What is Natural Language Processing? Introduction to NLP

Data processing serves as the first phase, where input text data is prepared and cleaned so that the machine is able to analyze it. The data is processed in such a way that it points out all the features in the input text and makes it suitable for computer algorithms. Basically, the data processing stage prepares the data in a form that the machine can understand.

However, building a whole infrastructure from scratch requires years of data science and programming experience or you may have to hire whole teams of engineers. Now that you’ve gained some insight into the basics of NLP and its current applications in business, you may be wondering how to put NLP into practice. Retently discovered the most relevant topics mentioned by customers, and which ones they valued most. Below, you can see that most of the responses referred to “Product Features,” followed by “Product UX” and “Customer Support” (the last two topics were mentioned mostly by Promoters). The use of voice assistants is expected to continue to grow exponentially as they are used to control home security systems, thermostats, lights, and cars – even let you know what you’re running low on in the refrigerator. You can even customize lists of stopwords to include words that you want to ignore.

Unfortunately, NLP is also the focus of several controversies, and understanding them is also part of being a responsible practitioner. For instance, researchers have found that models will natural language processing algorithm parrot biased language found in their training data, whether they’re counterfactual, racist, or hateful. Moreover, sophisticated language models can be used to generate disinformation.

How are NLP Algorithms categorized?

Therefore, the objective of this study was to review the current methods used for developing and evaluating NLP algorithms that map clinical text fragments onto ontology concepts. To standardize the evaluation of algorithms and reduce heterogeneity between studies, we propose a list of recommendations. The possibility of translating text and speech to different languages has always been https://chat.openai.com/ one of the main interests in the NLP field. From the first attempts to translate text from Russian to English in the 1950s to state-of-the-art deep learning neural systems, machine translation (MT) has seen significant improvements but still presents challenges. NLP powers many applications that use language, such as text translation, voice recognition, text summarization, and chatbots.

However, free-text descriptions cannot be readily processed by a computer and, therefore, have limited value in research and care optimization. The model performs better when provided with popular topics which have a high representation in the data (such as Brexit, for example), while it offers poorer results when prompted with highly niched or technical content. As customers crave fast, personalized, and around-the-clock support experiences, chatbots have become the heroes of customer service strategies. It involves filtering out high-frequency words that add little or no semantic value to a sentence, for example, which, to, at, for, is, etc. Ultimately, the more data these NLP algorithms are fed, the more accurate the text analysis models will be. This algorithm is basically a blend of three things – subject, predicate, and entity.

“One of the most compelling ways NLP offers valuable intelligence is by tracking sentiment — the tone of a written message (tweet, Facebook update, etc.) — and tag that text as positive, negative or neutral,” says Rehling. Austin is a data science and tech writer with years of experience both as a data scientist and a data analyst in healthcare. Starting his tech journey with only a background in biological sciences, he now helps others make the same transition through his tech blog AnyInstructor.com.

Some of the algorithms might use extra words, while some of them might help in extracting keywords based on the content of a given text. Topic modeling is one of those algorithms that utilize statistical NLP techniques to find out themes or main topics from a massive bunch of text documents. However, when symbolic and machine learning works together, it leads to better results as it can ensure that models correctly understand a specific passage. Along with all the techniques, NLP algorithms utilize natural language principles to make the inputs better understandable for the machine. They are responsible for assisting the machine to understand the context value of a given input; otherwise, the machine won’t be able to carry out the request.

Even though stemmers can lead to less-accurate results, they are easier to build and perform faster than lemmatizers. But lemmatizers are recommended if you’re seeking more precise linguistic rules. When we speak or write, we tend to use inflected forms of a word (words in their different grammatical forms). To make these words easier for computers to understand, NLP uses lemmatization and stemming to transform them back to their root form. Sentence tokenization splits sentences within a text, and word tokenization splits words within a sentence.

One of the most popular text classification tasks is sentiment analysis, which aims to categorize unstructured data by sentiment. Natural Language Processing (NLP) allows machines to break down and interpret human language. It’s at the core of tools we use every day – from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools. Natural language processing (NLP) is the ability of a computer program to understand human language as it’s spoken and written — referred to as natural language. NLP is a dynamic technology that uses different methodologies to translate complex human language for machines. It mainly utilizes artificial intelligence to process and translate written or spoken words so they can be understood by computers.

