The Congress Viewer (years 1900 - 2000), a prototype text mining app, demonstrates the potential of tools designed to measure lexical changes, including advanced NLP techniques like parsing and analyzing grammatical relationships. This app can increase transparency in Congress while also providing new insights into the evolution and nature of political language across various contexts, including different time periods and discourse communities.
Use an array of data-mining and statistical approaches to gain new insights into the evolution and nature of political language as it occurs in different time periods and in different contexts.
AI agents can personalize education by identifying students’ strengths, weaknesses, and personalities to generate content tailored to them. This work presents "personalized education agents" deployed in an educational version of Minecraft. Agents bridge concepts from lessons to "big picture" thinking by creating connections between STEM and interdisciplinary topics, such as the Language Arts. Agents translate student progression and learning outcomes to teachers for their assessment of student progress.
Computational methods allow researchers to systematically analyze and interpret large volumes of social, political, and cultural data, uncovering underlying patterns and insights at scale. These course materials, made for the Santa Fe Institute, are designed to equip humanities researchers with computational and quantitative tools. The course aims to foster a supportive community, build practical skills, and diversify the field of humanities analytics by welcoming participants from various backgrounds and stages of their academic careers.
Computational methods are changing the way that we access information about history and society. These methods help us to detect change over time, to identify influential figures, and to name turning points. What happens when we apply these tools to the entire Hansard corpus or to a million congressional debates and tweets? This work provides an introduction to the analytic methodologies transforming the humanities and social sciences via a book, under contract at Cambridge University Press, and series of Jupyter Notebooks aimed at exploring questions like these.
PANGeA is a system that uses large language models (LLMs) to create narrative content for turn-based RPGs based on game designers' high-level criteria. It introduces a novel validation system for handling free-form text input during development and gameplay, employing "self-reflection" techniques, enabling small/local LLMs to perform comparably to foundational models. It enriches player-NPC interactions by generating personality-biased non-playable characters (NPCs). It improves AI accuracy through crowdsourcing mechanics. PANGeA houses a server with a custom memory system that provides context for LLM generation. The server's REST interface enables integration with any game engine.
Knowledge graphs (KGs) can augment large language models (LLMs) while also providing an explainable set of facts that can be inspected by a human. Explainability is valuable for fields that may otherwise avoid LLMs due to hallucinations, such as human trafficking analysis. Creating KGs poses challenges, however. KGs parsed from documents may include explicit connections (those directly stated in a document) but miss implicit connections (those evident to a human, but not directly stated). This research introduces GAME-KG, an approach to modifying explicit and implicit KG connections by crowdsourcing feedback through video games.
Dark Shadows is a film-noir style detective thriller that acts as a test bed for proof-of-concept and prototype system components, frameworks, and models that contribute to research in AI and machine learning. The gameplay focuses on social scenarios where players provide natural language input to progress the narrative. Dark Shadows includes PANGeA’s novel validation system, which leverages self-reflection to evoke a large language model's (LLM) intelligence when evaluating and responding to user input. Narrative and artwork are procedurally generated.
Computational methods allow researchers to systematically analyze and interpret large volumes of social, political, and cultural data, uncovering underlying patterns and insights at scale. These course materials, made for the Santa Fe Institute, are designed to equip humanities researchers with computational and quantitative tools. The course aims to foster a supportive community, build practical skills, and diversify the field of humanities analytics by welcoming participants from various backgrounds and stages of their academic careers.
The Congress Viewer (years 1900 - 2000), a prototype text mining app, demonstrates the potential of tools designed to measure lexical changes, including advanced NLP techniques like parsing and analyzing grammatical relationships. This app can increase transparency in Congress while also providing new insights into the evolution and nature of political language across various contexts, including different time periods and discourse communities.
Use an array of data-mining and statistical approaches to gain new insights into the evolution and nature of political language as it occurs in different time periods and in different contexts.
Open government data enables citizens to see government activities and decision-making processes, empowering them to participate more fully in the democratic process. Researchers can use open government data for studies and projects, generating insights and contributing to evidence-based policy-making. To this end, this work introduces R packages hosted by the rOpengov Universe that are designed to make analyzing contemporary and historical open government data more accessible. Queried data is returned in a clean and analysis-ready dataframe.
Computational methods are changing the way that we access information about history and society. These methods help us to detect change over time, to identify influential figures, and to name turning points. What happens when we apply these tools to the entire Hansard corpus or to a million congressional debates and tweets? This work provides an introduction to the analytic methodologies transforming the humanities and social sciences via a book, under contract at Cambridge University Press, and series of Jupyter Notebooks aimed at exploring questions like these.
