Natural language processing with Apache OpenNLP

Beginning to display what humans call “common sense” is improving as the models capture more basic details about the world. The mathematical approaches are a mixture of rigid, rule-based structure and flexible probability. The structural approaches build models of phrases and sentences that are similar to the diagrams that are sometimes used to teach grammar to school-aged children.

Teaching computers to make sense of human language has long been a goal of computer scientists. The natural language that people use when speaking to each other is complex and deeply dependent upon context. While humans may instinctively understand that different words are spoken at home, at work, at a school, at a store or in a religious building, none of these differences are apparent to a computer algorithm. For example, suppose a dataset has language that assigns certain roles to men, such as computer programmers or doctors but assigns roles, like homemaker or nurse, to women. In that case, the AI program will implicitly apply those terms to men and women when communicating in real time.

Detecting sentences with OpenNLP

Maximum entropy is a concept from statistics that is used in natural language processing to optimize for best results. We’ll use /opennlp/src/main/java/com/infoworld/App.java for this example. Chatbots and cognitive agents are used to answer questions, look up information, or schedule appointments, without needing a human agent in the loop.

The prompt is also how one “programs” the model, and designing a good prompt is a big part of getting good results. Now, we’ll grab the “Person name finder” model for English, called en-ner-person.bin. Not that this model is located on the Sourceforge model downloads page. Once you have the model, put it in the resources directory for your project and use it to find names in the document, as shown in Listing 11. Let’s look at some of the main ways in which companies are adopting NLP technology and using it to improve business processes.

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DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation. The search engines have become adept at predicting or understanding whether the user wants a product, a definition, or a pointer into a document. This classification, though, is largely probabilistic, and the algorithms fail the user when the request doesn’t follow the standard statistical pattern.

• Let’s look at some of the main ways in which companies are adopting NLP technology and using it to improve business processes.
• As humans use more natural language products, they begin to intuitively predict what the AI may or may not understand and choose the best words.
• Doing that sort of thing and more can be done with OpenAI’s GPT-3, a natural language prediction model with an API that is probably a lot easier to use than you might think.
• After English, it guessed the language might be Tagalog, Welsh, or War-Jaintia.

The engine itself can be thought of as a sort of fantastically-complex state machine, while at the same time it is also not quite like anything else. OpenAI provides some excellent documentation as well as a web tool through which one can experiment interactively. Currently, it is usually not powerful enough to produce fully grammatical and idiomatic translations, but it can give you the gist of a web page or email in a language you don’t speak. 500 million people each day use Google Translate to help them understand text in over 100 languages. It’s not hard to see that the combination has potential for harm if used irresponsibly.

I will describe using the API in its most basic way, that of completion. That means one presents the API with a prompt, from which it will provide a text completion that attempts to match the prompt. All of this is done entirely in text, and formatted as natural language.

For instance, natural language processing can have implicit biases, create a significant carbon footprint, and stoke concerns about AI sentience. Natural language processing is a field in machine learning where a computer processes human language through vast amounts of data to understand, translate, extract, and organize information. However, the language processing tools such as Open AI’s Chat GPT and other tools run into some challenges, such as misspellings, speech recognition, and the ability of a computer to understand the nuances of human language. We know from virtual assistants like Alexa that machines are getting better at decoding the human voice all the time. As a result, the way humans communicate with machines and query information is beginning to change – and this could have a dramatic impact on the future of data analysis.

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Grammarly, for instance, makes a tool that proofreads text documents to flag grammatical problems caused by issues like verb tense. The free version detects basic errors, while the premium subscription of $12 offers access to more sophisticated error checking like identifying plagiarism or helping users adopt a more confident and polite tone. The company is more than 11 years old and it is integrated with most online environments where text might be edited.

It is not possible for AI to register experiences or feelings because it does not have the ability to think, feel, or perceive the world with a sentient mind. This material may not be published, broadcast, rewritten, or redistributed. Using the API isn’t free in the long term, but creating an account will give you a set of free credits that can be used to play around and try a few ideas out, and using even the most expensive engine for personal projects costs a pittance. All of my enthusiastic experimentation has so far used barely two dollars USD worth of my free trial.