The field of study that focuses on the interactions between human language and computers is called natural language processing, or NLP for short. It sits at the intersection of computer science, artificial intelligence, and computational linguistics (Wikipedia). To fully comprehend human language, data scientists need to teach NLP tools to look beyond definitions and word order, to understand context, word ambiguities, and other complex concepts connected to messages. But, they also need to consider other aspects, like culture, background, and gender, when fine-tuning natural language processing models. Sarcasm and humor, for example, can vary greatly from one country to the next.

After reviewing the titles and abstracts, we selected 256 publications for additional screening. Out of the 256 publications, we excluded 65 publications, as the described Natural Language Processing algorithms in those publications were not evaluated. Other interesting applications of NLP revolve around customer service automation.

In the medical domain, SNOMED CT [7] and the Human Phenotype Ontology (HPO) [8] are examples of widely used ontologies to annotate clinical data. Natural Language Generation (NLG) is a subfield of NLP designed to build computer systems or applications that can automatically produce all kinds of texts in natural language by using a semantic representation as input. Some of the applications of NLG are question answering and text summarization. Tokenization is an essential task in natural language processing used to break up a string of words into semantically useful units called tokens.

Can Python be used for NLP?

We believe that our recommendations, along with the use of a generic reporting standard, such as TRIPOD, STROBE, RECORD, or STARD, will increase the reproducibility and reusability of future studies and algorithms. But deep learning is a more flexible, intuitive approach in which algorithms learn to identify speakers’ intent from many examples — almost like how a child would learn human language. From chatbots and sentiment analysis to document classification and machine translation, natural language processing (NLP) is quickly becoming a technological staple for many industries.

Two hundred fifty six studies reported on the development of NLP algorithms for mapping free text to ontology concepts. Twenty-two studies did not perform a validation on unseen data and 68 studies did not perform external validation. Of 23 studies that claimed that their algorithm was generalizable, 5 tested this by external validation. A list of sixteen recommendations regarding Chat PG the usage of NLP systems and algorithms, usage of data, evaluation and validation, presentation of results, and generalizability of results was developed. Two reviewers examined publications indexed by Scopus, IEEE, MEDLINE, EMBASE, the ACM Digital Library, and the ACL Anthology. Publications reporting on NLP for mapping clinical text from EHRs to ontology concepts were included.

Natural Language Processing in Government

NLP can also be used to automate routine tasks, such as document processing and email classification, and to provide personalized assistance to citizens through chatbots and virtual assistants. It can also help government agencies comply with Federal regulations by automating the analysis of legal and regulatory documents. Segmentation

Segmentation in NLP involves breaking down a larger piece of text into smaller, meaningful units such as sentences or paragraphs.

In financial services, NLP is being used to automate tasks such as fraud detection, customer service, and even day trading. For example, JPMorgan Chase developed a program called COiN that uses NLP to analyze legal documents and extract important data, reducing the time and cost of manual review. In fact, the bank was able to reclaim 360,000 hours annually by using NLP to handle everyday tasks. Text processing is a valuable tool for analyzing and understanding large amounts of textual data, and has applications in fields such as marketing, customer service, and healthcare. Machine learning algorithms use annotated datasets to train models that can automatically identify sentence boundaries.

#3. Natural Language Processing With Transformers

The applications of NLP have led it to be one of the most sought-after methods of implementing machine learning. Natural Language Processing (NLP) is a field that combines computer science, linguistics, and machine learning to study how computers and humans communicate in natural language. The goal of NLP is for computers to be able to interpret and generate human language. This not only improves the efficiency of work done by humans but also helps in interacting with the machine. The Machine and Deep Learning communities have been actively pursuing Natural Language Processing (NLP) through various techniques. Some of the techniques used today have only existed for a few years but are already changing how we interact with machines.

Sentiment analysis is the process of identifying, extracting and categorizing opinions expressed in a piece of text. The goal of sentiment analysis is to determine whether a given piece of text (e.g., an article or review) is positive, negative or neutral in tone. Statistical algorithms allow machines to read, understand, and derive meaning from human languages.

