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Term extraction is an information extraction task at the root of knowledge discovery platforms. Developing term extractors that are able to generalize across very diverse and potentially highly technical domains is challenging, as annotations for domains r ...
Natural Language Processing (NLP) has become increasingly utilized to provide adaptivity in educational applications. However, recent research has highlighted a variety of biases in pre-trained language models. While existing studies investigate bias in di ...
We present the HIPE-2022 shared task on named entity processing in multilingual historical documents. Following the success of the first CLEF-HIPE-2020 evaluation lab, this edition confronts systems with the challenges of dealing with more languages, learn ...
Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...
Approaches for estimating the similarity between individual publications are an area of long -standing interest in the scientometrics and informetrics communities. Traditional techniques have generally relied on references and other metadata, while text mi ...
Remote sensing visual question answering (RQA) was recently proposed with the aim of interfacing natural language and vision to ease the access of information contained in Earth Observation data for a wide audience, which is granted by simple questions in ...
Most of the Natural Language Processing (NLP) algorithms involve use of distributed vector representations of linguistic units (primarily words and sentences) also known as embeddings in one way or another. These embeddings come in two flavours namely, sta ...
Machine Translation (MT) has made considerable progress in the past two decades, particularly after the introduction of neural network models (NMT). During this time, the research community has mostly focused on modeling and evaluating MT systems at the se ...
With the current exponential growth of video-based social networks, video retrieval using natural language is receiving ever-increasing attention. Most existing approaches tackle this task by extracting individual frame-level spatial features to represent ...
We discuss some properties of generative models for word embeddings. Namely, (Arora et al., 2016) proposed a latent discourse model implying the concentration of the partition function of the word vectors. This concentration phenomenon led to an asymptotic ...