This paper describes a method of triples extraction, posextract, which has been designed to meet the increasing need for high-accuracy triples outputs for the analysis of text.
The Congress Viewer (years 1900 - 2000), a prototype text mining app, demonstrates the potential of tools designed to measure lexical changes, including advanced NLP techniques like parsing and analyzing grammatical relationships. This app can increase transparency in Congress while also providing new insights into the evolution and nature of political language across various contexts, including different time periods and discourse communities.
Use an array of data-mining and statistical approaches to gain new insights into the evolution and nature of political language as it occurs in different time periods and in different contexts.
Open government data enables citizens to see government activities and decision-making processes, empowering them to participate more fully in the democratic process. Researchers can use open government data for studies and projects, generating insights and contributing to evidence-based policy-making. To this end, this work introduces R packages hosted by the rOpengov Universe that are designed to make analyzing contemporary and historical open government data more accessible. Queried data is returned in a clean and analysis-ready dataframe.
Computational methods are changing the way that we access information about history and society. These methods help us to detect change over time, to identify influential figures, and to name turning points. What happens when we apply these tools to the entire Hansard corpus or to a million congressional debates and tweets? This work provides an introduction to the analytic methodologies transforming the humanities and social sciences via a book, under contract at Cambridge University Press, and series of Jupyter Notebooks aimed at exploring questions like these.
PANGeA is a system that uses large language models (LLMs) to create narrative content for turn-based RPGs based on game designers' high-level criteria. It introduces a novel validation system for handling free-form text input during development and gameplay, employing "self-reflection" techniques, enabling small/local LLMs to perform comparably to foundational models. It enriches player-NPC interactions by generating personality-biased non-playable characters (NPCs). It improves AI accuracy through crowdsourcing mechanics. PANGeA houses a server with a custom memory system that provides context for LLM generation. The server's REST interface enables integration with any game engine.
Knowledge graphs (KGs) can augment large language models (LLMs) while also providing an explainable set of facts that can be inspected by a human. Explainability is valuable for fields that may otherwise avoid LLMs due to hallucinations, such as human trafficking analysis. Creating KGs poses challenges, however. KGs parsed from documents may include explicit connections (those directly stated in a document) but miss implicit connections (those evident to a human, but not directly stated). This research introduces GAME-KG, an approach to modifying explicit and implicit KG connections by crowdsourcing feedback through video games.
Dark Shadows is a film-noir style detective thriller that acts as a test bed for proof-of-concept and prototype system components, frameworks, and models that contribute to research in AI and machine learning. The gameplay focuses on social scenarios where players provide natural language input to progress the narrative. Dark Shadows includes PANGeA’s novel validation system, which leverages self-reflection to evoke a large language model's (LLM) intelligence when evaluating and responding to user input. Narrative and artwork are procedurally generated.
Knowledge graphs (KGs) can augment large language models (LLMs) while also providing an explainable set of facts that can be inspected by a human. Explainability is valuable for fields that may otherwise avoid LLMs due to hallucinations, such as human trafficking analysis. Creating KGs poses challenges, however. KGs parsed from documents may include explicit connections (those directly stated in a document) but miss implicit connections (those evident to a human, but not directly stated). This research introduces GAME-KG, an approach to modifying explicit and implicit KG connections by crowdsourcing feedback through video games.
PANGeA is a system that uses large language models (LLMs) to create narrative content for turn-based RPGs based on game designers' high-level criteria. It introduces a novel validation system for handling free-form text input during development and gameplay, employing "self-reflection" techniques, enabling small/local LLMs to perform comparably to foundational models. It enriches player-NPC interactions by generating personality-biased non-playable characters (NPCs). It improves AI accuracy through crowdsourcing mechanics. PANGeA houses a server with a custom memory system that provides context for LLM generation. The server's REST interface enables integration with any game engine.
Dark Shadows is a film-noir style detective thriller that acts as a test bed for proof-of-concept and prototype system components, frameworks, and models that contribute to research in AI and machine learning. The gameplay focuses on social scenarios where players provide natural language input to progress the narrative. Dark Shadows includes PANGeA’s novel validation system, which leverages self-reflection to evoke a large language model's (LLM) intelligence when evaluating and responding to user input. Narrative and artwork are procedurally generated.
Knowledge graphs (KGs) can augment large language models (LLMs) while also providing an explainable set of facts that can be inspected by a human. Explainability is valuable for fields that may otherwise avoid LLMs due to hallucinations, such as human trafficking analysis. Creating KGs poses challenges, however. KGs parsed from documents may include explicit connections (those directly stated in a document) but miss implicit connections (those evident to a human, but not directly stated). This research introduces GAME-KG, an approach to modifying explicit and implicit KG connections by crowdsourcing feedback through video games.
