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We present the HIPE-2022 shared task on named entity processing in multilingual historical documents.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 ...

We present an approach for the prediction of user authorship and feedback behavior with shared content. We consider that users use models of other users and their feedback to choose what to publish next. We look at the problem as a game between ...

Multi-hop machine reading comprehension (MRC) requires models to mine and utilize relevant information from multiple documents to predict the answer to a semantically related question. Existing work resorts to either document-level or entity-level ...

Since its introduction some twenty years ago, named entity (NE) processing has become an essential component of virtually any text mining application and has undergone major changes. Recently, two main trends characterise its developments: the ...

One undergraduate student’s strategy to deal with long assignment instructions is to black out all of the information that they deem to be unimportant in the text, allowing them to focus just on the “important” information. While this technique ...

The correct definition and recognition of sentences is the basis of NLP. For the characteristics of Chinese text structure, the theory of NT clause was proposed from the perspective of micro topics. Based on this theory, this paper proposes a ...

While studies investigating the semantic similarity among concepts, sentences and short text fragments have been fruitful, the problem of document-level semantic matching remains largely unexplored due to its complexity. In this paper, we explore ...

Machine Reading and Comprehension recently has drawn a fair amount of attention in the field of natural language processing. In this paper, we consider integrating side information to improve machine comprehension on answering cloze-style questions more ...

This paper presents an outline of an Ontological and Semantic understanding-based model (SEMONTOQA) for an open-domain factoid Question Answering (QA) system. The outlined model analyses unstructured English natural language texts to a vast extent and ...

Automatic knowledge acquisition from texts is one of the challenges of the information society that can only be mastered by technical means.

While the syntactic analysis of isolated sentences is relatively well understood, the problem of automatically ...

This article advances a strategic proposal that would enable future research in data/knowledge engineering and natural language processing to take a broader range of meanings into account than can be derived from analyzing text with current methods ...

Natural scene images usually contain varying colors which make segmentation more difficult. Without any a priori knowledge of degradations and based on physical light reflectance, we propose a selective metric-based clustering to extract textual ...