach to automation. Reinvent critical workflows and operations by adding AI to maximize experiences, real-time decision-making and business value. Conversational AI is also very scalable as adding infrastructure to support conversational AI is cheaper and faster than the hiring and on-boarding process for new employees.

AI Agents & The Need For An Agentic Spectrum – substack.com

AI Agents & The Need For An Agentic Spectrum.

Posted: Sun, 19 Jan 2025 19:14:45 GMT [source]

CNNs and RNNs are competent models, however, they require sequences of data to be processed in a fixed order. Transformer models are considered a significant improvement because they don’t require data sequences to be processed in any fixed order. This function triggers the pre-processing function, that creates a folder with all converted files ready to be analyzed, and then iterates through every file. It resamples the file, then transcribes it, analyzes the text and generates the report. SoundHound, based in Santa Clara, California, develops technologies like speech and sound recognition, NLU, and search. Some of its use cases include food ordering technology, video discovery, and home assistance.

The strategic implications are far-reaching, from product development to customer engagement to competitive positioning. Essentially, multi-dimensional sentiment metrics enable businesses to adapt to shifting emotional landscapes, thereby crafting strategies that are responsive and predictive of consumer behavior. Therefore, companies that leverage these advanced analytical tools effectively position themselves at the forefront of market trends, gaining a competitive edge that is both data-driven and emotionally attuned. Google Cloud Natural Language API is widely used by organizations leveraging Google’s cloud infrastructure for seamless integration with other Google services. It allows users to build custom ML models using AutoML Natural Language, a tool designed to create high-quality models without requiring extensive knowledge in machine learning, using Google’s NLP technology. Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders.

Its ease of use and streamlined API make it a popular choice among developers and researchers working on NLP projects. We picked Hugging Face Transformers for its extensive library of pre-trained models and its flexibility in customization. Its user-friendly interface and support for multiple deep learning frameworks make it ideal for developers looking to implement robust NLP models quickly.

Also, by 2022, 70% of white-collar workers will interact with some form of conversational AI on a daily basis. And if those interactions were to be meaningful, it clearly indicates that conversational AI vendors will have to step up their game. Apple is looking into using transfer learning techniques for its voice assistant Siri, while Google has already released its own version of GPT-4 called Bard.

Natural language processing (NLP) is a subfield of artificial intelligence and computer science that focuses on the tokenization of data – the parsing of human language into its elemental pieces. By combining computational linguistics with statistical machine learning techniques and deep learning models, NLP enables computers to process human language in the form of text or voice data. Lemmatization and part of speech tagging enable a deep understanding of language, including context, the speaker or writer’s intent and sentiment. Natural language processing (NLP) is a field within artificial intelligence that enables computers to interpret and understand human language. Using machine learning and AI, NLP tools analyze text or speech to identify context, meaning, and patterns, allowing computers to process language much like humans do. One of the key benefits of NLP is that it enables users to engage with computer systems through regular, conversational language—meaning no advanced computing or coding knowledge is needed.

During training, machine learning models process large corpora of text and tune their parameters based on how words appear next to each other. In these models, context is determined by the statistical relations between word sequences, not the meaning behind the words. Naturally, the larger the dataset and more diverse the examples, the better those numerical parameters will be able to capture the variety of ways words can appear next to each other. The voracious data and compute requirements of Deep Neural Networks would seem to severely limit their usefulness. However, transfer learning enables a trained deep neural network to be further trained to achieve a new task with much less training data and compute effort. It consists simply of first training the model on a large generic dataset (for example, Wikipedia) and then further training (“fine-tuning”) the model on a much smaller task-specific dataset that is labeled with the actual target task.

The platform provides pre-trained models for everyday text analysis tasks such as sentiment analysis, entity recognition, and keyword extraction, as well as the ability to create custom models tailored to specific needs. NLP and NLU are closely related fields within AI that focus on the interaction between computers and human languages. It includes tasks such as speech recognition, language translation, and sentiment analysis. NLP serves as the foundation that enables machines to handle the intricacies of human language, converting text into structured data that can be analyzed and acted upon. Or requested something from a virtual assistant like Siri, Alexa, or your car’s infotainment system?

This is primarily due to a phenomenon known as “exposure bias,” as seen in the work Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks. This imperfection arises due to a mismatch between how these models are trained and their actual use during inference, often leading to error accumulation during text generation. In sentiment analysis, multi-dimensional sentiment metrics offer an unprecedented depth of understanding that transcends the rudimentary classifications of positive, negative, or neutral feelings. Traditional sentiment analysis tools have limitations, often glossing over the intricate spectrum of human emotions and reducing them to overly simplistic categories.

A sequence to sequence (or seq2seq) model takes an entire sentence or document as input (as in a document classifier) but it produces a sentence or some other sequence (for example, a computer program) as output. For each word in a document, the model predicts whether that word is part of an entity mention, and if so, what kind of entity is involved. For example, in “XYZ Corp shares traded for $28 yesterday”, “XYZ Corp” is a company entity, “$28” is a currency amount, and “yesterday” is a date. The training data for entity recognition is a collection of texts, where each word is labeled with the kinds of entities the word refers to. This kind of model, which produces a label for each word in the input, is called a sequence labeling model.

Connectionist methods rely on mathematical models of neuron-like networks for processing, commonly called artificial neural networks. In the last decade, however, deep learning models have met or exceeded prior approaches in NLP. Stanford CoreNLPOpens a new window is an NLTK-like library meant for NLP-related processing tasks.