Related papers: Entity Ranking in Wikipedia
Document indexation is an essential task achieved by archivists or automatic indexing tools. To retrieve relevant documents to a query, keywords describing this document have to be carefully chosen. Archivists have to find out the right…
Comprehending an article requires understanding its constituent events. However, the context where an event is mentioned often lacks the details of this event. A question arises: how can the reader obtain more knowledge about this…
The task of expert finding has been getting increasing attention in information retrieval literature. However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence in an optimal…
In document-level relation extraction, entities may appear multiple times in a document, and their relationships can shift from one context to another. Accurate prediction of the relationship between two entities across an entire document…
Answering multiple-choice questions in a setting in which no supporting documents are explicitly provided continues to stand as a core problem in natural language processing. The contribution of this article is two-fold. First, it describes…
Attribution and fact verification are critical challenges in natural language processing for assessing information reliability. While automated systems and Large Language Models (LLMs) aim to retrieve and select concise evidence to support…
How different cultures evaluate a person? Is an important person in one culture is also important in the other culture? We address these questions via ranking of multilingual Wikipedia articles. With three ranking algorithms based on…
This article presents the application of the Universal Named Entity framework to generate automatically annotated corpora. By using a workflow that extracts Wikipedia data and meta-data and DBpedia information, we generated an English…
In tasks like question answering or text summarisation, it is essential to have background knowledge about the relevant entities. The information about entities - in particular, about long-tail or emerging entities - in publicly available…
Organizations generate vast amounts of interconnected content across various platforms. While language models enable sophisticated reasoning for use in business applications, retrieving and contextualizing information from organizational…
Named entity recognition (NER) is one of the tasks in natural language processing that can greatly benefit from the use of external knowledge sources. We propose a named entity recognition framework composed of knowledge-based feature…
This paper presents a new user feedback mechanism based on Wikipedia concepts for interactive retrieval. In this mechanism, the system presents to the user a group of Wikipedia concepts, and the user can choose those relevant to refine…
Entity recognition provides semantic access to ancient materials in the Digital Humanities: itexposes people and places of interest in texts that cannot be read exhaustively, facilitates linkingresources and can provide a window into text…
We introduce the task of entity-centric query refinement. Given an input query whose answer is a (potentially large) collection of entities, the task output is a small set of query refinements meant to assist the user in efficient domain…
After decades of massive digitisation, an unprecedented amount of historical documents is available in digital format, along with their machine-readable texts. While this represents a major step forward with respect to preservation and…
Traditional information retrieval systems represent documents and queries by keyword sets. However, the content of a document or a query is mainly defined by both keywords and named entities occurring in it. Named entities have ontological…
A large amount of information in today's world is now stored in knowledge bases. Named Entity Recognition (NER) is a process of extracting, disambiguation, and linking an entity from raw text to insightful and structured knowledge bases.…
Tourism information is scattered around nowadays. To search for the information, it is usually time consuming to browse through the results from search engine, select and view the details of each accommodation. In this paper, we present a…
Entity disambiguation, or mapping a phrase to its canonical representation in a knowledge base, is a fundamental step in many natural language processing applications. Existing techniques based on global ranking models fail to capture the…
Entity linking, the task of mapping textual mentions to known entities, has recently been tackled using contextualized neural networks. We address the question whether these results -- reported for large, high-quality datasets such as…