American writer Thomas Bulfinch was one of the most important scholars of world mythology. His nineteenth-century collections of folk literature from Europe and ancient Greece remain some of the most influential works in this field. The collection The Age of Chivalry focuses on romantic, action-packed tales from the King Arthur era.
For the first time, proper names are made the topic of a cross-linguistic account of morphosyntactic properties which formally distinguish place names, personal names, and common nouns. It is shown that the behaviour of place names and personal names in morphology and syntax frequently disagrees with the rules established for other word classes independent of the language’s genetic affiliation, grammatical structure, and geographic location. Place names and personal names each boast a grammar of their own. They are candidates for the status of a distinct word class. Their special grammar comes frequently to the fore in the domain of spatial and possessive relations. This fact is explained with reference to functional notions.
The book is based on a detailed corpus-based investigation of the structure of noun phrases (NPs) in Singaporean English and Kenyan English with the aim of detecting, on the one hand, typological effects from substrate languages and, on the other hand, simplification patterns known to play a role in such varieties.
For almost a century and a half, Bulfinch's Mythology has been the text by which the great tales of the gods and goddesses, Greek and Roman antiquity; Scandinavian, Celtic, and Oriental fables and myths; and the age of chivalry have been known. The stories are divided into three sections: The Age of Fable or Stories of Gods and Heroes (first published in 1855); The Age of Chivalry (1858), which contains King Arthur and His Knights, The Mabinogeon, and The Knights of English History; and Legends of Charlemagne or Romance of the Middle Ages (1863). For the Greek myths, Bulfinch drew on Ovid and Virgil, and for the sagas of the north, from Mallet's Northern Antiquities. He provides lively versions of the myths of Zeus and Hera, Venus and Adonis, Daphne and Apollo, and their cohorts on Mount Olympus; the love story of Pygmalion and Galatea; the legends of the Trojan War and the epic wanderings of Ulysses and Aeneas; the joys of Valhalla and the furies of Thor; and the tales of Beowulf and Robin Hood. The tales are eminently readable. As Bulfinch wrote, "Without a knowledge of mythology much of the elegant literature of our own language cannot be understood and appreciated. . . . Our book is an attempt to solve this problem, by telling the stories of mythology in such a manner as to make them a source of amusement.
Information Retrieval (IR) models are a core component of IR research and IR systems. The past decade brought a consolidation of the family of IR models, which by 2000 consisted of relatively isolated views on TF-IDF (Term-Frequency times Inverse-Document-Frequency) as the weighting scheme in the vector-space model (VSM), the probabilistic relevance framework (PRF), the binary independence retrieval (BIR) model, BM25 (Best-Match Version 25, the main instantiation of the PRF/BIR), and language modelling (LM). Also, the early 2000s saw the arrival of divergence from randomness (DFR). Regarding intuition and simplicity, though LM is clear from a probabilistic point of view, several people stated: "It is easy to understand TF-IDF and BM25. For LM, however, we understand the math, but we do not fully understand why it works." This book takes a horizontal approach gathering the foundations of TF-IDF, PRF, BIR, Poisson, BM25, LM, probabilistic inference networks (PIN's), and divergence-based models. The aim is to create a consolidated and balanced view on the main models. A particular focus of this book is on the "relationships between models." This includes an overview over the main frameworks (PRF, logical IR, VSM, generalized VSM) and a pairing of TF-IDF with other models. It becomes evident that TF-IDF and LM measure the same, namely the dependence (overlap) between document and query. The Poisson probability helps to establish probabilistic, non-heuristic roots for TF-IDF, and the Poisson parameter, average term frequency, is a binding link between several retrieval models and model parameters. Table of Contents: List of Figures / Preface / Acknowledgments / Introduction / Foundations of IR Models / Relationships Between IR Models / Summary & Research Outlook / Bibliography / Author's Biography / Index
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