Natural language processing (NLP) is an interdisciplinary subfield of computer science and information retrieval. It is primarily concerned with giving computers the ability to support and manipulate human language. It involves processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of “understanding”[citation needed] the contents of documents, including the contextual nuances of the language within them. To this end, natural language processing often borrows ideas from theoretical linguistics. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

They use highly trained algorithms that, not only search for related words, but for the intent of the searcher. Results often change on a daily basis, following trending queries and morphing right along with human language. They even learn to suggest topics and subjects related to your query that you may not have even realized you were interested in. Text classification is the process of understanding the meaning of unstructured text and organizing it into predefined categories (tags).

The main job of these algorithms is to utilize different techniques to efficiently transform confusing or unstructured input into knowledgeable information that the machine can learn from. Like humans have brains for processing all the inputs, computers utilize a specialized program that helps them process the input to an understandable output. NLP operates in two phases during the conversion, where one is data processing and the other one is algorithm development. And with the introduction of NLP algorithms, the technology became a crucial part of Artificial Intelligence (AI) to help streamline unstructured data. Human languages are difficult to understand for machines, as it involves a lot of acronyms, different meanings, sub-meanings, grammatical rules, context, slang, and many other aspects. Sentiment analysis can be performed on any unstructured text data from comments on your website to reviews on your product pages.

Introduction to Convolution Neural Network

Natural Language Processing (NLP) is a branch of artificial intelligence that involves the use of algorithms to analyze, understand, and generate human language. Deep-learning models take as input a word embedding and, at each time state, return the probability distribution of the next word as the probability for every word in the dictionary. Pre-trained language models learn the structure of a particular language by processing a large corpus, such as Wikipedia. For instance, BERT has been fine-tuned for tasks ranging from fact-checking to writing headlines. Sentiment analysis (seen in the above chart) is one of the most popular NLP tasks, where machine learning models are trained to classify text by polarity of opinion (positive, negative, neutral, and everywhere in between). The main benefit of NLP is that it improves the way humans and computers communicate with each other.

It can help improve efficiency and comprehension by presenting information in a condensed and easily digestible format. Speech recognition is widely used in applications, such as in virtual assistants, dictation software, and automated customer service. It can help improve accessibility for individuals with hearing or speech impairments, and can also improve efficiency in industries such as healthcare, finance, and transportation. Speech recognition, also known as automatic speech recognition (ASR), is the process of using NLP to convert spoken language into text. Natural Language Generation (NLG) is the process of using NLP to automatically generate natural language text from structured data. NLG is often used to create automated reports, product descriptions, and other types of content.

Text processing using NLP involves analyzing and manipulating text data to extract valuable insights and information. Text processing uses processes such as tokenization, stemming, and lemmatization to break down text into smaller components, remove unnecessary information, and identify the underlying meaning. Parsing

Parsing involves analyzing the structure of sentences to understand their meaning. It involves breaking down a sentence into its constituent parts of speech and identifying the relationships between them.

All data generated or analysed during the study are included in this published article and its supplementary information files. Text classification is a core NLP task that assigns predefined categories (tags) to a text, based on its content. It’s great for organizing qualitative feedback (product reviews, social media conversations, surveys, etc.) into appropriate subjects or department categories. Other classification tasks include intent detection, topic modeling, and language detection. However, since language is polysemic and ambiguous, semantics is considered one of the most challenging areas in NLP. Individuals working in NLP may have a background in computer science, linguistics, or a related field.

It can be used to determine the voice of your customer and to identify areas for improvement. It can also be used for customer service purposes such as detecting negative feedback about an issue so it can be resolved quickly. The challenge is that the human speech mechanism is difficult to replicate using computers because of the complexity of the process. It involves several steps such as acoustic analysis, feature extraction and language modeling. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.

During segmentation, a segmenter analyzes a long article and divides it into individual sentences, allowing for easier analysis and understanding of the content. For example, in the sentence “The cat chased the mouse,” parsing would involve identifying that “cat” is the subject, “chased” is the verb, and “mouse” is the object. It would also involve identifying that “the” is a definite article and “cat” and “mouse” are nouns. By parsing sentences, NLP can better understand the meaning behind natural language text. Natural Language Processing (NLP) uses a range of techniques to analyze and understand human language.

