Analyzing literature involves tracking interactions between characters, locations, and themes. Visualization has the potential to facilitate the mapping and analysis of these complex relationships, but capturing structured information from unstructured story data remains a challenge. As large language models (LLMs) continue to advance, we see an opportunity to use their text processing and analysis capabilities to augment and reimagine existing storyline visualization techniques. Toward this goal, we introduce an LLM-driven data parsing pipeline that automatically extracts relevant narrative information from novels and scripts. We then apply this pipeline to create Story Ribbons, an interactive visualization system that helps novice and expert literary analysts explore detailed character and theme trajectories at multiple narrative levels. Through pipeline evaluations and user studies with Story Ribbons on 36 literary works, we demonstrate the potential of LLMs to streamline narrative visualization creation and reveal new insights about familiar stories. We also describe current limitations of AI-based systems, and interaction motifs designed to address these issues.

Renard (Relationships Extraction from NARrative Documents) is a Python library that allows users to define custom natural language processing (NLP) pipelines to extract character networks from narrative texts. Contrary to the few existing tools, Renard can extract dynamic networks, as well as the more common static networks. Renard pipelines are modular: users can choose the implementation of each NLP subtask needed to extract a character network. This allows users to specialize pipelines to particular types of texts and to study the impact of each subtask on the extracted network.

While your body is lying in bed, your mind may be strolling around the manicured gardens of a manor house or the gritty streets of Victorian London. But now you can see some of the most iconic homes in literature with your own eyes, thanks an artificial intelligence (AI). These include Pemberley House, Mr Darcy's lavish estate in'Pride and Prejudice', and the residence of the world's most famous detective, Sherlock Holmes. Book lovers at Hammonds Furniture used the text-to-image software Midjourney to bring fictional homes to life in celebration of World Book Day 2023 - but how many of them can you guess? Jay Gatsby's mansion in'The Great Gatsby' (pictured) is described as a'colossal affair by any standard' and an'imitation of some Hôtel de Ville in Normandy' Daisy Buchanan's estate in'The Great Gatsby' (pictured) is described as a'cheerful red-and-white Georgian Colonial mansion', as well as'elaborate', 'bright' and'rosy-coloured' The above two houses are depictions of those from'The Great Gatsby', a novel set in 1922 that follows the life of mysterious millionaire Jay Gatsby.

Computers can now mimic everything from literary characters to Nirvana songs.

For data scientists and engineers, speed is important along with accuracy. For accuracy, we built Finetuner, which lets you finetune neural networks to achieve top performance on downstream tasks. Concerning speed, Jina was already fast, but now it's even faster. DocArray has been created to remove all the shortcomings in existing data structures, especially for ML and data science-related tasks. Here is a comparison of DocArray with other data structures.

Irina Raicu is the director of the Internet Ethics program (@IEthics) at the Markkula Center for Applied Ethics. It is a truth universally acknowledged--or at least a belief shared by many artificial intelligence and machine learning researchers--that, given a vast database and sophisticated modeling, an algorithm will be able to predict the behavior of individual human beings. Jane Austen might seem like the wrong authority to turn to in order to dispute this. But she does have some relevant insights on the topic. You might remember that in her novel Pride and Prejudice Austen features a heroine named Elizabeth who has a lot of interactions with a character named Mr. Darcy, whom she eventually marries.

W e present a Markov model at the discourse level for Steven Pinker's "mentalese", or chains of mental states that transcend the spoken/written forms. Such (potentially) universal temporal structures of textual pa tterns lead us to a language-independent semantic representation, or a translationally-invariant word embe dding, thereby forming the common ground for both comprehensibility within a given language and transla tability between different languages. Applying our model to documents of moderate lengths, without relying on external knowledge bases, we reconcile Noam Chomsky's "poverty of stimulus" paradox with statisti cal learning of natural languages. W e human beings distinguish ourselves from other animals ( 1-3), in that our brain development ( 4-6) enables us to convey sophisticated ideas and to share individual experience s, via languages ( 7-9). Texts written in natural languages constitute a major medium that perpetuates our civilizations ( 10), as a cumulative body of knowledge.

It is a truth universally acknowledged that sentiment analysis is super fun, and Pride and Prejudice is probably my very favorite book in all of literature, so let's do some Jane Austen natural language processing. Project Gutenberg makes e-texts available for many, many books, including Pride and Prejudice which is available here. I am using the plain text UTF-8 file available at that link for this analysis. Let's read the file and get it ready for analysis. The plain text file has lines that are just over 70 characters long.

This paper explores the application of computer simulation techniques to the fields of literary studies and narratology by developing a model for plot structure and characterization. Using a corpus of 19th Century British novels as a case study, the author begins with a descriptive quantitative analysis of character names, developing a set of stylized facts about the way narratives allocate attention to their characters. The author shows that narrative attention in many novels appears to follow a “long tail” distribution.The author then constructs an explanatory model in NetLogo, demonstrating that basic assumptions about plot structure are sufficient to generate output consistent with the real novels in the corpus.