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Found 12 papers, 3 papers with code
Overview of the First Workshop on Scholarly Document Processing (SDP)
To reach to the broader NLP and AI/ML community, pool distributed efforts and enable shared access to published research, we held the 1st Workshop on Scholarly Document Processing at EMNLP 2020 as a virtual event.
Valet: Rule-Based Information Extraction for Rapid Deployment
Arguing that rule-based information extraction is an important methodology early in the development cycle, we describe an experiment in which a VALET model is used to annotate examples for a machine learning extraction model.
Argument Mining for Scholarly Document Processing: Taking Stock and Looking Ahead
Argument mining targets structures in natural language related to interpretation and persuasion which are central to scientific communication.
Overview of the Second Workshop on Scholarly Document Processing
With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task.
Overview of the Third Workshop on Scholarly Document Processing
With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task.
Accelerating Human Authorship of Information Extraction Rules
We consider whether machine models can facilitate the human development of rule sets for information extraction.
Schema-Driven Information Extraction from Heterogeneous Tables
We use this collection of annotated tables to evaluate the ability of open-source and API-based language models to extract information from tables covering diverse domains and data formats.
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SynKB: Semantic Search for Synthetic Procedures
In this paper we present SynKB, an open-source, automatically extracted knowledge base of chemical synthesis protocols.
Overview and Results: CL-SciSumm Shared Task 2019
All papers are from the open access research papers in the CL domain.
Discourse-Wide Extraction of Assay Frames from the Biological Literature
We consider the problem of populating multi-part knowledge frames from textual information distributed over multiple sentences in a document.
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.
