Related papers: S2AND: A Benchmark and Evaluation System for Autho…
Author name disambiguation results are often evaluated by measures such as Cluster-F, K-metric, Pairwise-F, Splitting & Lumping Error, and B-cubed. Although these measures have distinctive evaluation schemes, this paper shows that they can…
Named Entity Disambiaguation (NED) is a central task for applications dealing with natural language text. Assume that we have a graph based knowledge base (subsequently referred as Knowledge Graph) where nodes represent various real world…
With thousands of academic articles shared on a daily basis, it has become increasingly difficult to keep up with the latest scientific findings. To overcome this problem, we introduce a new task of disentangled paper summarization, which…
In this work, we introduce a novel method for entity resolution author disambiguation in bibliographic networks. Such a method is based on a 2-steps network traversal using topological similarity measures for rating candidate nodes.…
Scholars have often relied on name initials to resolve name ambiguities in large-scale coauthorship network research. This approach bears the risk of incorrectly merging or splitting author identities. The use of initial-based…
Effective scientific communication depends on accurate citations that validate sources and guide readers to supporting evidence. Yet academic literature faces mounting challenges: semantic citation errors that misrepresent sources,…
Entity disambiguation (ED) is the task of linking mentions in text to corresponding entries in a knowledge base. Dual Encoders address this by embedding mentions and label candidates in a shared embedding space and applying a similarity…
There are a number of solutions that perform unsupervised name disambiguation based on the similarity of bibliographic records or common co-authorship patterns. Whether the use of these advanced methods, which are often difficult to…
Named Entity Recognition and Disambiguation (NERD) systems are foundational for information retrieval, question answering, event detection, and other natural language processing (NLP) applications. We introduce TweetNERD, a dataset of 340K+…
Author similarity and detection is an integral first step in detecting state-led disinformation campaigns in an automated fashion. Current detection techniques require an analyst or subject matter expert to hand-curate accounts. Stylometric…
In this paper, we present our work to support publishers and editors in finding descriptive tags for e-books through tag recommendations. We propose a hybrid tag recommendation system for e-books, which leverages search query terms from…
We present the problem of finding comparable researchers for any given researcher. This problem has many motivations. Firstly, know thyself. The answers of where we stand among research community and who we are most alike may not be easily…
Acronyms are the short forms of longer phrases and they are frequently used in writing, especially scholarly writing, to save space and facilitate the communication of information. As such, every text understanding tool should be capable of…
The disambiguation of author names is an important and challenging task in bibliometrics. We propose an approach that relies on an external source of information for selecting and validating clusters of publications identified through an…
Automatically locating named entities in natural language text - named entity recognition - is an important task in the biomedical domain. Many named entity mentions are ambiguous between several bioconcept types, however, causing text…
Accurate parsing of citations is necessary for machine-readable scholarly infrastructure. But, despite sustained interest in this problem, existing evaluation techniques are often not generalizable, based on synthetic data, or not publicly…
In real-world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesake of one another. Such mistakes deteriorate the performance of document…
As digital collections of scientific literature are widespread and used frequently in knowledge-intense working environments, it has become a challenge to identify author names correctly. The treatment of homonyms is crucial for the…
Machine learning models can perpetuate unintended biases from unfair and imbalanced datasets. Evaluating and debiasing these datasets and models is especially hard in text datasets where sensitive attributes such as race, gender, and sexual…
Entity resolution (record linkage, microclustering) systems are notoriously difficult to evaluate. Looking for a needle in a haystack, traditional evaluation methods use sophisticated, application-specific sampling schemes to find matching…