Author: John Niekrasz
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Developing an AI-Supported Approach to Identify Instructional Groupings in Early Childhood Education Classrooms
Abstract High-quality early childhood classrooms provide children with a nurturing environment to develop their physical, social, and academic capabilities. Understanding how instructional time is organized in pre-K classrooms is essential for supporting high-quality teaching and coaching. This technical white paper examines the feasibility of using AI-supported approaches to automatically identify instructional groupings (e.g., small group,…
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Developing an AI Model to Identify Math and Literacy Instruction in Early Childhood Education Classrooms
Abstract High-quality early childhood classrooms provide children with a nurturing environment to develop their physical, social, and academic capabilities. Accurately identifying when instruction occurs in early childhood classrooms is essential for supporting high-quality teaching and understanding children’s learning experiences. This technical white paper explores the feasibility of using AI and classroom video to automatically detect…
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Accelerating Human Authorship of Information Extraction Rules
We simulate the process of corpus review and word list creation, showing that several simple interventions greatly improve recall as a function of simulated labor.
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Feature Derivation for Exploitation of Distant Annotation via Pattern Induction against Dependency Parses
We consider the use of distant supervision for biological information extraction, and introduce two understudied corpora of this form, the Biological Expression Language (BEL) Large Corpus and the Pathway Logic (PL) Datum Corpus.
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Assessing Problem-Solving Process At Scale
This paper describes a hybrid approach to assessing process at scale in the context of the use of computational thinking practices during programming.
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An Annotated Corpus and Method for Analysis of Ad-Hoc Structures Embedded in Text
We describe a method for identifying and performing functional analysis of structured regions that are embedded in natural language documents, such as tables or key-value lists.
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Unsupervised Discovery and Extraction of Semi-Structured Regions in Text Via Self-Information
We present initial work that uses significant patterns to generate extraction rules, and conclude with a discussion of future directions of our work.
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A corpus of online discussions for research into linguistic memes
We describe a 460-million word corpus of online discussions.
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Unbiased discourse segmentation evaluation
We show that the performance measures Pk and Window Diff, commonly used for discourse, topic, and story segmentation evaluation, are biased in favor of segmentations with fewer or adjacent segment boundaries.
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The CALO meeting assistant system
This paper presents the CALO-MA architecture and its speech recognition and understanding components.
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Annotating Participant Reference in English Spoken Conversation
We present a method for annotating verbal reference to people in conversational speech, with a focus on reference to conversation participants.
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Participant Subjectivity and Involvement As a Basis for Discourse Segmentation
We propose a framework for analyzing episodic conversational activities in terms of expressed relationships between the participants and utterance content.