Topic
Phrase
About: Phrase is a research topic. Over the lifetime, 12580 publications have been published within this topic receiving 317823 citations. The topic is also known as: syntagma & phrases.
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TL;DR: The authors showed that comprehenders are sensitive to the frequencies of compositional four-word phrases (e.g. don't have to worry) and that more frequent phrases are processed faster.
492 citations
Book•
02 Aug 2004
TL;DR: The Turkish Alphabet and writing conventions as discussed by the authors have been observed in the Turkish language since the early nineties and have been studied extensively in the Middle Ages and the early 1990s.
Abstract: Acknowledgements. Introduction. Abbreviations. List of Conventions Observed in this Book. The Turkish Alphabet and Writing Conventions. Part 1: Phonology: The Sound System 1. Phonological Units 2. Sound Changes in the Stem after Suffixiation 3. Vowel Harmony 4. Word Stress 5. Intonation Part 2. Morphology: The Strucutre of Words 6. Principles of Suffixiation 7. Word Classes and Derivational Suffixes 9. Reduplication 10. Noun Compounds Part 3. Syntax: The Structure of Sentences 12. Simple and Complex Sentences 13. The Verb Phrase 14. The Noun Phrase 15. Adjectival Constructions, Determiners and Numerals 16. Adverbal Constructions 17. The Postpositional Phrase 18. Pronouns and Reference 19. Questions 20. Negation 21. Tense, Aspect and Modality 22. Definiteness, Specificity and Generic Reference 23. Word Order 24. Noun Clauses 25. Relative Clauses 26. Adverbial Clauses 27. Conditional Sentences 28. Conjunctions, Co-ordination and Discourse Connection. Appendix 1. Reduplicated Stems. Appendix 2. Tense/Aspect/Modality Suffixes. Glossary. Bibliography. Index
485 citations
TL;DR: The phrase "welfare state" is of recent origin this paper and was first used to describe Labour Britain after 1945 and it was freely employed, usually but not exclusively by politicians and journalists, in relation to diverse societies at diverse stages of development.
Abstract: The phrase “welfare state” is of recent origin. It was first used to describe Labour Britain after 1945. From Britain the phrase made its way round the world. It was freely employed, usually but not exclusively by politicians and journalists, in relation to diverse societies at diverse stages of development. Historians also took over the phrase. Attempts were made to re-write nineteenth and twentieth century history, particularly British history, in terms of the “origins” and “development” of a “welfare state”.
470 citations
TL;DR: This work uses event–related brain potentials (ERP) to demonstrate that intonational phrasing guides the initial analysis of sentence structure and finds a positive shift in the ERP at intonation phrase boundaries, suggesting a specific on–line brain response to prosodic processing.
Abstract: Spoken language, in contrast to written text, provides prosodic information such as rhythm, pauses, accents, amplitude and pitch variations. However, little is known about when and how these features are used by the listener to interpret the speech signal. Here we use event-related brain potentials (ERP) to demonstrate that intonational phrasing guides the initial analysis of sentence structure. Our finding of a positive shift in the ERP at intonational phrase boundaries suggests a specific on-line brain response to prosodic processing. Additional ERP components indicate that a false prosodic boundary is sufficient to mislead the listener’s sentence processor. Thus, the application of ERP measures is a promising approach for revealing the time course and neural basis of prosodic information processing.
462 citations
20 Apr 2018
TL;DR: The authors proposed two model variants, a neural and a phrase-based model, which leverage a careful initialization of the parameters, the denoising effect of language models and automatic generation of parallel data by iterative back-translation.
Abstract: Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs. This work investigates how to learn to translate when having access to only large monolingual corpora in each language. We propose two model variants, a neural and a phrase-based model. Both versions leverage a careful initialization of the parameters, the denoising effect of language models and automatic generation of parallel data by iterative back-translation. These models are significantly better than methods from the literature, while being simpler and having fewer hyper-parameters. On the widely used WMT’14 English-French and WMT’16 German-English benchmarks, our models respectively obtain 28.1 and 25.2 BLEU points without using a single parallel sentence, outperforming the state of the art by more than 11 BLEU points. On low-resource languages like English-Urdu and English-Romanian, our methods achieve even better results than semi-supervised and supervised approaches leveraging the paucity of available bitexts. Our code for NMT and PBSMT is publicly available.
461 citations