You can foun additiona information about ai customer service and artificial intelligence and NLP. It gives machines the ability to understand texts and the spoken language of humans. With NLP, machines can perform translation, speech recognition, summarization, topic segmentation, and many other tasks on behalf of developers. With existing knowledge and established connections between entities, you can extract information with a high degree of accuracy.

The goal of NLP is to bridge the communication gap between humans and machines, allowing us to interact with technology in a more natural and intuitive way. Based on the findings of the systematic review and elements from the TRIPOD, STROBE, RECORD, and STARD statements, we formed a list of recommendations. The recommendations focus on the development and evaluation of NLP algorithms for mapping clinical text fragments onto ontology concepts and the reporting of evaluation results. Natural Language Processing (NLP) can be used to (semi-)automatically process free text.

Sentiment analysis is the automated process of classifying opinions in a text as positive, negative, or neutral. You can track and analyze sentiment in comments about your overall brand, a product, particular feature, or compare your brand to your competition. There are many challenges in Natural language processing but one of the main reasons NLP is difficult is simply because human language is ambiguous. PoS tagging is useful for identifying relationships between words and, therefore, understand the meaning of sentences.

Python is the best programming language for NLP for its wide range of NLP libraries, ease of use, and community support. However, other programming languages like R and Java are also popular for NLP. You can refer to the list of algorithms we discussed earlier for more information. This algorithm creates a graph network of important entities, such as people, places, and things. This graph can then be used to understand how different concepts are related.

Other practical uses of NLP include monitoring for malicious digital attacks, such as phishing, or detecting when somebody is lying. And NLP is also very helpful for web developers in any field, as it provides them with the turnkey tools needed to create advanced applications and prototypes. Depending on what type of algorithm you are using, you might see metrics such as sentiment scores or keyword frequencies.

As the technology evolved, different approaches have come to deal with NLP tasks. Insurance agencies are using NLP to improve their claims processing system by extracting key information from the claim documents to streamline the claims process. NLP is also used to analyze large volumes of data to identify potential risks and fraudulent claims, thereby improving accuracy and reducing losses. Chatbots powered by NLP can provide personalized responses to customer queries, improving customer satisfaction.

This approach contrasts machine learning models which rely on statistical analysis instead of logic to make decisions about words. To understand human language is to understand not only the words, but the concepts and how they’re linked together to create meaning. Despite language being one of the easiest things for the human mind to learn, the ambiguity of language is what makes natural language processing a difficult problem for computers to master.

Over one-fourth of the publications that report on the use of such NLP algorithms did not evaluate the developed or implemented algorithm. In addition, over one-fourth of the included studies did not perform a validation and nearly nine out of ten studies did not perform external validation. Of the studies that claimed that their algorithm was generalizable, only one-fifth tested this by external validation. Based on the assessment of the approaches and findings from the literature, we developed a list of sixteen recommendations for future studies.

Deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been applied to tasks such as sentiment analysis and machine translation, achieving state-of-the-art results. Natural Language Processing (NLP) is a subfield of artificial intelligence that deals with the interaction between computers and humans in natural language. It involves the use of computational techniques to process and analyze natural language data, such as text and speech, with the goal of understanding the meaning behind the language. From speech recognition, sentiment analysis, and machine translation to text suggestion, statistical algorithms are used for many applications. The main reason behind its widespread usage is that it can work on large data sets.

• More broadly speaking, the technical operationalization of increasingly advanced aspects of cognitive behaviour represents one of the developmental trajectories of NLP (see trends among CoNLL shared tasks above).
• By parsing sentences, NLP can better understand the meaning behind natural language text.
• To standardize the evaluation of algorithms and reduce heterogeneity between studies, we propose a list of recommendations.
• You may have used some of these applications yourself, such as voice-operated GPS systems, digital assistants, speech-to-text software, and customer service bots.
• SaaS tools, on the other hand, are ready-to-use solutions that allow you to incorporate NLP into tools you already use simply and with very little setup.

