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This paper addresses automatic extraction of abbreviations (encompassing acronyms and initialisms) and corresponding long-form expansions from plain unstructured text. We create and are going to release a multilingual resource for…
The increasing volume of scholarly publications requires advanced tools for efficient knowledge discovery and management. This paper introduces ongoing work on a system using Large Language Models (LLMs) for the semantic extraction of key…
Semi-structured content in HTML tables, lists, and infoboxes accounts for a substantial share of factual data on the web, yet the formatting complicates usage, and reliably extracting structured information from them remains challenging.…
This paper describes an abstractive summarization method for tabular data which employs a knowledge base semantic embedding to generate the summary. Assuming the dataset contains descriptive text in headers, columns and/or some augmenting…
Keyphrase extraction is the task of extracting a small set of phrases that best describe a document. Most existing benchmark datasets for the task typically have limited numbers of annotated documents, making it challenging to train…
An important characteristic of English written text is the abundance of noun compounds - sequences of nouns acting as a single noun, e.g., colon cancer tumor suppressor protein. While eventually mastered by domain experts, their…
Traditional approaches to extractive summarization rely heavily on human-engineered features. In this work we propose a data-driven approach based on neural networks and continuous sentence features. We develop a general framework for…
We present a hierarchical convolutional document model with an architecture designed to support introspection of the document structure. Using this model, we show how to use visualisation techniques from the computer vision literature to…
Semantic web information is at the extremities of long pipelines held by human beings. They are at the origin of information and they will consume it either explicitly because the information will be delivered to them in a readable way, or…
Wikipedia categories, a classification scheme built for organizing and describing Wikpedia articles, are being applied in computer science research. This paper adopts a systematic literature review approach, in order to identify different…
Data exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a novel language tool, in the form of a publicly available Python library for extracting patterns…
The number of publicly available Web services (WS) is continuously growing, and in parallel, we are witnessing a rapid development in semantic-related web technologies. The intersection of the semantic web and WS allows the development of…
Tracking users' activities on the World Wide Web (WWW) allows researchers to analyze each user's internet behavior as time passes and for the amount of time spent on a particular domain. This analysis can be used in research design, as…
Acronyms are omnipresent. They usually express information that is repetitive and well known. But acronyms can also be ambiguous because there can be multiple expansions for the same acronym. In this paper, we propose a general system for…
In this paper, we present a meta-analysis of several Web content extraction algorithms, and make recommendations for the future of content extraction on the Web. First, we find that nearly all Web content extractors do not consider a very…
A web crawler is a system designed to collect web pages, and efficient crawling of new pages requires appropriate algorithms. While website features such as XML sitemaps and the frequency of past page updates provide important clues for…
Mainstream knowledge management researchers generally agree that knowledge extracted from unstructured data and semi-structured data have become imperative for organizational strategic decision making. In this research, we develop a…
With the rapid development of Internet technology, people have more and more access to a variety of web page resources. At the same time, the current rapid development of deep learning technology is often inseparable from the huge amount of…
Term extraction is one of the layers in the ontology development process which has the task to extract all the terms contained in the input document automatically. The purpose of this process is to generate list of terms that are relevant…
Real applications of natural language document processing are very often confronted with domain specific lexical gaps during the analysis of documents of a new domain. This paper describes an approach for the derivation of domain specific…