Autodive: An Integrated Onsite Scientific Literature Annotation Tool (2023.acl-demo)
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| Challenge: | Annotating scientific literature directly on PDF documents can greatly improve the labeling efficiency of scientists whose annotation costs are very high. |
| Approach: | They propose an integrated onsite scientific literature annotation tool for natural scientists and Natural Language Processing (NLP) researchers. |
| Outcome: | The proposed tool supports the whole lifecycle of corpus generation including i)project management, ii)resource management, and iv)ontology management, as well as manual annotation, onsite auto annotation, and vi)task statistic. |
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Embedding-based Scientific Literature Discovery in a Text Editor Application (2020.acl-demos)
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| Challenge: | Despite the availability of powerful search engines and text editing software, discovering relevant papers and integrating the knowledge into a manuscript remain complex tasks associated with high cognitive load. |
| Approach: | They propose to combine text editing and literature discovery in an interactive user interface with a search engine that couples Boolean keyword filtering with nearest neighbor search over text embeddings. |
| Outcome: | The proposed application combines text editing and literature discovery in an interactive user interface. |
FITAnnotator: A Flexible and Intelligent Text Annotation System (2021.naacl-demos)
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| Challenge: | In this paper, we introduce FITAnnotator, a generic web-based tool for efficient text annotation. |
| Approach: | They propose a generic web-based tool for efficient text annotation. |
| Outcome: | The proposed tool is based on a fully modular architecture and provides three kinds of interfaces to annotate instances, evaluate annotation quality and manage the annotation task for annotators, reviewers and managers. |
SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation (2023.acl-demo)
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| Challenge: | Scientific writing involves retrieving, summarizing, and citing relevant papers. |
| Approach: | They propose a pipeline that automatically recommends relevant papers, extracts highlights, and suggests a reference sentence as a citation of a paper. |
| Outcome: | The proposed pipeline recommends relevant papers from large databases of hundreds of millions of papers . it provides extractive summaries and abstractively-generated citation sentences . authors question whether it is possible to partly automate this process to reduce cognitive load . |
TextAnnotator: A UIMA Based Tool for the Simultaneous and Collaborative Annotation of Texts (2020.lrec-1)
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| Challenge: | Existing annotation tools are not efficient for the annotation of corpora and are not error-free. |
| Approach: | They propose to extend existing annotation tools by evaluating their flexibility and efficiency. |
| Outcome: | The proposed system performs platform-independent multimodal annotations and annotates complex textual structures. |
AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation (2026.eacl-tutorials)
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| Challenge: | This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process. |
| Approach: | This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process. |
| Outcome: | This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process. |
NLATool: an Application for Enhanced Deep Text Understanding (C18-2)
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Markus Gärtner, Sven Mayer, Valentin Schwind, Eric Hämmerle, Emine Turcan, Florin Rheinwald, Gustav Murawski, Lars Lischke, Jonas Kuhn
| Challenge: | a wide range of subfields in natural language processing see systems solving their tasks with sufficiently high-quality levels. |
| Approach: | They propose a web application that supports text annotation and enriches the text with additional information from a number of sources directly within the application. |
| Outcome: | The proposed web application is based on a human-centered design process . it offers a rich visualization of texts and the entities mentioned in them through an easy to use interface. |
Related Work and Citation Text Generation: A Survey (2024.emnlp-main)
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| Challenge: | Academic research paper authors must perform literature review to compare work with prior work . authors must compose coherent story that connects prior work and current work based on author's understanding of field . |
| Approach: | They propose to use automatic related work generation (RWG) to generate papers . authors summarize key approaches and define tasks in a zoo of historical works . |
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BioReddit: Word Embeddings for User-Generated Biomedical NLP (D19-62)
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| Challenge: | a corpus of medical-themed posts was scrapped from Reddit to train word embeddings on downstream tasks. |
| Approach: | They propose to train word embeddings from a corpus of medical forums from reddit scrapping posts from medical-themed subreddits. |
| Outcome: | The proposed system outperforms embeddings trained on general purpose data or on scientific papers when applied on user-generated content. |
Infrastructure for Semantic Annotation in the Genomics Domain (2020.lrec-1)
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Mahmoud El-Haj, Nathan Rutherford, Matthew Coole, Ignatius Ezeani, Sheryl Prentice, Nancy Ide, Jo Knight, Scott Piao, John Mariani, Paul Rayson, Keith Suderman
| Challenge: | a novel infrastructure for biomedical text mining combines NLP and corpus linguistics methods to provide a comprehensive corpus for literature-based discovery. |
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| Outcome: | The proposed infrastructure allows for extreme-scale research on the open access PubMed Central archive. |
A Multi-level Annotated Corpus of Scientific Papers for Scientific Document Summarization and Cross-document Relation Discovery (2020.lrec-1)
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| Challenge: | Recent studies have proposed to take advantage of the scientific paper's citation network to approach literature summarization. |
| Approach: | They propose to annotate related work sections, cite papers and sentences using machine readable data and an additional layer of papers citing the references. |
| Outcome: | The proposed corpus expands the existing data-set of related work sections and cites the papers cited in the related work section. |