相关论文: Leveraging Subjective Human Annotation for Cluster…
Digitization projects in humanities often generate vast quantities of page images from historical documents, presenting significant challenges for manual sorting and analysis. These archives contain diverse content, including various text…
The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models. However, datasets often contain noisy data inadvertently included during the construction process. Numerous attempts have been…
With the uptake of algorithmic personalization in the news domain, news organizations increasingly trust automated systems with previously considered editorial responsibilities, e.g., prioritizing news to readers. In this paper we study an…
To support efficient, balanced news consumption, merging articles from diverse sources into one, potentially through crowdsourcing, could alleviate some hurdles. However, the merging process could also impact annotators' attitudes towards…
The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize…
Historians and archivists often find and analyze the occurrences of query words in newspaper archives, to help answer fundamental questions about society. But much work in text analytics focuses on helping people investigate other textual…
Local explanation methods highlight the input tokens that have a considerable impact on the outcome of classifying the document at hand. For example, the Anchor algorithm applies a statistical analysis of the sensitivity of the classifier…
The escalating volume of academic literature presents a formidable challenge in staying updated with the newest research developments. Addressing this, this study introduces a pioneering AI-based tool, configured specifically to streamline…
With the widespread use of the internet, it has become increasingly crucial to extract specific information from vast amounts of academic articles efficiently. Data mining techniques are generally employed to solve this issue. However, data…
Text document classification is an important task for diverse natural language processing based applications. Traditional machine learning approaches mainly focused on reducing dimensionality of textual data to perform classification. This…
Optical character recognition (OCR) is crucial for a deeper access to historical collections. OCR needs to account for orthographic variations, typefaces, or language evolution (i.e., new letters, word spellings), as the main source of…
[Context] Researchers analyze underground forums to study abuse and cybercrime activities. Due to the size of the forums and the domain expertise required to identify criminal discussions, most approaches employ supervised machine learning…
In this work, in which we build on, and use the outcome of, an earlier study on topic identification in an algorithmically constructed publication-level classification (ACPLC), we address the issue how to algorithmically obtain a…
Content annotation at scale remains challenging, requiring substantial human expertise and effort. This paper presents a case study in code documentation analysis, where we explore the balance between automation efficiency and annotation…
It is desirable to coarsely classify short scientific texts, such as grant or publication abstracts, for strategic insight or research portfolio management. These texts efficiently transmit dense information to experts possessing a rich…
As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic work. We address this challenge by presenting a scalable pipeline for automating grammatical…
Optical character recognition (OCR) is a vital process that involves the extraction of handwritten or printed text from scanned or printed images, converting it into a format that can be understood and processed by machines. This enables…
Finding related published articles is an important task in any science, but with the explosion of new work in the biomedical domain it has become especially challenging. Most existing methodologies use text similarity metrics to identify…
Text Categorization (TC), also known as Text Classification, is the task of automatically classifying a set of text documents into different categories from a predefined set. If a document belongs to exactly one of the categories, it is a…
The analysis of historical documents is still a topical issue given the importance of information that can be extracted and also the importance given by the institutions to preserve their heritage. The main idea in order to characterize the…