AI agents can personalize education by identifying students’ strengths, weaknesses, and personalities to generate content tailored to them. This work presents "personalized education agents" deployed in an educational version of Minecraft. Agents bridge concepts from lessons to "big picture" thinking by creating connections between STEM and interdisciplinary topics, such as the Language Arts. Agents translate student progression and learning outcomes to teachers for their assessment of student progress.
PANGeA is a system that uses large language models (LLMs) to create narrative content for turn-based RPGs based on game designers' high-level criteria. It introduces a novel validation system for handling free-form text input during development and gameplay, employing "self-reflection" techniques, enabling small/local LLMs to perform comparably to foundational models. It enriches player-NPC interactions by generating personality-biased non-playable characters (NPCs). It improves AI accuracy through crowdsourcing mechanics. PANGeA houses a server with a custom memory system that provides context for LLM generation. The server's REST interface enables integration with any game engine.
Knowledge graphs (KGs) can augment large language models (LLMs) while also providing an explainable set of facts that can be inspected by a human. Explainability is valuable for fields that may otherwise avoid LLMs due to hallucinations, such as human trafficking analysis. Creating KGs poses challenges, however. KGs parsed from documents may include explicit connections (those directly stated in a document) but miss implicit connections (those evident to a human, but not directly stated). This research introduces GAME-KG, an approach to modifying explicit and implicit KG connections by crowdsourcing feedback through video games.
Computational methods allow researchers to systematically analyze and interpret large volumes of social, political, and cultural data, uncovering underlying patterns and insights at scale. These course materials, made for the Santa Fe Institute, are designed to equip humanities researchers with computational and quantitative tools. The course aims to foster a supportive community, build practical skills, and diversify the field of humanities analytics by welcoming participants from various backgrounds and stages of their academic careers.
The Congress Viewer (years 1900 - 2000), a prototype text mining app, demonstrates the potential of tools designed to measure lexical changes, including advanced NLP techniques like parsing and analyzing grammatical relationships. This app can increase transparency in Congress while also providing new insights into the evolution and nature of political language across various contexts, including different time periods and discourse communities.
Use an array of data-mining and statistical approaches to gain new insights into the evolution and nature of political language as it occurs in different time periods and in different contexts.
Computational methods are changing the way that we access information about history and society. These methods help us to detect change over time, to identify influential figures, and to name turning points. What happens when we apply these tools to the entire Hansard corpus or to a million congressional debates and tweets? This work provides an introduction to the analytic methodologies transforming the humanities and social sciences via a book, under contract at Cambridge University Press, and series of Jupyter Notebooks aimed at exploring questions like these.
This paper describes a method of triples extraction, posextract, which has been designed to meet the increasing need for high-accuracy triples outputs for the analysis of text.
PANGeA is a system that uses large language models (LLMs) to create narrative content for turn-based RPGs based on game designers' high-level criteria. It introduces a novel validation system for handling free-form text input during development and gameplay, employing "self-reflection" techniques, enabling small/local LLMs to perform comparably to foundational models. It enriches player-NPC interactions by generating personality-biased non-playable characters (NPCs). It improves AI accuracy through crowdsourcing mechanics. PANGeA houses a server with a custom memory system that provides context for LLM generation. The server's REST interface enables integration with any game engine.
Knowledge graphs (KGs) can augment large language models (LLMs) while also providing an explainable set of facts that can be inspected by a human. Explainability is valuable for fields that may otherwise avoid LLMs due to hallucinations, such as human trafficking analysis. Creating KGs poses challenges, however. KGs parsed from documents may include explicit connections (those directly stated in a document) but miss implicit connections (those evident to a human, but not directly stated). This research introduces GAME-KG, an approach to modifying explicit and implicit KG connections by crowdsourcing feedback through video games.
Dark Shadows is a film-noir style detective thriller that acts as a test bed for proof-of-concept and prototype system components, frameworks, and models that contribute to research in AI and machine learning. The gameplay focuses on social scenarios where players provide natural language input to progress the narrative. Dark Shadows includes PANGeA’s novel validation system, which leverages self-reflection to evoke a large language model's (LLM) intelligence when evaluating and responding to user input. Narrative and artwork are procedurally generated.
AI agents can personalize education by identifying students’ strengths, weaknesses, and personalities to generate content tailored to them. This work presents "personalized education agents" deployed in an educational version of Minecraft. Agents bridge concepts from lessons to "big picture" thinking by creating connections between STEM and interdisciplinary topics, such as the Language Arts. Agents translate student progression and learning outcomes to teachers for their assessment of student progress.
