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Large language models have led to remarkable progress on many NLP tasks, and researchers are turning to ever-larger text corpora to train them. Some of the largest corpora available are made by scraping significant portions of the internet,…

Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this…

Understanding and analyzing big data is firmly recognized as a powerful and strategic priority. For deeper interpretation of and better intelligence with big data, it is important to transform raw data (unstructured, semi-structured and…

The size of web has increased exponentially over the past few years with thousands of documents related to a subject available to the user. With this much amount of information available, it is not possible to take the full advantage of the…

With the fast growth of the Internet, more and more information is available on the Web. The Semantic Web has many features which cannot be handled by using the traditional search engines. It extracts metadata for each discovered Web…

Large language models (LLMs) have demonstrated remarkable capabilities, but their success heavily relies on the quality of pretraining corpora. For Chinese LLMs, the scarcity of high-quality Chinese datasets presents a significant…

The rapid growth of scientific literature has made it increasingly difficult for researchers to efficiently discover, evaluate, and synthesize relevant work. Recent advances in multi-agent large language models (LLMs) have demonstrated…

Many disciplines pose natural-language research questions over large document collections whose answers typically require structured evidence, traditionally obtained by manually designing an annotation schema and exhaustively labeling the…

Tables in scientific papers contain a wealth of valuable knowledge for the scientific enterprise. To help the many of us who frequently consult this type of knowledge, we present Tab2Know, a new end-to-end system to build a Knowledge Base…

The exponential growth of scientific literature requires effective management and extraction of valuable insights. While existing scientific search engines excel at delivering search results based on relational databases, they often neglect…

Blogs are undoubtedly the richest source of information available in cyberspace. Blogs can be of various natures i.e. personal blogs which contain posts on mixed issues or blogs can be domain specific which contains posts on particular…

In the rapidly evolving field of artificial intelligence (AI), mapping innovation patterns and understanding effective technology transfer from research to applications are essential for economic growth. However, existing data…

The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping…

The popularity and rapid development of Cloud Computing in recent years has led to a vast number of publications capturing the accumulated knowledge in this field. Due to the interdisciplinary nature and significant relevance of cloud…

Can a Web crawler efficiently locate an unknown relevant page? While this question is receiving much empirical attention due to its considerable commercial value in the search engine community [Cho98,Chakrabarti99,Menczer00,Menczer01],…

Many active learning and search approaches are intractable for large-scale industrial settings with billions of unlabeled examples. Existing approaches search globally for the optimal examples to label, scaling linearly or even…

Over the past few decades, the amount of scientific articles and technical literature has increased exponentially in size. Consequently, there is a great need for systems that can ingest these documents at scale and make their content…

As the information contained within the web is increasing day by day, organizing this information could be a necessary requirement.The data mining process is to extract information from a data set and transform it into an understandable…

Motivation: In recent years, image-based biological assays have steadily become high-throughput, sparking a need for fast automated methods to extract biologically-meaningful information from hundreds of thousands of images. Taking…

Recent regulatory initiatives like the European AI Act and relevant voices in the Machine Learning (ML) community stress the need to describe datasets along several key dimensions for trustworthy AI, such as the provenance processes and…