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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Patent•
19 Mar 1971TL;DR: In this paper, a system was described for converting printed text into speech sounds by converting text into alpha-numeric signal data, for example, by a scanner and dictionary lookup, which is then analyzed to determine the proper phrase category, e.g., subject, verb, object, etc., of word intervals, and assign pause, stress, duration, pitch and intensity values to the words.
Abstract: A system is disclosed for converting printed text into speech sounds. Text is converted to alpha-numeric signal data, for example, by a scanner and dictionary lookup. Syntax of the input information is then analyzed to determine the proper phrase category, e.g., subject, verb, object, etc., of word intervals, and to assign pause, stress, duration, pitch and intensity values to the words. From these data a phonetic description of each word is found in a stored dictionary, modified by the accumulated data, and used to prepare synthesizer control signals.
265 citations
Patent•
03 Sep 2013TL;DR: In this paper, a system for guiding a search for information is presented, which comprises a user interface that accepts a phrase and receives at least one suggestion based at least in part on the phrase.
Abstract: A system for guiding a search for information is presented. The system comprises a user interface that accepts a phrase and receives at least one suggestion based at least in part on the phrase. The system also includes a phrase suggestion engine that matches the phrase with the at least one suggestion. Methods of using the system are also provided.
265 citations
TL;DR: This article showed that syntactic priming is a form of implicit learning and that lexically-based, short-term mechanisms operate in tandem with abstract, longer-term learning mechanisms can explain the full pattern of results.
264 citations
17 Jul 2006
TL;DR: A novel reordering model for phrase-based statistical machine translation (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs) that obtains significant improvements in BLEU score on the NIST MT-05 and IWSLT-04 tasks.
Abstract: We propose a novel reordering model for phrase-based statistical machine translation (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). The model provides content-dependent, hierarchical phrasal reordering with generalization based on features automatically learned from a real-world bitext. We present an algorithm to extract all reordering events of neighbor blocks from bilingual data. In our experiments on Chinese-to-English translation, this MaxEnt-based reordering model obtains significant improvements in BLEU score on the NIST MT-05 and IWSLT-04 tasks.
264 citations
TL;DR: This paper found that infants turn their heads for isolated bisyllabic words when presented with sentences that either contained the familiarized words or contained both their syllables separated by a phonological phrase boundary.
264 citations