Finally, you’ll see for yourself just how easy it is to get started with code-free natural language processing tools. Over 80% of Fortune 500 companies use natural language processing (NLP) to extract text and unstructured data value. Aspect mining classifies texts into distinct categories to identify attitudes described in each category, often called sentiments. Aspects are sometimes compared to topics, which classify the topic instead of the sentiment. Depending on the technique used, aspects can be entities, actions, feelings/emotions, attributes, events, and more. 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.

The most direct way to manipulate a computer is through code — the computer’s language. Enabling computers to understand human language makes interacting with computers much more intuitive for humans. Syntax and semantic analysis are two main techniques used in natural language processing. NLP is an integral part of the modern AI world that helps machines understand human languages and interpret them. The expert.ai Platform leverages a hybrid approach to NLP that enables companies to address their language needs across all industries and use cases.

This knowledge base article will provide you with a comprehensive understanding of NLP and its applications, as well as its benefits and challenges. Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that makes human language intelligible to machines. NLP techniques are widely used in a variety of applications such as search engines, machine translation, sentiment analysis, text summarization, question answering, and many more. NLP research is an active field and recent advancements in deep learning have led to significant improvements in NLP performance. However, NLP is still a challenging field as it requires an understanding of both computational and linguistic principles.

Machine translation using NLP involves training algorithms to automatically translate text from one language to another. This is done using large sets of texts in both the source and target languages. Syntax analysis involves breaking down sentences into their grammatical components to understand their structure and meaning. One of the main activities of clinicians, besides providing direct patient care, is documenting care in the electronic health record (EHR). These free-text descriptions are, amongst other purposes, of interest for clinical research [3, 4], as they cover more information about patients than structured EHR data [5].

Hugh O’Neill, Earl of Tyrone Wikipedia

In 1595, Sir John Norris was ordered to Ireland at the head of a considerable force for the purpose of subduing him, but O’Neill succeeded in taking the Blackwater Fort before Norris could prepare his forces. O’Neill was instantly proclaimed a traitor at Dundalk.[1] The war that followed is known as the Nine Years’ War. Although born into the powerful O’Neill family of Ulster, Hugh was fostered as a ward of the crown in County Dublin after the assassination of his father, Matthew, in 1558. His wardship ended in 1567, and, after a visit to the court in London, he returned to Ireland in 1568 and assumed his grandfather’s title of earl of Tyrone. By initially cooperating with the government of Queen Elizabeth I, he established his base of power, and in 1593 he replaced Turlough Luineach O’Neill as chieftain of the O’Neills. But his dominance in Ulster led to a deterioration in his relations with the crown, and skirmishes between Tyrone’s forces and the English in 1595 were followed by three years of fruitless negotiations between the two sides.

• His victory (August 14) over the English in the Battle of the Yellow Ford on the River Blackwater, Ulster—the most serious defeat sustained by the English in the Irish wars—sparked a general revolt throughout the country.
• He was the second son of Matthew O’Neill, also called Feardorach,[4] reputed illegitimate son of Conn, 1st Earl of Tyrone.
• Following active duty, he attended the University of Georgia and graduated with a Bachelor of Business Administration degree.
• With the recession of the water O’Neil was pulled rapidly downward to his chin, while Rotruck sank to his chest.

As he looked about for Claudia, water began to bubble up on the floor of the crater, causing some sliding of the sandy soil. Rotruck sank to his knees and, as the water receded with a loud suction sound, was pulled downward to his waist. At Rotruck’s call for help, O’Neil moved to within 12 feet of him. A second surge of water caused further slides, and O’Neil’s legs sank in the wet sand. With the recession of the water O’Neil was pulled rapidly downward to his chin, while Rotruck sank to his chest. Men swung the longer ladder by its rope to O’Neil, who briefly grasped it before he was pulled under.

Hugh O’Neil

Firemen arrived, but by the time one man reached the bottom of another ladder lowered near him Rotruck also had been pulled under. More of the pavement later gave way, a heavy slide occurred, and the water dislodged the sedan. The body of O’Neil was drawn into the storm sewer and carried through it to a river bank, while the bodies of Claudia and Rotruck later were recovered from the crater.

