Titlebook: Embeddings in Natural Language Processing; Theory and Advances Mohammad Taher Pilehvar,Jose Camacho-Collados Book 2021 Springer Nature Swi

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hermetic

Conclusions, a massive boost. Neural networks provided an efficient way for processing large amounts of texts and for directly computing dense compact representations. Since then, the term . has been almost fully substituted by their dense version, called .. This development path has revolutionalized other fiel

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Obstruction

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Hui Lu,Zheng Li,Mengqi Li,Deqiang Duanmutransform computers into “intelligent” agents. By intelligence here we do not necessarily mean an extraordinary level of smartness; it often involves basic problems that humans solve frequently in their day-to-day lives. This can be as simple as recognizing faces in an image, driving a car, playing

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https://doi.org/10.1007/978-90-481-9769-9imensional space and how distances in this space can denote the similarities between objects. However, we did not discuss how these spaces are constructed. In other words, the following question remained unanswered: how can we place hundreds of thousands of words in a space such that their positioni

CLEFT

https://doi.org/10.1007/978-90-481-9735-4ccurrence and semantic networks, citation networks, telecommunication networks, molecular graph structures, and biological networks. Therefore, analyzing them can play a central role in various real-world scenarios, such as drug design, friendship recommendation in social networks, semantic modeling

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Aleksandra Sklodowska,Renata Matlakowskaling with natural language, understanding longer units of meaning such as sentences. and documents is crucial. While this chapter is not covered as exhaustively as the other chapters, we still provide a basic overview and pointers so the reader can delve into these topics on their own.