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Speech analytics is a component of natural language processing that combines UIM with sentiment analysis. It’s used by call centers to turn text chats and transcriptions of phone conversations into structured data and analyze them using sentiment analysis. This can all be done in real-time, giving call center agents live feedback and suggestions during a call, and alerting a manager if the customer is unhappy. Some natural language processing algorithms focus on understanding spoken words captured by a microphone. These speech recognition algorithms also rely upon similar mixtures of statistics and grammar rules to make sense of the stream of phonemes.

Listing 11. Name finding with OpenNLP

The process used to train, experiment, and fine-tune a natural language process model has been estimated to create on average more CO2 emissions than two Americans annually. As well as saving you time and irritation by filtering out spam, this technology can be used to automate domain-specific classification tasks. Smartling is adapting natural language algorithms to do a better job automating translation, so companies can do a better job delivering software to people who speak different languages. They provide a managed pipeline to simplify the process of creating multilingual documentation and sales literature at a large, multinational scale.

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This process requires deep technical knowledge, extraordinary innovation and problem-solving abilities. Information engineers design and develop AI algorithms and systems, including creating machine learning models, selecting and processing data, and implementing artificial intelligence software solutions. Their technical expertise is essential for building robust and efficient systems. Professional engineers, the true protagonists of the AI revolution, are at the heart of this transformation. Their role crucially develops technology and ensures AI is implemented ethically and responsibly.

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Membership in such organizations enhances credibility and instills trust among clients and institutions, with certification often serving as a marker of competence and reliability. These organizations also offer continuous training and development opportunities, which are crucial in the fast-paced realm of AI, where technologies and best practices evolve rapidly. Information engineers will be at the forefront of this revolution, contributing to the research and development of novel AI technologies and methodologies, including advancements in machine learning models, network architectures and experimental approaches. Their efforts will push the boundaries of what is possible, enhancing our ability to understand and interact with the world. Future DALL-E artists, whether self-taught or schooled, will always need the ability to communicate and design an original point of view. It will continue to be humans who inject their ideas into machines in service of the newer and ever-changing language of creation.

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Engineers who master these technologies will remain at the forefront of innovation, contributing to the development of innovative solutions and shaping the future of their field. By embracing AI, software developers can enhance their productivity, tackle ambitious projects, and maintain their relevance in an increasingly AI-driven world. The race to develop foundational AI systems is intensifying, with organizations striving to secure long-term advantages. Early adoption of AI technologies is proving critical, as industries increasingly rely on skilled professionals to implement, manage, and innovate with these systems. This underscores the growing demand for expertise in AI integration and ethical deployment.

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But they are without the humanity that makes these activities worthwhile in the first place. Remember, when the pocket calculator was invented in the 20th century, many thought that it would lead to a decline in our ability to do basic mathematics. What actually happened was that lots of people – school children in particular – became capable of solving more complicated math problems more quickly.

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• Now an AI Integration Engineer at Boeing, Angelie joins Professional Quotient host Jason Winningham for a wide-ranging conversation on neurodivergent leadership, workplace inclusion, and how careers aren’t always linear — they’re lived.
• The ubiquity of artificial intelligence (AI) products has led me to deep consideration of what it means to be an academic, researcher, and scholar.
• They are setting direction, validating outcomes and ensuring that the software being shipped actually serves the business.
• It’s in the interest of their employees, who will benefit from the personal development.
• In order to design the output of these machine learning tools, we will need experience beyond engineering and design, in the same way that understanding the physics of light and aperture takes photographic art beyond the mundane.

AI tools can generate a wide array of test cases based on past failures or code coverage gaps. However, they frequently miss critical business-specific scenarios or edge conditions that are not well represented in historical data. They factor in company-specific constraints, regulatory risks and organizational readiness, which AI is not equipped to evaluate.