Titlebook: Virtual, Distributed and Flexible Organisations; Studies in Organisat Kecheng Liu Book 2004 Springer Science+Business Media B.V. 2004 Analy

大洪水

craving

crutch

was the top performing headline generation system at DUC 2004. Topiary-style headlines consist of a number of general topic labels followed by a compressed version of the lead sentence of a news story. The Topiary system uses a statistical learning approach to finding topic labels for headlines, whi

偽造者

嗎啡

Ricardo R. Gudwint has become pervasive during the COVID-19 pandemic. Previous research [.] has approached this problem with standard classification technology using a set of features that have included linguistic and external variables, among others. In this work, we aim to replicate parts of the study conducted by

天然熱噴泉

Peter B?gh Andersenent state-of-the-art systems fail to consider the underlying semantics involved when rating an item. This in turn contributes to many false recommendations. These models hinder the possibility of explaining?. or .. In this paper, we develop an approach incorporating the underlying semantics involved

Maximize

Carlos Alberto Cocozza Simoni,Maria Cecília Calani Baranaukasdscape of memes, the realm of sarcastic memes holds a unique position with its foundation in irony, mockery, satire, and messages that diverge from literal meanings. Detecting sarcasm in memes is challenging due to the intricate interplay between sarcasm and humor. While prior research has primarily

Radiation

Wernickes-area

Pierre-Jean Charrelromote, represent crucial challenges for research and industry. However, the current lack of automatic methods to comprehensively measure or increase viewpoint diversity in search results complicates the understanding and mitigation of such effects. This paper proposes a viewpoint bias metric that e

佛刊

Elena Revilla,José Sánchez-Alarcosaper, we provide an overview . the TDT research program from its inception to the third phrase that is now underway. We also discuss our approach to two of the TDT problems in detail. For event clustering (Detection), we show that classic Information Retrieval clustering techniques can be modified s