Titlebook

OMIT

Using Entities in?Knowledge Graph Hierarchies to?Classify Sensitive Information to the public. However, automatically classifying sensitive information is difficult, since sensitivity is often due to contextual knowledge that must be inferred from the text. For example, the mention of a specific named entity is unlikely to provide enough context to automatically know if the in

synovium

施加

MILL

Query Expansion, Argument Mining and?Document Scoring for?an?Efficient Question Answering Systemcomparative question by retrieving documents based only on traditional measures (such as TF-IDF and BM25) does not always satisfy the need. In this paper, we propose a multi-layer architecture to answer comparative questions based on arguments. Our approach consists of a pipeline of query expansion,

BUDGE

Transformer-Encoder-Based Mathematical Information Retrievalrieval systems should not only be able to process natural language, but also mathematical and scientific notation to retrieve documents..In this work, we evaluate two transformer-encoder-based approaches on a Question Answer retrieval task. Our pre-trained ALBERT-model demonstrated competitive perfo

FRONT

注意力集中

accessory

Tracking News Stories in?Short Messages in?the?Era of?Infodemic[.]), its impact on the results and why it is key to this type of work. We used a supervised algorithm proposed by Miranda et al. [.] and K-Means to provide evaluations for different use cases. We found that TF-IDF vectors are not always the best ones to group documents, and that algorithms are sens

寬宏大量

blister

Rhythmic and?Psycholinguistic Features for?Authorship Tasks in?the?Spanish Parliament: Evaluation anobtained by a BETO transformer, when the latter is trained on the original text, i.e., potentially learning from topical information. Moreover, we further investigate the results for the different authors, showing that variations in performance are partially explainable in terms of the authors’ poli