The defeat of O’Neill and the conquest of his province of Ulster was the final step in the subjugation of Ireland by the English. Hugh Lee O’Neal Sr died February 20, 2023 peacefully at his home surrounded by his family. He was born September 14, 1934 and grew up on a farm in Stark, Georgia. In High School, he participated in Future Farmers of America [FFA] and then continued on to Abraham Baldwin Agricultural College (ABAC).

Celebration of Life

He loved Georgia football, especially listening to Larry Munson call the play-by-play on crisp October weekends as he raked leaves in the yard with his sons. Growing up on a farm, he learned to build and repair everything himself. Elizabeth’s successor, King James I, allowed Tyrone to keep most of his lands, but the chieftain soon found that he could not bear the loss of his former independence and prestige. In September 1607 Tyrone, with Rory O’Donnell, earl of Tyrconnell, and their followers, secretly embarked on a ship bound for Spain. You can foun additiona information about ai customer service and artificial intelligence and NLP. Outlawed by the English, O’Neill lived in Rome the rest of his life. Hugh O’Neill, 2nd earl of Tyrone (born c. 1550—died July 20, 1616, Rome, Papal States [Italy]) was an Irish rebel who, from 1595 to 1603, led an unsuccessful Roman Catholic uprising against English rule in Ireland.

His victory (August 14) over the English in the Battle of the Yellow Ford on the River Blackwater, Ulster—the most serious defeat sustained by the English in the Irish wars—sparked a general revolt throughout the country. Pope Clement VIII lent moral support to Tyrone’s cause, and, in September 1601, 4,000 Spanish troops arrived at Kinsale, Munster, to assist the insurrection. But those reinforcements were quickly surrounded at Kinsale, and Tyrone suffered a staggering defeat (December 1601) while attempting to break the siege. He continued to resist until forced to surrender on March 30, 1603, six days after the death of Queen Elizabeth.

September 14, 1934 — February 20, 2023

He was at ABAC for only two years when he joined the Navy and began his training at NAS Pensacola to become a naval fighter pilot. After earning his gold wings, he would serve four years hugh oneal active duty and in the reserves for sixteen years. Following active duty, he attended the University of Georgia and graduated with a Bachelor of Business Administration degree.

Hugh O’Neill came from a line of the O’Neill dynasty—derbfine—that the English authorities recognized as the legitimate successors to the Chiefs of the O’Neills and to the title of Earl of Tyrone. He was the second son of Matthew O’Neill, also called Feardorach,[4] reputed illegitimate son of Conn, 1st Earl of Tyrone.

Family background and early life

Hugh Michael O’Neil helped to rescue Janet E. Lewis and Velma M. Shidler and died attempting to rescue Ronald D. Rotruck from a cave-in, Akron, Ohio, July 21, 1964. The sedan landed on its back end in an almost vertical position with the roof against the Chat PG sloping wall of a crater 30 feet deep and 20 feet in diameter. Claudia fell through the rear window, but Mrs. Shidler drew Janet into the front seat and called for help. A number of motorists, including O’Neil, 19, student, had stopped at the scene.

O’Neil volunteered to go to the aid of the sedan’s occupants; and an 18 -foot ladder, attached to a rope tied to a truck, was lowered into the crater. With a rope tied around his waist and held by several other men, O’Neil descended the ladder, dropped 13 feet to the floor of the crater, and made his way around the perimeter to the sedan. Rotruck, 27, police patrolman, arrived, noted the situation, and asked for a rope.

Hugh O’Neill, 2nd earl of Tyrone

Because Janet’s injuries prevented her holding to the ladder, O’Neil removed his rope and tied her to the lower rungs. Men at the surface raised the ladder and then re-lowered it after removing Janet. O’Neil moved to meet them and aided Mrs. Shidler, who told them there was another person to be rescued. While O’Neil took Mrs. Shidler to the ladder by which she was raised to the surface, Rotruck returned to the sedan.

Two ropes were tied together and then around the waist of Rotruck, who also descended the ladder. As O’Neil carried Janet https://chat.openai.com/ to a longer ladder which had been lowered nearer the sedan. Rotruck made his way around the perimeter to the automobile.